From c313435fa60fe27199b227233e76cd15ce7f1d41 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Mon, 13 Nov 2023 19:34:29 -0500
Subject: [PATCH 01/30] fix processing multiple queries in pos/neg
 sameby/diffby

---
 src/copairs/map.py | 38 ++++++++++++++++++++++++++++----------
 1 file changed, 28 insertions(+), 10 deletions(-)

diff --git a/src/copairs/map.py b/src/copairs/map.py
index fc69403..ca199f2 100644
--- a/src/copairs/map.py
+++ b/src/copairs/map.py
@@ -12,26 +12,42 @@
 logger = logging.getLogger('copairs')
 
 
-def evaluate_and_filter(df, columns) -> list:
-    '''Evaluate the query and filter the dataframe'''
+def extract_filters(columns, df_columns) -> list:
+    '''Extract and validate filters from columns'''
     parsed_cols = []
+    queries_to_eval = []
+
     for col in columns:
-        if col in df.columns:
+        if col in df_columns:
             parsed_cols.append(col)
             continue
-
         column_names = re.findall(r'(\w+)\s*[=<>!]+', col)
-        valid_column_names = [col for col in column_names if col in df.columns]
+
+        valid_column_names = [col for col in column_names if col in df_columns]
         if not valid_column_names:
             raise ValueError(f"Invalid query or column name: {col}")
+        
+        queries_to_eval.append(col)
+        parsed_cols.extend(valid_column_names)
+
+        if len(parsed_cols) != len(set(parsed_cols)):
+            raise ValueError(f"Duplicate queries for column: {col}")
+
+    return queries_to_eval, parsed_cols
+
 
+def apply_filters(df, query_list):
+    '''Apply filters to dataframe'''
+    for query in query_list:
         try:
-            df = df.query(col)
-            parsed_cols.extend(valid_column_names)
+            df = df.query(query)
         except:
-            raise ValueError(f"Invalid query expression: {col}")
+            raise ValueError(f"Invalid query expression: {query}")
 
-    return df, parsed_cols
+        if df.empty:
+            raise ValueError(f"Empty dataframe after processing query: {query}")
+    
+    return df
 
 
 def flatten_str_list(*args):
@@ -55,7 +71,9 @@ def create_matcher(obs: pd.DataFrame,
                    neg_diffby,
                    multilabel_col=None):
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
-    obs, columns = evaluate_and_filter(obs, columns)
+    query_list, columns = extract_filters(columns, obs.columns)
+    obs = apply_filters(obs, query_list)
+    
     if multilabel_col:
         return MatcherMultilabel(obs, columns, multilabel_col, seed=0)
     return Matcher(obs, columns, seed=0)

From 5d9b2b3f7a802d90c28aa69d61faf20c27b88793 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Mon, 13 Nov 2023 22:55:59 -0500
Subject: [PATCH 02/30] merge df filter queries before applying

---
 src/copairs/map.py | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/src/copairs/map.py b/src/copairs/map.py
index ca199f2..7c48d3e 100644
--- a/src/copairs/map.py
+++ b/src/copairs/map.py
@@ -37,17 +37,20 @@ def extract_filters(columns, df_columns) -> list:
 
 
 def apply_filters(df, query_list):
-    '''Apply filters to dataframe'''
-    for query in query_list:
-        try:
-            df = df.query(query)
-        except:
-            raise ValueError(f"Invalid query expression: {query}")
-
-        if df.empty:
-            raise ValueError(f"Empty dataframe after processing query: {query}")
-    
-    return df
+    '''Combine and apply filters to dataframe'''
+    if not query_list:
+        return df
+
+    combined_query = " & ".join(f"({query})" for query in query_list)
+    try:
+        df_filtered = df.query(combined_query)
+    except Exception as e:
+        raise ValueError(f"Invalid combined query expression: {combined_query}. Error: {e}")
+
+    if df_filtered.empty:
+        raise ValueError(f"Empty dataframe after processing combined query: {combined_query}")
+
+    return df_filtered
 
 
 def flatten_str_list(*args):

From 87be587d8fa3ba8b1c1c48942c5b7402e9a88a3e Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Tue, 21 Nov 2023 00:25:07 -0500
Subject: [PATCH 03/30] fix pval: use proportion of null above the _last_ entry
 of the statistic value closes #49

---
 src/copairs/compute.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index 41512d0..55221e3 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -151,7 +151,7 @@ def compute_p_values(ap_scores, null_confs, null_size: int, seed):
     p_values = np.empty(len(ap_scores), dtype=np.float32)
     for i, (ap_score, ix) in enumerate(zip(ap_scores, rev_ix)):
         # Reverse to get from hi to low
-        num = null_size - np.searchsorted(null_dists[ix], ap_score)
+        num = null_size - np.searchsorted(null_dists[ix], ap_score, side='right')
         p_values[i] = (num + 1) / (null_size + 1)
     return p_values
 

From be2e3435c6d797fba16e2760c869569b71755a5f Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 22 Nov 2023 14:35:06 -0500
Subject: [PATCH 04/30] pval: revert default to finding first accurentce of
 statistic value in the null

---
 src/copairs/compute.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index 55221e3..cff271f 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -151,7 +151,7 @@ def compute_p_values(ap_scores, null_confs, null_size: int, seed):
     p_values = np.empty(len(ap_scores), dtype=np.float32)
     for i, (ap_score, ix) in enumerate(zip(ap_scores, rev_ix)):
         # Reverse to get from hi to low
-        num = null_size - np.searchsorted(null_dists[ix], ap_score, side='right')
+        num = null_size - np.searchsosrted(null_dists[ix], ap_score, side='left')
         p_values[i] = (num + 1) / (null_size + 1)
     return p_values
 

From 900c807f3b440abdc57de4a4b0d11c7ac6c65126 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 22 Nov 2023 14:39:11 -0500
Subject: [PATCH 05/30] fix typo in computing p vals

---
 src/copairs/compute.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index cff271f..5062f8c 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -151,7 +151,7 @@ def compute_p_values(ap_scores, null_confs, null_size: int, seed):
     p_values = np.empty(len(ap_scores), dtype=np.float32)
     for i, (ap_score, ix) in enumerate(zip(ap_scores, rev_ix)):
         # Reverse to get from hi to low
-        num = null_size - np.searchsosrted(null_dists[ix], ap_score, side='left')
+        num = null_size - np.searchsorted(null_dists[ix], ap_score, side='left')
         p_values[i] = (num + 1) / (null_size + 1)
     return p_values
 

From 3955665d7e1a0446752ed86f17d233b866046b8c Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Tue, 13 Feb 2024 17:44:46 -0500
Subject: [PATCH 06/30] feat: add pairwise euclidean distance

---
 src/copairs/compute.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index 5062f8c..7243ba8 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -64,6 +64,12 @@ def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     return c_dist
 
 
+@batch_processing
+def pairwise_euclidean(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    e_dist = np.sqrt(np.sum((x_sample - y_sample) ** 2, axis=1))
+    return 1 - e_dist
+
+
 def random_binary_matrix(n, m, k, rng):
     """Generate a random binary matrix of n*m with exactly k values in 1 per row.
     Args:

From 45239f76a47cb16c23f9f040bec144d220200acf Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Tue, 13 Feb 2024 17:46:15 -0500
Subject: [PATCH 07/30] fix: return ids for rank lists needed for multilabel
 mode

---
 src/copairs/map.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/copairs/map.py b/src/copairs/map.py
index 7c48d3e..45a01d7 100644
--- a/src/copairs/map.py
+++ b/src/copairs/map.py
@@ -101,7 +101,7 @@ def aggregate(result: pd.DataFrame, sameby, threshold: float) -> pd.DataFrame:
     return agg_rs
 
 
-def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists):
+def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists, return_unique=False):
     labels = np.concatenate([
         np.ones(pos_pairs.size, dtype=np.int32),
         np.zeros(neg_pairs.size, dtype=np.int32)
@@ -112,7 +112,9 @@ def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists):
          np.repeat(neg_dists, 2)])
     ix_sort = np.lexsort([1 - dist_all, ix])
     rel_k_list = labels[ix_sort]
-    _, counts = np.unique(ix, return_counts=True)
+    unique_ids, counts = np.unique(ix, return_counts=True)
+    if return_unique:
+        return rel_k_list, counts, unique_ids
     return rel_k_list, counts
 
 

From 1f6e4f052445882e518bb76b2c2bc665e0758460 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Mon, 13 Nov 2023 19:34:29 -0500
Subject: [PATCH 08/30] fix processing multiple queries in pos/neg
 sameby/diffby

---
 src/copairs/map.py | 309 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 309 insertions(+)
 create mode 100644 src/copairs/map.py

diff --git a/src/copairs/map.py b/src/copairs/map.py
new file mode 100644
index 0000000..ca199f2
--- /dev/null
+++ b/src/copairs/map.py
@@ -0,0 +1,309 @@
+import itertools
+import logging
+import re
+
+import numpy as np
+import pandas as pd
+from statsmodels.stats.multitest import multipletests
+
+from copairs import compute
+from copairs.matching import Matcher, MatcherMultilabel
+
+logger = logging.getLogger('copairs')
+
+
+def extract_filters(columns, df_columns) -> list:
+    '''Extract and validate filters from columns'''
+    parsed_cols = []
+    queries_to_eval = []
+
+    for col in columns:
+        if col in df_columns:
+            parsed_cols.append(col)
+            continue
+        column_names = re.findall(r'(\w+)\s*[=<>!]+', col)
+
+        valid_column_names = [col for col in column_names if col in df_columns]
+        if not valid_column_names:
+            raise ValueError(f"Invalid query or column name: {col}")
+        
+        queries_to_eval.append(col)
+        parsed_cols.extend(valid_column_names)
+
+        if len(parsed_cols) != len(set(parsed_cols)):
+            raise ValueError(f"Duplicate queries for column: {col}")
+
+    return queries_to_eval, parsed_cols
+
+
+def apply_filters(df, query_list):
+    '''Apply filters to dataframe'''
+    for query in query_list:
+        try:
+            df = df.query(query)
+        except:
+            raise ValueError(f"Invalid query expression: {query}")
+
+        if df.empty:
+            raise ValueError(f"Empty dataframe after processing query: {query}")
+    
+    return df
+
+
+def flatten_str_list(*args):
+    '''create a single list with all the params given'''
+    columns = set()
+    for col in args:
+        if isinstance(col, str):
+            columns.add(col)
+        elif isinstance(col, dict):
+            columns.update(itertools.chain.from_iterable(col.values()))
+        else:
+            columns.update(col)
+    columns = list(columns)
+    return columns
+
+
+def create_matcher(obs: pd.DataFrame,
+                   pos_sameby,
+                   pos_diffby,
+                   neg_sameby,
+                   neg_diffby,
+                   multilabel_col=None):
+    columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    query_list, columns = extract_filters(columns, obs.columns)
+    obs = apply_filters(obs, query_list)
+    
+    if multilabel_col:
+        return MatcherMultilabel(obs, columns, multilabel_col, seed=0)
+    return Matcher(obs, columns, seed=0)
+
+
+def aggregate(result: pd.DataFrame, sameby, threshold: float) -> pd.DataFrame:
+    agg_rs = result.groupby(sameby, as_index=False, observed=True).agg({
+        'average_precision':
+        'mean',
+        'p_value':
+        lambda p_values: -np.log10(p_values).mean(),
+    })
+    reject, pvals_corrected, alphacSidak, alphacBonf = multipletests(
+        10**-agg_rs['p_value'], method='fdr_bh')
+    agg_rs['q_value'] = pvals_corrected
+    agg_rs['nlog10qvalue'] = (-np.log10(agg_rs['q_value']))
+    agg_rs.rename({'p_value': 'nlog10pvalue'}, axis=1, inplace=True)
+    agg_rs['above_p_threshold'] = agg_rs['nlog10pvalue'] > -np.log10(threshold)
+    agg_rs['above_q_threshold'] = agg_rs['nlog10qvalue'] > -np.log10(threshold)
+    agg_rs.rename(columns={'average_precision': 'mean_average_precision'},
+                  inplace=True)
+    return agg_rs
+
+
+def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists):
+    labels = np.concatenate([
+        np.ones(pos_pairs.size, dtype=np.int32),
+        np.zeros(neg_pairs.size, dtype=np.int32)
+    ])
+    ix = np.concatenate([pos_pairs.ravel(), neg_pairs.ravel()])
+    dist_all = np.concatenate(
+        [np.repeat(pos_dists, 2),
+         np.repeat(neg_dists, 2)])
+    ix_sort = np.lexsort([1 - dist_all, ix])
+    rel_k_list = labels[ix_sort]
+    _, counts = np.unique(ix, return_counts=True)
+    return rel_k_list, counts
+
+
+def validate_pipeline_input(meta, feats, columns):
+    if meta[columns].isna().any(axis=None):
+        raise ValueError('metadata columns should not have null values.')
+    if len(meta) != len(feats):
+        raise ValueError('meta and feats have different number of rows')
+
+
+def run_pipeline(meta,
+                 feats,
+                 pos_sameby,
+                 pos_diffby,
+                 neg_sameby,
+                 neg_diffby,
+                 null_size,
+                 batch_size=20000,
+                 seed=0) -> pd.DataFrame:
+    columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    validate_pipeline_input(meta, feats, columns)
+
+    # Critical!, otherwise the indexing wont work
+    meta = meta.reset_index(drop=True).copy()
+    logger.info('Indexing metadata...')
+    matcher = create_matcher(meta, pos_sameby, pos_diffby, neg_sameby,
+                             neg_diffby)
+
+    logger.info('Finding positive pairs...')
+    pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
+    pos_total = sum(len(p) for p in pos_pairs.values())
+    pos_pairs = np.fromiter(itertools.chain.from_iterable(pos_pairs.values()),
+                            dtype=np.dtype((np.int32, 2)),
+                            count=pos_total)
+
+    logger.info('Finding negative pairs...')
+    neg_pairs = matcher.get_all_pairs(sameby=neg_sameby, diffby=neg_diffby)
+    total_neg = sum(len(p) for p in neg_pairs.values())
+    neg_pairs = np.fromiter(itertools.chain.from_iterable(neg_pairs.values()),
+                            dtype=np.dtype((np.int32, 2)),
+                            count=total_neg)
+
+    logger.info('Computing positive similarities...')
+    pos_dists = compute.pairwise_cosine(feats, pos_pairs, batch_size)
+
+    logger.info('Computing negative similarities...')
+    neg_dists = compute.pairwise_cosine(feats, neg_pairs, batch_size)
+
+    logger.info('Building rank lists...')
+    rel_k_list, counts = build_rank_lists(pos_pairs, neg_pairs, pos_dists,
+                                          neg_dists)
+
+    logger.info('Computing average precision...')
+    ap_scores, null_confs = compute.compute_ap_contiguous(rel_k_list, counts)
+
+    logger.info('Computing p-values...')
+    p_values = compute.compute_p_values(ap_scores,
+                                        null_confs,
+                                        null_size,
+                                        seed=seed)
+
+    logger.info('Creating result DataFrame...')
+    meta['average_precision'] = ap_scores
+    meta['p_value'] = p_values
+    meta["n_pos_pairs"] = null_confs[:, 0]
+    meta["n_total_pairs"] = null_confs[:, 1]
+    logger.info('Finished.')
+    return meta
+
+
+def create_neg_query_solver(neg_pairs, neg_dists):
+    # Melting and sorting by ix. neg_cutoffs splits the contiguous array
+    neg_ix = neg_pairs.ravel()
+    neg_dists = np.repeat(neg_dists, 2)
+
+    sort_ix = np.argsort(neg_ix)
+    neg_dists = neg_dists[sort_ix]
+
+    neg_ix, neg_counts = np.unique(neg_ix, return_counts=True)
+    neg_cutoffs = compute.to_cutoffs(neg_counts)
+
+    def negs_for(query: np.ndarray):
+        locs = np.searchsorted(neg_ix, query)
+        sizes = neg_counts[locs]
+        start = neg_cutoffs[locs]
+        end = start + sizes
+        slices = compute.concat_ranges(start, end)
+        batch_dists = neg_dists[slices]
+        return batch_dists, sizes
+
+    return negs_for
+
+
+def build_rank_lists_multi(pos_pairs, pos_dists, pos_counts, negs_for,
+                           null_size, seed):
+    ap_scores_list, p_values_list, ix_list = [], [], []
+
+    start = 0
+    for end in pos_counts.cumsum():
+        mpos_pairs = pos_pairs[start:end]
+        mpos_dists = pos_dists[start:end]
+        start = end
+        query = np.unique(mpos_pairs)
+        neg_dists, neg_counts = negs_for(query)
+        neg_ix = np.repeat(query, neg_counts)
+        labels = np.concatenate([
+            np.ones(mpos_pairs.size, dtype=np.int32),
+            np.zeros(len(neg_dists), dtype=np.int32)
+        ])
+
+        ix = np.concatenate([mpos_pairs.ravel(), neg_ix])
+        dist_all = np.concatenate([np.repeat(mpos_dists, 2), neg_dists])
+        ix_sort = np.lexsort([1 - dist_all, ix])
+        rel_k_list = labels[ix_sort]
+        _, counts = np.unique(ix, return_counts=True)
+        ap_scores, null_confs = compute.compute_ap_contiguous(
+            rel_k_list, counts)
+        p_values = compute.compute_p_values(ap_scores,
+                                            null_confs,
+                                            null_size,
+                                            seed=seed)
+
+        ap_scores_list.append(ap_scores)
+        p_values_list.append(p_values)
+        ix_list.append(query)
+    return ap_scores_list, p_values_list, ix_list
+
+
+def run_pipeline_multilabel(meta,
+                            feats,
+                            pos_sameby,
+                            pos_diffby,
+                            neg_sameby,
+                            neg_diffby,
+                            null_size,
+                            multilabel_col,
+                            batch_size=20000,
+                            seed=0) -> pd.DataFrame:
+    columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    validate_pipeline_input(meta, feats, columns)
+    # Critical!, otherwise the indexing wont work
+    meta = meta.reset_index(drop=True).copy()
+
+    logger.info('Indexing metadata...')
+    matcher = create_matcher(meta, pos_sameby, pos_diffby, neg_sameby,
+                             neg_diffby, multilabel_col)
+    logger.info('Finding positive pairs...')
+    pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
+    pos_keys = pos_pairs.keys()
+    pos_counts = np.fromiter(map(len, pos_pairs.values()), dtype=np.int32)
+    pos_total = sum(pos_counts)
+    pos_pairs = np.fromiter(itertools.chain.from_iterable(pos_pairs.values()),
+                            dtype=np.dtype((np.int32, 2)),
+                            count=pos_total)
+
+    logger.info('Finding negative pairs...')
+    neg_pairs = matcher.get_all_pairs(sameby=neg_sameby, diffby=neg_diffby)
+    total_neg = sum(len(p) for p in neg_pairs.values())
+    neg_pairs = np.fromiter(itertools.chain.from_iterable(neg_pairs.values()),
+                            dtype=np.dtype((np.int32, 2)),
+                            count=total_neg)
+
+    logger.info('Dropping dups in negative pairs...')
+    neg_pairs = np.unique(neg_pairs, axis=0)
+
+    logger.info('Computing positive similarities...')
+    pos_dists = compute.pairwise_cosine(feats, pos_pairs, batch_size)
+
+    logger.info('Computing negative similarities...')
+    neg_dists = compute.pairwise_cosine(feats, neg_pairs, batch_size)
+
+    logger.info('Computing mAP and p-values per label...')
+    negs_for = create_neg_query_solver(neg_pairs, neg_dists)
+    ap_scores_list, p_values_list, ix_list = build_rank_lists_multi(
+        pos_pairs, pos_dists, pos_counts, negs_for, null_size, seed)
+
+    logger.info('Creating result DataFrame...')
+    results = []
+    for i, key in enumerate(pos_keys):
+        result = pd.DataFrame({
+            'average_precision': ap_scores_list[i],
+            'p_value': p_values_list[i],
+            'ix': ix_list[i],
+        })
+        if isinstance(key, tuple):
+            # Is a ComposedKey
+            for k, v in zip(key._fields, key):
+                result[k] = v
+        else:
+            result[multilabel_col] = key
+        results.append(result)
+    results = pd.concat(results).reset_index(drop=True)
+    meta = meta.drop(multilabel_col, axis=1)
+    results = meta.merge(results, right_on='ix', left_index=True).drop('ix',
+                                                                       axis=1)
+    logger.info('Finished.')
+    return results

From 9bbc8728f52cb95ee113335893e7829c49ca1658 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Mon, 13 Nov 2023 22:55:59 -0500
Subject: [PATCH 09/30] merge df filter queries before applying

---
 src/copairs/map.py | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/src/copairs/map.py b/src/copairs/map.py
index ca199f2..7c48d3e 100644
--- a/src/copairs/map.py
+++ b/src/copairs/map.py
@@ -37,17 +37,20 @@ def extract_filters(columns, df_columns) -> list:
 
 
 def apply_filters(df, query_list):
-    '''Apply filters to dataframe'''
-    for query in query_list:
-        try:
-            df = df.query(query)
-        except:
-            raise ValueError(f"Invalid query expression: {query}")
-
-        if df.empty:
-            raise ValueError(f"Empty dataframe after processing query: {query}")
-    
-    return df
+    '''Combine and apply filters to dataframe'''
+    if not query_list:
+        return df
+
+    combined_query = " & ".join(f"({query})" for query in query_list)
+    try:
+        df_filtered = df.query(combined_query)
+    except Exception as e:
+        raise ValueError(f"Invalid combined query expression: {combined_query}. Error: {e}")
+
+    if df_filtered.empty:
+        raise ValueError(f"Empty dataframe after processing combined query: {combined_query}")
+
+    return df_filtered
 
 
 def flatten_str_list(*args):

From e8031fc0c237d60f15e0d30575be7a7612c51af8 Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Tue, 13 Feb 2024 17:44:46 -0500
Subject: [PATCH 10/30] feat: add pairwise euclidean distance

---
 src/copairs/compute.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index da78ec6..04996ca 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -70,6 +70,12 @@ def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     return c_sim
 
 
+@batch_processing
+def pairwise_euclidean(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    e_dist = np.sqrt(np.sum((x_sample - y_sample) ** 2, axis=1))
+    return 1 - e_dist
+
+
 def random_binary_matrix(n, m, k, rng):
     """Generate a random binary matrix of n*m with exactly k values in 1 per row.
     Args:

From b518234742f2b2656c4144fb969cba523a3271fc Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Tue, 13 Feb 2024 17:46:15 -0500
Subject: [PATCH 11/30] fix: return ids for rank lists needed for multilabel
 mode

---
 src/copairs/map.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/copairs/map.py b/src/copairs/map.py
index 7c48d3e..45a01d7 100644
--- a/src/copairs/map.py
+++ b/src/copairs/map.py
@@ -101,7 +101,7 @@ def aggregate(result: pd.DataFrame, sameby, threshold: float) -> pd.DataFrame:
     return agg_rs
 
 
-def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists):
+def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists, return_unique=False):
     labels = np.concatenate([
         np.ones(pos_pairs.size, dtype=np.int32),
         np.zeros(neg_pairs.size, dtype=np.int32)
@@ -112,7 +112,9 @@ def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists):
          np.repeat(neg_dists, 2)])
     ix_sort = np.lexsort([1 - dist_all, ix])
     rel_k_list = labels[ix_sort]
-    _, counts = np.unique(ix, return_counts=True)
+    unique_ids, counts = np.unique(ix, return_counts=True)
+    if return_unique:
+        return rel_k_list, counts, unique_ids
     return rel_k_list, counts
 
 

From a0dc0e16447d374cf41d7b649db60ce87c0b2572 Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Fri, 23 Feb 2024 16:54:03 -0500
Subject: [PATCH 12/30] merge v0.4.0

---
 src/copairs/compute.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index c7af8b3..04996ca 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -76,12 +76,6 @@ def pairwise_euclidean(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray
     return 1 - e_dist
 
 
-@batch_processing
-def pairwise_euclidean(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
-    e_dist = np.sqrt(np.sum((x_sample - y_sample) ** 2, axis=1))
-    return 1 - e_dist
-
-
 def random_binary_matrix(n, m, k, rng):
     """Generate a random binary matrix of n*m with exactly k values in 1 per row.
     Args:

From d5ad0a94c3b5cc60576622a72d4cb07672a581fe Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Fri, 17 May 2024 18:07:38 -0400
Subject: [PATCH 13/30] refactor processing multiple queries for filtering the
 df see alxndrkalinin/copairs@5d9b2b3f7a802d90c28aa69d61faf20c27b88793

---
 src/copairs/map.py                   | 314 ---------------------------
 src/copairs/map/average_precision.py |   3 +-
 src/copairs/map/filter.py            |  48 +++-
 src/copairs/map/multilabel.py        |   3 +-
 4 files changed, 40 insertions(+), 328 deletions(-)
 delete mode 100644 src/copairs/map.py

diff --git a/src/copairs/map.py b/src/copairs/map.py
deleted file mode 100644
index 45a01d7..0000000
--- a/src/copairs/map.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import itertools
-import logging
-import re
-
-import numpy as np
-import pandas as pd
-from statsmodels.stats.multitest import multipletests
-
-from copairs import compute
-from copairs.matching import Matcher, MatcherMultilabel
-
-logger = logging.getLogger('copairs')
-
-
-def extract_filters(columns, df_columns) -> list:
-    '''Extract and validate filters from columns'''
-    parsed_cols = []
-    queries_to_eval = []
-
-    for col in columns:
-        if col in df_columns:
-            parsed_cols.append(col)
-            continue
-        column_names = re.findall(r'(\w+)\s*[=<>!]+', col)
-
-        valid_column_names = [col for col in column_names if col in df_columns]
-        if not valid_column_names:
-            raise ValueError(f"Invalid query or column name: {col}")
-        
-        queries_to_eval.append(col)
-        parsed_cols.extend(valid_column_names)
-
-        if len(parsed_cols) != len(set(parsed_cols)):
-            raise ValueError(f"Duplicate queries for column: {col}")
-
-    return queries_to_eval, parsed_cols
-
-
-def apply_filters(df, query_list):
-    '''Combine and apply filters to dataframe'''
-    if not query_list:
-        return df
-
-    combined_query = " & ".join(f"({query})" for query in query_list)
-    try:
-        df_filtered = df.query(combined_query)
-    except Exception as e:
-        raise ValueError(f"Invalid combined query expression: {combined_query}. Error: {e}")
-
-    if df_filtered.empty:
-        raise ValueError(f"Empty dataframe after processing combined query: {combined_query}")
-
-    return df_filtered
-
-
-def flatten_str_list(*args):
-    '''create a single list with all the params given'''
-    columns = set()
-    for col in args:
-        if isinstance(col, str):
-            columns.add(col)
-        elif isinstance(col, dict):
-            columns.update(itertools.chain.from_iterable(col.values()))
-        else:
-            columns.update(col)
-    columns = list(columns)
-    return columns
-
-
-def create_matcher(obs: pd.DataFrame,
-                   pos_sameby,
-                   pos_diffby,
-                   neg_sameby,
-                   neg_diffby,
-                   multilabel_col=None):
-    columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
-    query_list, columns = extract_filters(columns, obs.columns)
-    obs = apply_filters(obs, query_list)
-    
-    if multilabel_col:
-        return MatcherMultilabel(obs, columns, multilabel_col, seed=0)
-    return Matcher(obs, columns, seed=0)
-
-
-def aggregate(result: pd.DataFrame, sameby, threshold: float) -> pd.DataFrame:
-    agg_rs = result.groupby(sameby, as_index=False, observed=True).agg({
-        'average_precision':
-        'mean',
-        'p_value':
-        lambda p_values: -np.log10(p_values).mean(),
-    })
-    reject, pvals_corrected, alphacSidak, alphacBonf = multipletests(
-        10**-agg_rs['p_value'], method='fdr_bh')
-    agg_rs['q_value'] = pvals_corrected
-    agg_rs['nlog10qvalue'] = (-np.log10(agg_rs['q_value']))
-    agg_rs.rename({'p_value': 'nlog10pvalue'}, axis=1, inplace=True)
-    agg_rs['above_p_threshold'] = agg_rs['nlog10pvalue'] > -np.log10(threshold)
-    agg_rs['above_q_threshold'] = agg_rs['nlog10qvalue'] > -np.log10(threshold)
-    agg_rs.rename(columns={'average_precision': 'mean_average_precision'},
-                  inplace=True)
-    return agg_rs
-
-
-def build_rank_lists(pos_pairs, neg_pairs, pos_dists, neg_dists, return_unique=False):
-    labels = np.concatenate([
-        np.ones(pos_pairs.size, dtype=np.int32),
-        np.zeros(neg_pairs.size, dtype=np.int32)
-    ])
-    ix = np.concatenate([pos_pairs.ravel(), neg_pairs.ravel()])
-    dist_all = np.concatenate(
-        [np.repeat(pos_dists, 2),
-         np.repeat(neg_dists, 2)])
-    ix_sort = np.lexsort([1 - dist_all, ix])
-    rel_k_list = labels[ix_sort]
-    unique_ids, counts = np.unique(ix, return_counts=True)
-    if return_unique:
-        return rel_k_list, counts, unique_ids
-    return rel_k_list, counts
-
-
-def validate_pipeline_input(meta, feats, columns):
-    if meta[columns].isna().any(axis=None):
-        raise ValueError('metadata columns should not have null values.')
-    if len(meta) != len(feats):
-        raise ValueError('meta and feats have different number of rows')
-
-
-def run_pipeline(meta,
-                 feats,
-                 pos_sameby,
-                 pos_diffby,
-                 neg_sameby,
-                 neg_diffby,
-                 null_size,
-                 batch_size=20000,
-                 seed=0) -> pd.DataFrame:
-    columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
-    validate_pipeline_input(meta, feats, columns)
-
-    # Critical!, otherwise the indexing wont work
-    meta = meta.reset_index(drop=True).copy()
-    logger.info('Indexing metadata...')
-    matcher = create_matcher(meta, pos_sameby, pos_diffby, neg_sameby,
-                             neg_diffby)
-
-    logger.info('Finding positive pairs...')
-    pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
-    pos_total = sum(len(p) for p in pos_pairs.values())
-    pos_pairs = np.fromiter(itertools.chain.from_iterable(pos_pairs.values()),
-                            dtype=np.dtype((np.int32, 2)),
-                            count=pos_total)
-
-    logger.info('Finding negative pairs...')
-    neg_pairs = matcher.get_all_pairs(sameby=neg_sameby, diffby=neg_diffby)
-    total_neg = sum(len(p) for p in neg_pairs.values())
-    neg_pairs = np.fromiter(itertools.chain.from_iterable(neg_pairs.values()),
-                            dtype=np.dtype((np.int32, 2)),
-                            count=total_neg)
-
-    logger.info('Computing positive similarities...')
-    pos_dists = compute.pairwise_cosine(feats, pos_pairs, batch_size)
-
-    logger.info('Computing negative similarities...')
-    neg_dists = compute.pairwise_cosine(feats, neg_pairs, batch_size)
-
-    logger.info('Building rank lists...')
-    rel_k_list, counts = build_rank_lists(pos_pairs, neg_pairs, pos_dists,
-                                          neg_dists)
-
-    logger.info('Computing average precision...')
-    ap_scores, null_confs = compute.compute_ap_contiguous(rel_k_list, counts)
-
-    logger.info('Computing p-values...')
-    p_values = compute.compute_p_values(ap_scores,
-                                        null_confs,
-                                        null_size,
-                                        seed=seed)
-
-    logger.info('Creating result DataFrame...')
-    meta['average_precision'] = ap_scores
-    meta['p_value'] = p_values
-    meta["n_pos_pairs"] = null_confs[:, 0]
-    meta["n_total_pairs"] = null_confs[:, 1]
-    logger.info('Finished.')
-    return meta
-
-
-def create_neg_query_solver(neg_pairs, neg_dists):
-    # Melting and sorting by ix. neg_cutoffs splits the contiguous array
-    neg_ix = neg_pairs.ravel()
-    neg_dists = np.repeat(neg_dists, 2)
-
-    sort_ix = np.argsort(neg_ix)
-    neg_dists = neg_dists[sort_ix]
-
-    neg_ix, neg_counts = np.unique(neg_ix, return_counts=True)
-    neg_cutoffs = compute.to_cutoffs(neg_counts)
-
-    def negs_for(query: np.ndarray):
-        locs = np.searchsorted(neg_ix, query)
-        sizes = neg_counts[locs]
-        start = neg_cutoffs[locs]
-        end = start + sizes
-        slices = compute.concat_ranges(start, end)
-        batch_dists = neg_dists[slices]
-        return batch_dists, sizes
-
-    return negs_for
-
-
-def build_rank_lists_multi(pos_pairs, pos_dists, pos_counts, negs_for,
-                           null_size, seed):
-    ap_scores_list, p_values_list, ix_list = [], [], []
-
-    start = 0
-    for end in pos_counts.cumsum():
-        mpos_pairs = pos_pairs[start:end]
-        mpos_dists = pos_dists[start:end]
-        start = end
-        query = np.unique(mpos_pairs)
-        neg_dists, neg_counts = negs_for(query)
-        neg_ix = np.repeat(query, neg_counts)
-        labels = np.concatenate([
-            np.ones(mpos_pairs.size, dtype=np.int32),
-            np.zeros(len(neg_dists), dtype=np.int32)
-        ])
-
-        ix = np.concatenate([mpos_pairs.ravel(), neg_ix])
-        dist_all = np.concatenate([np.repeat(mpos_dists, 2), neg_dists])
-        ix_sort = np.lexsort([1 - dist_all, ix])
-        rel_k_list = labels[ix_sort]
-        _, counts = np.unique(ix, return_counts=True)
-        ap_scores, null_confs = compute.compute_ap_contiguous(
-            rel_k_list, counts)
-        p_values = compute.compute_p_values(ap_scores,
-                                            null_confs,
-                                            null_size,
-                                            seed=seed)
-
-        ap_scores_list.append(ap_scores)
-        p_values_list.append(p_values)
-        ix_list.append(query)
-    return ap_scores_list, p_values_list, ix_list
-
-
-def run_pipeline_multilabel(meta,
-                            feats,
-                            pos_sameby,
-                            pos_diffby,
-                            neg_sameby,
-                            neg_diffby,
-                            null_size,
-                            multilabel_col,
-                            batch_size=20000,
-                            seed=0) -> pd.DataFrame:
-    columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
-    validate_pipeline_input(meta, feats, columns)
-    # Critical!, otherwise the indexing wont work
-    meta = meta.reset_index(drop=True).copy()
-
-    logger.info('Indexing metadata...')
-    matcher = create_matcher(meta, pos_sameby, pos_diffby, neg_sameby,
-                             neg_diffby, multilabel_col)
-    logger.info('Finding positive pairs...')
-    pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
-    pos_keys = pos_pairs.keys()
-    pos_counts = np.fromiter(map(len, pos_pairs.values()), dtype=np.int32)
-    pos_total = sum(pos_counts)
-    pos_pairs = np.fromiter(itertools.chain.from_iterable(pos_pairs.values()),
-                            dtype=np.dtype((np.int32, 2)),
-                            count=pos_total)
-
-    logger.info('Finding negative pairs...')
-    neg_pairs = matcher.get_all_pairs(sameby=neg_sameby, diffby=neg_diffby)
-    total_neg = sum(len(p) for p in neg_pairs.values())
-    neg_pairs = np.fromiter(itertools.chain.from_iterable(neg_pairs.values()),
-                            dtype=np.dtype((np.int32, 2)),
-                            count=total_neg)
-
-    logger.info('Dropping dups in negative pairs...')
-    neg_pairs = np.unique(neg_pairs, axis=0)
-
-    logger.info('Computing positive similarities...')
-    pos_dists = compute.pairwise_cosine(feats, pos_pairs, batch_size)
-
-    logger.info('Computing negative similarities...')
-    neg_dists = compute.pairwise_cosine(feats, neg_pairs, batch_size)
-
-    logger.info('Computing mAP and p-values per label...')
-    negs_for = create_neg_query_solver(neg_pairs, neg_dists)
-    ap_scores_list, p_values_list, ix_list = build_rank_lists_multi(
-        pos_pairs, pos_dists, pos_counts, negs_for, null_size, seed)
-
-    logger.info('Creating result DataFrame...')
-    results = []
-    for i, key in enumerate(pos_keys):
-        result = pd.DataFrame({
-            'average_precision': ap_scores_list[i],
-            'p_value': p_values_list[i],
-            'ix': ix_list[i],
-        })
-        if isinstance(key, tuple):
-            # Is a ComposedKey
-            for k, v in zip(key._fields, key):
-                result[k] = v
-        else:
-            result[multilabel_col] = key
-        results.append(result)
-    results = pd.concat(results).reset_index(drop=True)
-    meta = meta.drop(multilabel_col, axis=1)
-    results = meta.merge(results, right_on='ix', left_index=True).drop('ix',
-                                                                       axis=1)
-    logger.info('Finished.')
-    return results
diff --git a/src/copairs/map/average_precision.py b/src/copairs/map/average_precision.py
index c335bfa..fc886e8 100644
--- a/src/copairs/map/average_precision.py
+++ b/src/copairs/map/average_precision.py
@@ -31,12 +31,13 @@ def average_precision(
     meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby, batch_size=20000
 ) -> pd.DataFrame:
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    meta, columns = evaluate_and_filter(meta, columns)
     validate_pipeline_input(meta, feats, columns)
 
     # Critical!, otherwise the indexing wont work
     meta = meta.reset_index(drop=True).copy()
     logger.info("Indexing metadata...")
-    matcher = Matcher(*evaluate_and_filter(meta, columns), seed=0)
+    matcher = Matcher(meta, columns, seed=0)
 
     logger.info("Finding positive pairs...")
     pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
diff --git a/src/copairs/map/filter.py b/src/copairs/map/filter.py
index ff94328..e89579e 100644
--- a/src/copairs/map/filter.py
+++ b/src/copairs/map/filter.py
@@ -1,6 +1,5 @@
 import itertools
 import re
-from typing import List, Tuple
 
 import pandas as pd
 import numpy as np
@@ -29,23 +28,48 @@ def flatten_str_list(*args):
     return columns
 
 
-def evaluate_and_filter(df, columns) -> Tuple[pd.DataFrame, List[str]]:
-    """Evaluate the query and filter the dataframe"""
+def evaluate_and_filter(df, columns) -> tuple[pd.DataFrame, list[str]]:
+    """Evaluate queries and filter the dataframe"""
+    query_list, columns = extract_filters(columns, df.columns)
+    df = apply_filters(df, query_list)
+    return df, columns
+
+
+def extract_filters(columns, df_columns) -> tuple[list[str], list[str]]:
+    """Extract and validate filters from columns"""
     parsed_cols = []
+    queries_to_eval = []
+
     for col in columns:
-        if col in df.columns:
+        if col in df_columns:
             parsed_cols.append(col)
             continue
+        column_names = re.findall(r'(\w+)\s*[=<>!]+', col)
 
-        column_names = re.findall(r"(\w+)\s*[=<>!]+", col)
-        valid_column_names = [col for col in column_names if col in df.columns]
+        valid_column_names = [col for col in column_names if col in df_columns]
         if not valid_column_names:
             raise ValueError(f"Invalid query or column name: {col}")
+        
+        queries_to_eval.append(col)
+        parsed_cols.extend(valid_column_names)
+
+        if len(parsed_cols) != len(set(parsed_cols)):
+            raise ValueError(f"Duplicate queries for column: {col}")
+
+    return queries_to_eval, parsed_cols
+
+
+def apply_filters(df, query_list):
+    """Combine and apply filters to dataframe"""
+    if not query_list:
+        return df
 
-        try:
-            df = df.query(col)
-            parsed_cols.extend(valid_column_names)
-        except:
-            raise ValueError(f"Invalid query expression: {col}")
+    combined_query = " & ".join(f"({query})" for query in query_list)
+    try:
+        df_filtered = df.query(combined_query)
+        if df_filtered.empty:
+            raise ValueError(f"No data matched the query: {combined_query}")
+    except Exception as e:
+        raise ValueError(f"Invalid combined query expression: {combined_query}. Error: {e}")
 
-    return df, parsed_cols
+    return df_filtered
diff --git a/src/copairs/map/multilabel.py b/src/copairs/map/multilabel.py
index 2961b42..25e5b9a 100644
--- a/src/copairs/map/multilabel.py
+++ b/src/copairs/map/multilabel.py
@@ -76,13 +76,14 @@ def average_precision(
     batch_size=20000,
 ) -> pd.DataFrame:
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    meta, columns = evaluate_and_filter(meta, columns)
     validate_pipeline_input(meta, feats, columns)
     # Critical!, otherwise the indexing wont work
     meta = meta.reset_index(drop=True).copy()
 
     logger.info("Indexing metadata...")
     matcher = MatcherMultilabel(
-        *evaluate_and_filter(meta, columns), multilabel_col=multilabel_col, seed=0
+        meta, columns, multilabel_col=multilabel_col, seed=0
     )
 
     logger.info("Finding positive pairs...")

From 94f004eff662926886867b65b03d96537932dbd6 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Fri, 17 May 2024 18:08:10 -0400
Subject: [PATCH 14/30] add tests for query filtering

---
 tests/test_map_filter.py | 49 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 tests/test_map_filter.py

diff --git a/tests/test_map_filter.py b/tests/test_map_filter.py
new file mode 100644
index 0000000..59df5bc
--- /dev/null
+++ b/tests/test_map_filter.py
@@ -0,0 +1,49 @@
+import pytest
+import pandas as pd
+import numpy as np
+
+from copairs.map.filter import evaluate_and_filter
+from tests.helpers import simulate_random_dframe
+
+SEED = 0
+
+
+@pytest.fixture
+def mock_dataframe():
+    length = 10
+    vocab_size = {"p": 3, "w": 3, "l": 10}
+    pos_sameby = ["l"]
+    pos_diffby = ["p"]
+    rng = np.random.default_rng(SEED)
+    df = simulate_random_dframe(length, vocab_size, pos_sameby, pos_diffby, rng)
+    df.drop_duplicates(subset=pos_sameby, inplace=True)
+    return df
+
+
+def test_correct(mock_dataframe):
+    df, parsed_cols = evaluate_and_filter(mock_dataframe, ["p == 'p1'", "w > 'w2'"])
+    assert not df.empty
+    assert 'p' in parsed_cols and 'w' in parsed_cols
+    assert all(df['w'].str.extract(r'(\d+)')[0].astype(int) > 2)
+
+
+def test_invalid_query(mock_dataframe):
+    with pytest.raises(ValueError) as excinfo:
+        evaluate_and_filter(mock_dataframe, ['l == "lHello"'])
+    assert "Invalid combined query expression" in str(excinfo.value)
+    assert "No data matched the query" in str(excinfo.value)
+
+
+def test_empty_result(mock_dataframe):
+    with pytest.raises(ValueError) as excinfo:
+        evaluate_and_filter(mock_dataframe, ['p == "p1"', 'p == "p2"'])
+    assert "Duplicate queries for column" in str(excinfo.value)
+
+
+def test_empty_result_from_valid_query(mock_dataframe):
+    with pytest.raises(ValueError) as excinfo:
+        evaluate_and_filter(mock_dataframe, ['p == "p4"'])
+    assert "No data matched the query" in str(excinfo.value)
+
+
+

From f64449b9a7344cfed31999bb47f825b0f2285e61 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Mon, 20 May 2024 17:48:19 -0400
Subject: [PATCH 15/30] add python 3.11-12 to github actions

---
 .github/workflows/python-package.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 2c6cf4d..4597441 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3

From 51ad5604e6d92c963ae185b0f0f33b35a93d0eda Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Mon, 20 May 2024 22:44:13 -0400
Subject: [PATCH 16/30] add phenotypic activity example

---
 examples/demo.ipynb | 2276 +++++++++++++++++++++++++++++++++++++++++++
 pyproject.toml      |    1 +
 2 files changed, 2277 insertions(+)
 create mode 100644 examples/demo.ipynb

diff --git a/examples/demo.ipynb b/examples/demo.ipynb
new file mode 100644
index 0000000..0fea058
--- /dev/null
+++ b/examples/demo.ipynb
@@ -0,0 +1,2276 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from copairs import map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download data\n",
+    "\n",
+    "Download a single plate of profiles from the dataset \"cpg0004\" (aka LINCS), which contains Cell Painting images of 1,327 small-molecule perturbations of A549 human cells. The wells on each plate were perturbed with 56 different compounds in six different doses.\n",
+    "\n",
+    "> Way, G. P. et al. Morphology and gene expression profiling provide complementary information for mapping cell state. Cell Syst 13, 911–923.e9 (2022)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>Metadata_InChIKey14</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A01</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.3544</td>\n",
+       "      <td>-1.07770</td>\n",
+       "      <td>2.26020</td>\n",
+       "      <td>-0.377010</td>\n",
+       "      <td>-0.065840</td>\n",
+       "      <td>2.12360</td>\n",
+       "      <td>2.8740</td>\n",
+       "      <td>2.87500</td>\n",
+       "      <td>2.3047</td>\n",
+       "      <td>-0.92358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A02</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.3840</td>\n",
+       "      <td>-0.73440</td>\n",
+       "      <td>1.12090</td>\n",
+       "      <td>-0.182500</td>\n",
+       "      <td>-0.061450</td>\n",
+       "      <td>0.66985</td>\n",
+       "      <td>2.3919</td>\n",
+       "      <td>2.35230</td>\n",
+       "      <td>1.8672</td>\n",
+       "      <td>-0.11820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A03</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.9493</td>\n",
+       "      <td>-0.36148</td>\n",
+       "      <td>0.44050</td>\n",
+       "      <td>0.326660</td>\n",
+       "      <td>0.547200</td>\n",
+       "      <td>0.25015</td>\n",
+       "      <td>1.2271</td>\n",
+       "      <td>0.77847</td>\n",
+       "      <td>1.0651</td>\n",
+       "      <td>-0.44810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A04</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.2909</td>\n",
+       "      <td>-0.46380</td>\n",
+       "      <td>0.96434</td>\n",
+       "      <td>1.132200</td>\n",
+       "      <td>0.753500</td>\n",
+       "      <td>0.31403</td>\n",
+       "      <td>1.4384</td>\n",
+       "      <td>1.48110</td>\n",
+       "      <td>1.2943</td>\n",
+       "      <td>-0.83810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A05</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.8955</td>\n",
+       "      <td>-1.05350</td>\n",
+       "      <td>1.64840</td>\n",
+       "      <td>0.057781</td>\n",
+       "      <td>0.070229</td>\n",
+       "      <td>1.60990</td>\n",
+       "      <td>1.1296</td>\n",
+       "      <td>0.90213</td>\n",
+       "      <td>1.1016</td>\n",
+       "      <td>0.53225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>3.248700</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-6.1522</td>\n",
+       "      <td>1.81410</td>\n",
+       "      <td>1.54220</td>\n",
+       "      <td>-1.874700</td>\n",
+       "      <td>-1.133900</td>\n",
+       "      <td>1.57540</td>\n",
+       "      <td>-3.0962</td>\n",
+       "      <td>-3.25160</td>\n",
+       "      <td>-2.7683</td>\n",
+       "      <td>1.40170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>1.082900</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.1586</td>\n",
+       "      <td>1.50580</td>\n",
+       "      <td>1.68420</td>\n",
+       "      <td>-1.126400</td>\n",
+       "      <td>-1.066600</td>\n",
+       "      <td>1.24740</td>\n",
+       "      <td>-1.5305</td>\n",
+       "      <td>-1.79020</td>\n",
+       "      <td>-1.2474</td>\n",
+       "      <td>1.17600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.360970</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.9475</td>\n",
+       "      <td>1.42100</td>\n",
+       "      <td>1.51020</td>\n",
+       "      <td>-1.103600</td>\n",
+       "      <td>-1.666500</td>\n",
+       "      <td>1.19840</td>\n",
+       "      <td>-2.6086</td>\n",
+       "      <td>-2.97620</td>\n",
+       "      <td>-2.0026</td>\n",
+       "      <td>0.91557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.120320</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-8.4408</td>\n",
+       "      <td>2.99620</td>\n",
+       "      <td>2.55230</td>\n",
+       "      <td>-2.275200</td>\n",
+       "      <td>-1.783500</td>\n",
+       "      <td>2.49200</td>\n",
+       "      <td>-4.3964</td>\n",
+       "      <td>-4.19030</td>\n",
+       "      <td>-3.8360</td>\n",
+       "      <td>1.02240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.040108</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-7.9510</td>\n",
+       "      <td>2.55730</td>\n",
+       "      <td>3.05790</td>\n",
+       "      <td>-1.466300</td>\n",
+       "      <td>-1.673800</td>\n",
+       "      <td>1.99540</td>\n",
+       "      <td>-4.2176</td>\n",
+       "      <td>-4.49940</td>\n",
+       "      <td>-3.4922</td>\n",
+       "      <td>1.01170</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>384 rows × 507 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
+       "0                      DMSO            0.000000                   0.000000   \n",
+       "1                      DMSO            0.000000                   0.000000   \n",
+       "2                      DMSO            0.000000                   0.000000   \n",
+       "3                      DMSO            0.000000                   0.000000   \n",
+       "4                      DMSO            0.000000                   0.000000   \n",
+       "..                      ...                 ...                        ...   \n",
+       "379  BRD-K82746043-001-15-1            3.248700                   3.333300   \n",
+       "380  BRD-K82746043-001-15-1            1.082900                   1.111100   \n",
+       "381  BRD-K82746043-001-15-1            0.360970                   0.370370   \n",
+       "382  BRD-K82746043-001-15-1            0.120320                   0.123460   \n",
+       "383  BRD-K82746043-001-15-1            0.040108                   0.041152   \n",
+       "\n",
+       "    Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
+       "0                NaN                     NaN                A01   \n",
+       "1                NaN                     NaN                A02   \n",
+       "2                NaN                     NaN                A03   \n",
+       "3                NaN                     NaN                A04   \n",
+       "4                NaN                     NaN                A05   \n",
+       "..               ...                     ...                ...   \n",
+       "379    BRD-K82746043  BRD-K82746043-001-15-1                P20   \n",
+       "380    BRD-K82746043  BRD-K82746043-001-15-1                P21   \n",
+       "381    BRD-K82746043  BRD-K82746043-001-15-1                P22   \n",
+       "382    BRD-K82746043  BRD-K82746043-001-15-1                P23   \n",
+       "383    BRD-K82746043  BRD-K82746043-001-15-1                P24   \n",
+       "\n",
+       "    Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
+       "0                      control            control               NaN   \n",
+       "1                      control            control               NaN   \n",
+       "2                      control            control               NaN   \n",
+       "3                      control            control               NaN   \n",
+       "4                      control            control               NaN   \n",
+       "..                         ...                ...               ...   \n",
+       "379                        trt                trt     BRD-K82746043   \n",
+       "380                        trt                trt     BRD-K82746043   \n",
+       "381                        trt                trt     BRD-K82746043   \n",
+       "382                        trt                trt     BRD-K82746043   \n",
+       "383                        trt                trt     BRD-K82746043   \n",
+       "\n",
+       "    Metadata_InChIKey14  ... Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "0                   NaN  ...                                        -1.3544   \n",
+       "1                   NaN  ...                                        -2.3840   \n",
+       "2                   NaN  ...                                        -1.9493   \n",
+       "3                   NaN  ...                                        -2.2909   \n",
+       "4                   NaN  ...                                        -1.8955   \n",
+       "..                  ...  ...                                            ...   \n",
+       "379      JLYAXFNOILIKPP  ...                                        -6.1522   \n",
+       "380      JLYAXFNOILIKPP  ...                                        -5.1586   \n",
+       "381      JLYAXFNOILIKPP  ...                                        -5.9475   \n",
+       "382      JLYAXFNOILIKPP  ...                                        -8.4408   \n",
+       "383      JLYAXFNOILIKPP  ...                                        -7.9510   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "0                                          -1.07770   \n",
+       "1                                          -0.73440   \n",
+       "2                                          -0.36148   \n",
+       "3                                          -0.46380   \n",
+       "4                                          -1.05350   \n",
+       "..                                              ...   \n",
+       "379                                         1.81410   \n",
+       "380                                         1.50580   \n",
+       "381                                         1.42100   \n",
+       "382                                         2.99620   \n",
+       "383                                         2.55730   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "0                                         2.26020   \n",
+       "1                                         1.12090   \n",
+       "2                                         0.44050   \n",
+       "3                                         0.96434   \n",
+       "4                                         1.64840   \n",
+       "..                                            ...   \n",
+       "379                                       1.54220   \n",
+       "380                                       1.68420   \n",
+       "381                                       1.51020   \n",
+       "382                                       2.55230   \n",
+       "383                                       3.05790   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "0                                          -0.377010   \n",
+       "1                                          -0.182500   \n",
+       "2                                           0.326660   \n",
+       "3                                           1.132200   \n",
+       "4                                           0.057781   \n",
+       "..                                               ...   \n",
+       "379                                        -1.874700   \n",
+       "380                                        -1.126400   \n",
+       "381                                        -1.103600   \n",
+       "382                                        -2.275200   \n",
+       "383                                        -1.466300   \n",
+       "\n",
+       "     Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "0                                          -0.065840   \n",
+       "1                                          -0.061450   \n",
+       "2                                           0.547200   \n",
+       "3                                           0.753500   \n",
+       "4                                           0.070229   \n",
+       "..                                               ...   \n",
+       "379                                        -1.133900   \n",
+       "380                                        -1.066600   \n",
+       "381                                        -1.666500   \n",
+       "382                                        -1.783500   \n",
+       "383                                        -1.673800   \n",
+       "\n",
+       "     Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "0                              2.12360                              2.8740   \n",
+       "1                              0.66985                              2.3919   \n",
+       "2                              0.25015                              1.2271   \n",
+       "3                              0.31403                              1.4384   \n",
+       "4                              1.60990                              1.1296   \n",
+       "..                                 ...                                 ...   \n",
+       "379                            1.57540                             -3.0962   \n",
+       "380                            1.24740                             -1.5305   \n",
+       "381                            1.19840                             -2.6086   \n",
+       "382                            2.49200                             -4.3964   \n",
+       "383                            1.99540                             -4.2176   \n",
+       "\n",
+       "     Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "0                               2.87500                             2.3047   \n",
+       "1                               2.35230                             1.8672   \n",
+       "2                               0.77847                             1.0651   \n",
+       "3                               1.48110                             1.2943   \n",
+       "4                               0.90213                             1.1016   \n",
+       "..                                  ...                                ...   \n",
+       "379                            -3.25160                            -2.7683   \n",
+       "380                            -1.79020                            -1.2474   \n",
+       "381                            -2.97620                            -2.0026   \n",
+       "382                            -4.19030                            -3.8360   \n",
+       "383                            -4.49940                            -3.4922   \n",
+       "\n",
+       "     Nuclei_Texture_Variance_RNA_10_0  \n",
+       "0                            -0.92358  \n",
+       "1                            -0.11820  \n",
+       "2                            -0.44810  \n",
+       "3                            -0.83810  \n",
+       "4                             0.53225  \n",
+       "..                                ...  \n",
+       "379                           1.40170  \n",
+       "380                           1.17600  \n",
+       "381                           0.91557  \n",
+       "382                           1.02240  \n",
+       "383                           1.01170  \n",
+       "\n",
+       "[384 rows x 507 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "commit = \"da8ae6a3bc103346095d61b4ee02f08fc85a5d98\"\n",
+    "plate = \"SQ00014812\"\n",
+    "url = f\"https://media.githubusercontent.com/media/broadinstitute/lincs-cell-painting/{commit}/profiles/2016_04_01_a549_48hr_batch1/{plate}/{plate}_normalized_feature_select.csv.gz\"\n",
+    "\n",
+    "df = pd.read_csv(url)\n",
+    "df = df.loc[:, df.nunique() > 1] # remove constant columns\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculate mAP for assessing phenotypic activity of compounds\n",
+    "\n",
+    "Phenotypic activity of a perturbation reflects the average extent to which its replicate profiles are more similar to each other compared to control profiles (see Figure 1E in the [pre-print](https://doi.org/10.1101/2024.04.01.587631)).\n",
+    "\n",
+    "Here, we treat different doses of each compound as replicates and assess how well we can retrieve them by similarity against the group of negative controls (DMSO).\n",
+    "\n",
+    "To ensure correct grouping of profiles, we will add a dummy column that is equal to row index for all compound replicates and to -1 for all DMSO replicates. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>Metadata_InChIKey14</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "      <th>Metadata_treatment_index</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A01</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.07770</td>\n",
+       "      <td>2.26020</td>\n",
+       "      <td>-0.377010</td>\n",
+       "      <td>-0.065840</td>\n",
+       "      <td>2.12360</td>\n",
+       "      <td>2.8740</td>\n",
+       "      <td>2.87500</td>\n",
+       "      <td>2.3047</td>\n",
+       "      <td>-0.92358</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A02</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.73440</td>\n",
+       "      <td>1.12090</td>\n",
+       "      <td>-0.182500</td>\n",
+       "      <td>-0.061450</td>\n",
+       "      <td>0.66985</td>\n",
+       "      <td>2.3919</td>\n",
+       "      <td>2.35230</td>\n",
+       "      <td>1.8672</td>\n",
+       "      <td>-0.11820</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A03</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36148</td>\n",
+       "      <td>0.44050</td>\n",
+       "      <td>0.326660</td>\n",
+       "      <td>0.547200</td>\n",
+       "      <td>0.25015</td>\n",
+       "      <td>1.2271</td>\n",
+       "      <td>0.77847</td>\n",
+       "      <td>1.0651</td>\n",
+       "      <td>-0.44810</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A04</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.46380</td>\n",
+       "      <td>0.96434</td>\n",
+       "      <td>1.132200</td>\n",
+       "      <td>0.753500</td>\n",
+       "      <td>0.31403</td>\n",
+       "      <td>1.4384</td>\n",
+       "      <td>1.48110</td>\n",
+       "      <td>1.2943</td>\n",
+       "      <td>-0.83810</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A05</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.05350</td>\n",
+       "      <td>1.64840</td>\n",
+       "      <td>0.057781</td>\n",
+       "      <td>0.070229</td>\n",
+       "      <td>1.60990</td>\n",
+       "      <td>1.1296</td>\n",
+       "      <td>0.90213</td>\n",
+       "      <td>1.1016</td>\n",
+       "      <td>0.53225</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>3.248700</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.81410</td>\n",
+       "      <td>1.54220</td>\n",
+       "      <td>-1.874700</td>\n",
+       "      <td>-1.133900</td>\n",
+       "      <td>1.57540</td>\n",
+       "      <td>-3.0962</td>\n",
+       "      <td>-3.25160</td>\n",
+       "      <td>-2.7683</td>\n",
+       "      <td>1.40170</td>\n",
+       "      <td>379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>1.082900</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.50580</td>\n",
+       "      <td>1.68420</td>\n",
+       "      <td>-1.126400</td>\n",
+       "      <td>-1.066600</td>\n",
+       "      <td>1.24740</td>\n",
+       "      <td>-1.5305</td>\n",
+       "      <td>-1.79020</td>\n",
+       "      <td>-1.2474</td>\n",
+       "      <td>1.17600</td>\n",
+       "      <td>380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.360970</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.42100</td>\n",
+       "      <td>1.51020</td>\n",
+       "      <td>-1.103600</td>\n",
+       "      <td>-1.666500</td>\n",
+       "      <td>1.19840</td>\n",
+       "      <td>-2.6086</td>\n",
+       "      <td>-2.97620</td>\n",
+       "      <td>-2.0026</td>\n",
+       "      <td>0.91557</td>\n",
+       "      <td>381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.120320</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.99620</td>\n",
+       "      <td>2.55230</td>\n",
+       "      <td>-2.275200</td>\n",
+       "      <td>-1.783500</td>\n",
+       "      <td>2.49200</td>\n",
+       "      <td>-4.3964</td>\n",
+       "      <td>-4.19030</td>\n",
+       "      <td>-3.8360</td>\n",
+       "      <td>1.02240</td>\n",
+       "      <td>382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.040108</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.55730</td>\n",
+       "      <td>3.05790</td>\n",
+       "      <td>-1.466300</td>\n",
+       "      <td>-1.673800</td>\n",
+       "      <td>1.99540</td>\n",
+       "      <td>-4.2176</td>\n",
+       "      <td>-4.49940</td>\n",
+       "      <td>-3.4922</td>\n",
+       "      <td>1.01170</td>\n",
+       "      <td>383</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>384 rows × 508 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
+       "0                      DMSO            0.000000                   0.000000   \n",
+       "1                      DMSO            0.000000                   0.000000   \n",
+       "2                      DMSO            0.000000                   0.000000   \n",
+       "3                      DMSO            0.000000                   0.000000   \n",
+       "4                      DMSO            0.000000                   0.000000   \n",
+       "..                      ...                 ...                        ...   \n",
+       "379  BRD-K82746043-001-15-1            3.248700                   3.333300   \n",
+       "380  BRD-K82746043-001-15-1            1.082900                   1.111100   \n",
+       "381  BRD-K82746043-001-15-1            0.360970                   0.370370   \n",
+       "382  BRD-K82746043-001-15-1            0.120320                   0.123460   \n",
+       "383  BRD-K82746043-001-15-1            0.040108                   0.041152   \n",
+       "\n",
+       "    Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
+       "0                NaN                     NaN                A01   \n",
+       "1                NaN                     NaN                A02   \n",
+       "2                NaN                     NaN                A03   \n",
+       "3                NaN                     NaN                A04   \n",
+       "4                NaN                     NaN                A05   \n",
+       "..               ...                     ...                ...   \n",
+       "379    BRD-K82746043  BRD-K82746043-001-15-1                P20   \n",
+       "380    BRD-K82746043  BRD-K82746043-001-15-1                P21   \n",
+       "381    BRD-K82746043  BRD-K82746043-001-15-1                P22   \n",
+       "382    BRD-K82746043  BRD-K82746043-001-15-1                P23   \n",
+       "383    BRD-K82746043  BRD-K82746043-001-15-1                P24   \n",
+       "\n",
+       "    Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
+       "0                      control            control               NaN   \n",
+       "1                      control            control               NaN   \n",
+       "2                      control            control               NaN   \n",
+       "3                      control            control               NaN   \n",
+       "4                      control            control               NaN   \n",
+       "..                         ...                ...               ...   \n",
+       "379                        trt                trt     BRD-K82746043   \n",
+       "380                        trt                trt     BRD-K82746043   \n",
+       "381                        trt                trt     BRD-K82746043   \n",
+       "382                        trt                trt     BRD-K82746043   \n",
+       "383                        trt                trt     BRD-K82746043   \n",
+       "\n",
+       "    Metadata_InChIKey14  ... Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "0                   NaN  ...                                        -1.07770   \n",
+       "1                   NaN  ...                                        -0.73440   \n",
+       "2                   NaN  ...                                        -0.36148   \n",
+       "3                   NaN  ...                                        -0.46380   \n",
+       "4                   NaN  ...                                        -1.05350   \n",
+       "..                  ...  ...                                             ...   \n",
+       "379      JLYAXFNOILIKPP  ...                                         1.81410   \n",
+       "380      JLYAXFNOILIKPP  ...                                         1.50580   \n",
+       "381      JLYAXFNOILIKPP  ...                                         1.42100   \n",
+       "382      JLYAXFNOILIKPP  ...                                         2.99620   \n",
+       "383      JLYAXFNOILIKPP  ...                                         2.55730   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "0                                         2.26020   \n",
+       "1                                         1.12090   \n",
+       "2                                         0.44050   \n",
+       "3                                         0.96434   \n",
+       "4                                         1.64840   \n",
+       "..                                            ...   \n",
+       "379                                       1.54220   \n",
+       "380                                       1.68420   \n",
+       "381                                       1.51020   \n",
+       "382                                       2.55230   \n",
+       "383                                       3.05790   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "0                                          -0.377010   \n",
+       "1                                          -0.182500   \n",
+       "2                                           0.326660   \n",
+       "3                                           1.132200   \n",
+       "4                                           0.057781   \n",
+       "..                                               ...   \n",
+       "379                                        -1.874700   \n",
+       "380                                        -1.126400   \n",
+       "381                                        -1.103600   \n",
+       "382                                        -2.275200   \n",
+       "383                                        -1.466300   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "0                                         -0.065840   \n",
+       "1                                         -0.061450   \n",
+       "2                                          0.547200   \n",
+       "3                                          0.753500   \n",
+       "4                                          0.070229   \n",
+       "..                                              ...   \n",
+       "379                                       -1.133900   \n",
+       "380                                       -1.066600   \n",
+       "381                                       -1.666500   \n",
+       "382                                       -1.783500   \n",
+       "383                                       -1.673800   \n",
+       "\n",
+       "     Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "0                              2.12360                              2.8740   \n",
+       "1                              0.66985                              2.3919   \n",
+       "2                              0.25015                              1.2271   \n",
+       "3                              0.31403                              1.4384   \n",
+       "4                              1.60990                              1.1296   \n",
+       "..                                 ...                                 ...   \n",
+       "379                            1.57540                             -3.0962   \n",
+       "380                            1.24740                             -1.5305   \n",
+       "381                            1.19840                             -2.6086   \n",
+       "382                            2.49200                             -4.3964   \n",
+       "383                            1.99540                             -4.2176   \n",
+       "\n",
+       "     Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "0                               2.87500                             2.3047   \n",
+       "1                               2.35230                             1.8672   \n",
+       "2                               0.77847                             1.0651   \n",
+       "3                               1.48110                             1.2943   \n",
+       "4                               0.90213                             1.1016   \n",
+       "..                                  ...                                ...   \n",
+       "379                            -3.25160                            -2.7683   \n",
+       "380                            -1.79020                            -1.2474   \n",
+       "381                            -2.97620                            -2.0026   \n",
+       "382                            -4.19030                            -3.8360   \n",
+       "383                            -4.49940                            -3.4922   \n",
+       "\n",
+       "     Nuclei_Texture_Variance_RNA_10_0  Metadata_treatment_index  \n",
+       "0                            -0.92358                        -1  \n",
+       "1                            -0.11820                        -1  \n",
+       "2                            -0.44810                        -1  \n",
+       "3                            -0.83810                        -1  \n",
+       "4                             0.53225                        -1  \n",
+       "..                                ...                       ...  \n",
+       "379                           1.40170                       379  \n",
+       "380                           1.17600                       380  \n",
+       "381                           0.91557                       381  \n",
+       "382                           1.02240                       382  \n",
+       "383                           1.01170                       383  \n",
+       "\n",
+       "[384 rows x 508 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Metadata_treatment_index\"] = df.index\n",
+    "df.loc[df[\"Metadata_broad_sample\"] == \"DMSO\", \"Metadata_treatment_index\"] = -1\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define the rules by which profiles are grouped based on metadata:\n",
+    "\n",
+    "* Two profiles are a positive pair if they belong to the same group (i.e., they are replicates of the same compound). Therefore, they should share the same value in the metadata column that identifies the specific compound. We add this column to a list names `pos_sameby`.\n",
+    "\n",
+    "* No metadata columns are additionally needed to tell apart replicates of the same compound here (although in theory, one could request them to be from different plate rows or columns, for instance). So we keep `pos_diffby` empty.\n",
+    "\n",
+    "* Two profiles are a negative pair when one of them belongs to a group of compound replicates and another to a group of controls. That means they should be different both in the metadata column that identifies the specific compound and the treatment index columns that we created. The latter is needed to ensure that replicates of compounds are retrieved against only negative controls at this stage (and not against replicates of other compounds). We list these columns in `neg_diffby`.\n",
+    "\n",
+    "* No metadata columns are additionally needed to define negative pairs. So we keep `neg_sameby` empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pos_sameby = [\"Metadata_broad_sample\"]\n",
+    "pos_diffby = []\n",
+    "\n",
+    "neg_sameby = []\n",
+    "neg_diffby = [\"Metadata_broad_sample\", \"Metadata_treatment_index\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `average_precision` function to calculate the average precision score for each replicate of each compound."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed2301c774fb40a693c2eca1905ee301",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "467c254ad9cc414eba0f1bd9887457d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>Metadata_InChIKey14</th>\n",
+       "      <th>Metadata_moa</th>\n",
+       "      <th>Metadata_target</th>\n",
+       "      <th>Metadata_broad_date</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>Metadata_treatment_index</th>\n",
+       "      <th>n_pos_pairs</th>\n",
+       "      <th>n_total_pairs</th>\n",
+       "      <th>average_precision</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>5.655600</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A07</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A07</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.050922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>1.885200</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A08</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A08</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.308904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.628400</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A09</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A09</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.412513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.209470</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A10</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.377730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.069823</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A11</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.715591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>3.248700</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>379</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.726786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>1.082900</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>380</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.658824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.360970</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>381</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.517619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.120320</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>382</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.543290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.040108</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>383</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.535714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>360 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
+       "6    BRD-K74363950-004-01-0            5.655600                  10.000000   \n",
+       "7    BRD-K74363950-004-01-0            1.885200                   3.333300   \n",
+       "8    BRD-K74363950-004-01-0            0.628400                   1.111100   \n",
+       "9    BRD-K74363950-004-01-0            0.209470                   0.370370   \n",
+       "10   BRD-K74363950-004-01-0            0.069823                   0.123460   \n",
+       "..                      ...                 ...                        ...   \n",
+       "379  BRD-K82746043-001-15-1            3.248700                   3.333300   \n",
+       "380  BRD-K82746043-001-15-1            1.082900                   1.111100   \n",
+       "381  BRD-K82746043-001-15-1            0.360970                   0.370370   \n",
+       "382  BRD-K82746043-001-15-1            0.120320                   0.123460   \n",
+       "383  BRD-K82746043-001-15-1            0.040108                   0.041152   \n",
+       "\n",
+       "    Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
+       "6      BRD-K74363950  BRD-K74363950-004-01-0                A07   \n",
+       "7      BRD-K74363950  BRD-K74363950-004-01-0                A08   \n",
+       "8      BRD-K74363950  BRD-K74363950-004-01-0                A09   \n",
+       "9      BRD-K74363950  BRD-K74363950-004-01-0                A10   \n",
+       "10     BRD-K74363950  BRD-K74363950-004-01-0                A11   \n",
+       "..               ...                     ...                ...   \n",
+       "379    BRD-K82746043  BRD-K82746043-001-15-1                P20   \n",
+       "380    BRD-K82746043  BRD-K82746043-001-15-1                P21   \n",
+       "381    BRD-K82746043  BRD-K82746043-001-15-1                P22   \n",
+       "382    BRD-K82746043  BRD-K82746043-001-15-1                P23   \n",
+       "383    BRD-K82746043  BRD-K82746043-001-15-1                P24   \n",
+       "\n",
+       "    Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
+       "6                          trt                trt     BRD-K74363950   \n",
+       "7                          trt                trt     BRD-K74363950   \n",
+       "8                          trt                trt     BRD-K74363950   \n",
+       "9                          trt                trt     BRD-K74363950   \n",
+       "10                         trt                trt     BRD-K74363950   \n",
+       "..                         ...                ...               ...   \n",
+       "379                        trt                trt     BRD-K82746043   \n",
+       "380                        trt                trt     BRD-K82746043   \n",
+       "381                        trt                trt     BRD-K82746043   \n",
+       "382                        trt                trt     BRD-K82746043   \n",
+       "383                        trt                trt     BRD-K82746043   \n",
+       "\n",
+       "    Metadata_InChIKey14                       Metadata_moa  \\\n",
+       "6        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "7        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "8        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "9        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "10       ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "..                  ...                                ...   \n",
+       "379      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "380      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "381      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "382      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "383      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "\n",
+       "                   Metadata_target Metadata_broad_date Metadata_Well  \\\n",
+       "6    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A07   \n",
+       "7    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A08   \n",
+       "8    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A09   \n",
+       "9    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A10   \n",
+       "10   CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A11   \n",
+       "..                             ...                 ...           ...   \n",
+       "379             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P20   \n",
+       "380             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P21   \n",
+       "381             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P22   \n",
+       "382             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P23   \n",
+       "383             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P24   \n",
+       "\n",
+       "     Metadata_treatment_index  n_pos_pairs  n_total_pairs  average_precision  \n",
+       "6                           6            5            383           0.050922  \n",
+       "7                           7            5            383           0.308904  \n",
+       "8                           8            5            383           0.412513  \n",
+       "9                           9            5            383           0.377730  \n",
+       "10                         10            5            383           0.715591  \n",
+       "..                        ...          ...            ...                ...  \n",
+       "379                       379            5            383           0.726786  \n",
+       "380                       380            5            383           0.658824  \n",
+       "381                       381            5            383           0.517619  \n",
+       "382                       382            5            383           0.543290  \n",
+       "383                       383            5            383           0.535714  \n",
+       "\n",
+       "[360 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata = df.filter(regex=\"^Metadata\")\n",
+    "profiles = df.filter(regex=\"^(?!Metadata)\").values\n",
+    "\n",
+    "ap_scores = map.average_precision(metadata, profiles, pos_sameby, pos_diffby, neg_sameby, neg_diffby)\n",
+    "ap_scores = ap_scores.query(\"Metadata_broad_sample != 'DMSO'\") # remove DMSO\n",
+    "ap_scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the next step, we average the AP scores, calculate p-value using permutation testing, and perform FDR correction to compare across compounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79a1c25da230422ca6eeda791da76663",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7011f708da8f417d9ffdf43a26e5df08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/58 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>p_value</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>below_p</th>\n",
+       "      <th>below_corrected_p</th>\n",
+       "      <th>-log10_p_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BRD-A69275535-001-01-5</td>\n",
+       "      <td>0.203576</td>\n",
+       "      <td>0.012899</td>\n",
+       "      <td>0.016390</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.785430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BRD-A69636825-003-04-7</td>\n",
+       "      <td>0.269093</td>\n",
+       "      <td>0.000800</td>\n",
+       "      <td>0.001365</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.865004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BRD-A69815203-001-07-6</td>\n",
+       "      <td>0.862226</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BRD-A70858459-001-01-7</td>\n",
+       "      <td>0.351816</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.263986</td>\n",
+       "      <td>0.000900</td>\n",
+       "      <td>0.001491</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.826441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BRD-A72390365-001-15-2</td>\n",
+       "      <td>0.554667</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BRD-A73368467-003-17-6</td>\n",
+       "      <td>0.788666</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>BRD-A74980173-001-11-9</td>\n",
+       "      <td>0.500600</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BRD-A81233518-004-16-1</td>\n",
+       "      <td>0.140208</td>\n",
+       "      <td>0.015598</td>\n",
+       "      <td>0.018700</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.728154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BRD-A82035391-001-02-7</td>\n",
+       "      <td>0.052362</td>\n",
+       "      <td>0.077692</td>\n",
+       "      <td>0.078692</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.104069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>BRD-A82156122-001-01-9</td>\n",
+       "      <td>0.082152</td>\n",
+       "      <td>0.038296</td>\n",
+       "      <td>0.041909</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.377693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>BRD-K50691590-001-02-2</td>\n",
+       "      <td>0.995162</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>BRD-K60230970-001-10-0</td>\n",
+       "      <td>0.951137</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>BRD-K67789209-001-01-0</td>\n",
+       "      <td>0.317370</td>\n",
+       "      <td>0.000500</td>\n",
+       "      <td>0.000906</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.042795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>BRD-K67844266-003-01-9</td>\n",
+       "      <td>0.356141</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>BRD-K68103045-001-02-9</td>\n",
+       "      <td>0.268105</td>\n",
+       "      <td>0.000800</td>\n",
+       "      <td>0.001365</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.865004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>BRD-K68132782-003-13-8</td>\n",
+       "      <td>0.192768</td>\n",
+       "      <td>0.012899</td>\n",
+       "      <td>0.016390</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.785430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>BRD-K68164687-001-01-6</td>\n",
+       "      <td>0.938532</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>BRD-K68232413-001-01-2</td>\n",
+       "      <td>0.256140</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.001526</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.816399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>BRD-K68488863-001-04-9</td>\n",
+       "      <td>0.106826</td>\n",
+       "      <td>0.028397</td>\n",
+       "      <td>0.032295</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.490867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>BRD-K68532323-003-02-8</td>\n",
+       "      <td>0.701013</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>BRD-K68552125-001-05-3</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>BRD-K68693535-001-03-4</td>\n",
+       "      <td>0.072458</td>\n",
+       "      <td>0.048295</td>\n",
+       "      <td>0.050929</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.293031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>BRD-K68747584-001-02-0</td>\n",
+       "      <td>0.673878</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>BRD-K68938568-001-01-7</td>\n",
+       "      <td>0.336573</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>0.000773</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.111677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>BRD-K69236721-001-02-7</td>\n",
+       "      <td>0.356350</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>BRD-K69247067-001-01-8</td>\n",
+       "      <td>0.551509</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>BRD-K70330367-003-07-9</td>\n",
+       "      <td>0.150161</td>\n",
+       "      <td>0.014399</td>\n",
+       "      <td>0.017768</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.750351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>BRD-K70358946-001-15-7</td>\n",
+       "      <td>0.477971</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>BRD-K70401845-003-09-6</td>\n",
+       "      <td>0.688838</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>BRD-K70463136-001-01-5</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>BRD-K70557564-305-03-6</td>\n",
+       "      <td>0.126098</td>\n",
+       "      <td>0.018298</td>\n",
+       "      <td>0.021226</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.673134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>BRD-K70778732-003-26-7</td>\n",
+       "      <td>0.206752</td>\n",
+       "      <td>0.012899</td>\n",
+       "      <td>0.016390</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.785430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>BRD-K70912147-001-01-8</td>\n",
+       "      <td>0.097564</td>\n",
+       "      <td>0.030197</td>\n",
+       "      <td>0.033681</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.472612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>BRD-K70914287-300-02-8</td>\n",
+       "      <td>0.409013</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>BRD-K71035033-001-07-1</td>\n",
+       "      <td>0.052030</td>\n",
+       "      <td>0.078692</td>\n",
+       "      <td>0.078692</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.104069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>BRD-K71221037-001-01-6</td>\n",
+       "      <td>0.726187</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>BRD-K71281111-001-04-3</td>\n",
+       "      <td>0.353957</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>BRD-K71289571-001-11-4</td>\n",
+       "      <td>0.257474</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.001526</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.816399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>BRD-K71480163-001-01-4</td>\n",
+       "      <td>0.223272</td>\n",
+       "      <td>0.004900</td>\n",
+       "      <td>0.006931</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.159203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>BRD-K72215350-001-06-5</td>\n",
+       "      <td>0.466051</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>BRD-K72222507-003-16-8</td>\n",
+       "      <td>0.073473</td>\n",
+       "      <td>0.047095</td>\n",
+       "      <td>0.050584</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.295988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>BRD-K72414522-001-06-7</td>\n",
+       "      <td>0.186676</td>\n",
+       "      <td>0.012899</td>\n",
+       "      <td>0.016390</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.785430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>BRD-K73196317-003-14-8</td>\n",
+       "      <td>0.136886</td>\n",
+       "      <td>0.015798</td>\n",
+       "      <td>0.018700</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.728154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>BRD-K73237276-001-01-0</td>\n",
+       "      <td>0.239427</td>\n",
+       "      <td>0.001800</td>\n",
+       "      <td>0.002677</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.572408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>BRD-K73319509-001-08-0</td>\n",
+       "      <td>0.231632</td>\n",
+       "      <td>0.002700</td>\n",
+       "      <td>0.003915</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.407312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.408596</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>BRD-K75958547-238-01-0</td>\n",
+       "      <td>0.260311</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.001526</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.816399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>BRD-K76908866-001-07-6</td>\n",
+       "      <td>0.172913</td>\n",
+       "      <td>0.012999</td>\n",
+       "      <td>0.016390</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.785430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>BRD-K77908580-001-09-6</td>\n",
+       "      <td>0.509422</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>BRD-K79254416-001-22-6</td>\n",
+       "      <td>0.866124</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>BRD-K80700417-001-04-2</td>\n",
+       "      <td>0.070291</td>\n",
+       "      <td>0.050195</td>\n",
+       "      <td>0.051988</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.284100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>BRD-K81144366-003-19-7</td>\n",
+       "      <td>0.311539</td>\n",
+       "      <td>0.000500</td>\n",
+       "      <td>0.000906</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.042795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>BRD-K81258678-001-01-0</td>\n",
+       "      <td>0.758700</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>BRD-K81957469-001-01-0</td>\n",
+       "      <td>0.428269</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>BRD-K82135108-001-04-3</td>\n",
+       "      <td>0.419468</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>BRD-K82677201-001-01-6</td>\n",
+       "      <td>0.724665</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.592890</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_broad_sample  mean_average_precision   p_value  \\\n",
+       "0   BRD-A69275535-001-01-5                0.203576  0.012899   \n",
+       "1   BRD-A69636825-003-04-7                0.269093  0.000800   \n",
+       "2   BRD-A69815203-001-07-6                0.862226  0.000100   \n",
+       "3   BRD-A70858459-001-01-7                0.351816  0.000200   \n",
+       "4   BRD-A72309220-001-04-1                0.263986  0.000900   \n",
+       "5   BRD-A72390365-001-15-2                0.554667  0.000100   \n",
+       "6   BRD-A73368467-003-17-6                0.788666  0.000100   \n",
+       "7   BRD-A74980173-001-11-9                0.500600  0.000100   \n",
+       "8   BRD-A81233518-004-16-1                0.140208  0.015598   \n",
+       "9   BRD-A82035391-001-02-7                0.052362  0.077692   \n",
+       "10  BRD-A82156122-001-01-9                0.082152  0.038296   \n",
+       "11  BRD-K50691590-001-02-2                0.995162  0.000100   \n",
+       "12  BRD-K60230970-001-10-0                0.951137  0.000100   \n",
+       "13  BRD-K67789209-001-01-0                0.317370  0.000500   \n",
+       "14  BRD-K67844266-003-01-9                0.356141  0.000200   \n",
+       "15  BRD-K68103045-001-02-9                0.268105  0.000800   \n",
+       "16  BRD-K68132782-003-13-8                0.192768  0.012899   \n",
+       "17  BRD-K68164687-001-01-6                0.938532  0.000100   \n",
+       "18  BRD-K68232413-001-01-2                0.256140  0.001000   \n",
+       "19  BRD-K68488863-001-04-9                0.106826  0.028397   \n",
+       "20  BRD-K68532323-003-02-8                0.701013  0.000100   \n",
+       "21  BRD-K68552125-001-05-3                1.000000  0.000100   \n",
+       "22  BRD-K68693535-001-03-4                0.072458  0.048295   \n",
+       "23  BRD-K68747584-001-02-0                0.673878  0.000100   \n",
+       "24  BRD-K68938568-001-01-7                0.336573  0.000400   \n",
+       "25  BRD-K69236721-001-02-7                0.356350  0.000200   \n",
+       "26  BRD-K69247067-001-01-8                0.551509  0.000100   \n",
+       "27  BRD-K70330367-003-07-9                0.150161  0.014399   \n",
+       "28  BRD-K70358946-001-15-7                0.477971  0.000100   \n",
+       "29  BRD-K70401845-003-09-6                0.688838  0.000100   \n",
+       "30  BRD-K70463136-001-01-5                1.000000  0.000100   \n",
+       "31  BRD-K70557564-305-03-6                0.126098  0.018298   \n",
+       "32  BRD-K70778732-003-26-7                0.206752  0.012899   \n",
+       "33  BRD-K70912147-001-01-8                0.097564  0.030197   \n",
+       "34  BRD-K70914287-300-02-8                0.409013  0.000200   \n",
+       "35  BRD-K71035033-001-07-1                0.052030  0.078692   \n",
+       "36  BRD-K71221037-001-01-6                0.726187  0.000100   \n",
+       "37  BRD-K71281111-001-04-3                0.353957  0.000200   \n",
+       "38  BRD-K71289571-001-11-4                0.257474  0.001000   \n",
+       "39  BRD-K71480163-001-01-4                0.223272  0.004900   \n",
+       "40  BRD-K72215350-001-06-5                0.466051  0.000100   \n",
+       "41  BRD-K72222507-003-16-8                0.073473  0.047095   \n",
+       "42  BRD-K72414522-001-06-7                0.186676  0.012899   \n",
+       "43  BRD-K73196317-003-14-8                0.136886  0.015798   \n",
+       "44  BRD-K73237276-001-01-0                0.239427  0.001800   \n",
+       "45  BRD-K73319509-001-08-0                0.231632  0.002700   \n",
+       "46  BRD-K74363950-004-01-0                0.408596  0.000200   \n",
+       "47  BRD-K75958547-238-01-0                0.260311  0.001000   \n",
+       "48  BRD-K76908866-001-07-6                0.172913  0.012999   \n",
+       "49  BRD-K77908580-001-09-6                0.509422  0.000100   \n",
+       "50  BRD-K79254416-001-22-6                0.866124  0.000100   \n",
+       "51  BRD-K80700417-001-04-2                0.070291  0.050195   \n",
+       "52  BRD-K81144366-003-19-7                0.311539  0.000500   \n",
+       "53  BRD-K81258678-001-01-0                0.758700  0.000100   \n",
+       "54  BRD-K81957469-001-01-0                0.428269  0.000200   \n",
+       "55  BRD-K82135108-001-04-3                0.419468  0.000200   \n",
+       "56  BRD-K82677201-001-01-6                0.724665  0.000100   \n",
+       "57  BRD-K82746043-001-15-1                0.592890  0.000100   \n",
+       "\n",
+       "    corrected_p_value  below_p  below_corrected_p  -log10_p_value  \n",
+       "0            0.016390     True               True        1.785430  \n",
+       "1            0.001365     True               True        2.865004  \n",
+       "2            0.000276     True               True        3.558835  \n",
+       "3            0.000400     True               True        3.397983  \n",
+       "4            0.001491     True               True        2.826441  \n",
+       "5            0.000276     True               True        3.558835  \n",
+       "6            0.000276     True               True        3.558835  \n",
+       "7            0.000276     True               True        3.558835  \n",
+       "8            0.018700     True               True        1.728154  \n",
+       "9            0.078692    False              False        1.104069  \n",
+       "10           0.041909     True               True        1.377693  \n",
+       "11           0.000276     True               True        3.558835  \n",
+       "12           0.000276     True               True        3.558835  \n",
+       "13           0.000906     True               True        3.042795  \n",
+       "14           0.000400     True               True        3.397983  \n",
+       "15           0.001365     True               True        2.865004  \n",
+       "16           0.016390     True               True        1.785430  \n",
+       "17           0.000276     True               True        3.558835  \n",
+       "18           0.001526     True               True        2.816399  \n",
+       "19           0.032295     True               True        1.490867  \n",
+       "20           0.000276     True               True        3.558835  \n",
+       "21           0.000276     True               True        3.558835  \n",
+       "22           0.050929     True              False        1.293031  \n",
+       "23           0.000276     True               True        3.558835  \n",
+       "24           0.000773     True               True        3.111677  \n",
+       "25           0.000400     True               True        3.397983  \n",
+       "26           0.000276     True               True        3.558835  \n",
+       "27           0.017768     True               True        1.750351  \n",
+       "28           0.000276     True               True        3.558835  \n",
+       "29           0.000276     True               True        3.558835  \n",
+       "30           0.000276     True               True        3.558835  \n",
+       "31           0.021226     True               True        1.673134  \n",
+       "32           0.016390     True               True        1.785430  \n",
+       "33           0.033681     True               True        1.472612  \n",
+       "34           0.000400     True               True        3.397983  \n",
+       "35           0.078692    False              False        1.104069  \n",
+       "36           0.000276     True               True        3.558835  \n",
+       "37           0.000400     True               True        3.397983  \n",
+       "38           0.001526     True               True        2.816399  \n",
+       "39           0.006931     True               True        2.159203  \n",
+       "40           0.000276     True               True        3.558835  \n",
+       "41           0.050584     True              False        1.295988  \n",
+       "42           0.016390     True               True        1.785430  \n",
+       "43           0.018700     True               True        1.728154  \n",
+       "44           0.002677     True               True        2.572408  \n",
+       "45           0.003915     True               True        2.407312  \n",
+       "46           0.000400     True               True        3.397983  \n",
+       "47           0.001526     True               True        2.816399  \n",
+       "48           0.016390     True               True        1.785430  \n",
+       "49           0.000276     True               True        3.558835  \n",
+       "50           0.000276     True               True        3.558835  \n",
+       "51           0.051988    False              False        1.284100  \n",
+       "52           0.000906     True               True        3.042795  \n",
+       "53           0.000276     True               True        3.558835  \n",
+       "54           0.000400     True               True        3.397983  \n",
+       "55           0.000400     True               True        3.397983  \n",
+       "56           0.000276     True               True        3.558835  \n",
+       "57           0.000276     True               True        3.558835  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map_scores = map.mean_average_precision(ap_scores, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
+    "map_scores[\"-log10_p_value\"] = -map_scores[\"corrected_p_value\"].apply(np.log10)\n",
+    "map_scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data=map_scores, x=\"mean_average_precision\", y=\"-log10_p_value\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
+    "plt.xlabel(\"mean_average_precision\")\n",
+    "plt.ylabel(\"-log10_p_value\")\n",
+    "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
+    "plt.text(0.75, 1.5, f\"Retrieved = {100*map_scores.below_corrected_p.mean():.2f}%\", va=\"center\", ha=\"left\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "copairs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 5489728..7ba9d08 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,7 @@ dependencies = [
 
 [project.optional-dependencies]
 plot = ["plotly"]
+demo = ["notebook", "matplotlib"]
 
 [project.urls]
 "Homepage" = "https://github.com/cytomining/copairs"

From 4e0e96f9a86a0a765196d6aed795cd545ea7ca2b Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Sun, 30 Jun 2024 20:22:37 -0400
Subject: [PATCH 17/30] add phenotypic consistency to example

---
 examples/demo.ipynb | 2930 +++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 2842 insertions(+), 88 deletions(-)

diff --git a/examples/demo.ipynb b/examples/demo.ipynb
index 0fea058..e89acbe 100644
--- a/examples/demo.ipynb
+++ b/examples/demo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,7 +10,21 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from copairs import map"
+    "from copairs import map\n",
+    "from pycytominer import aggregate\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports for showing Figure 1 from the paper\n",
+    "import requests\n",
+    "from PIL import Image\n",
+    "from io import BytesIO\n",
+    "from IPython.display import display"
    ]
   },
   {
@@ -26,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -490,7 +504,7 @@
        "[384 rows x 507 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -505,22 +519,92 @@
     "df"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([nan, 'CHRM1|CHRM2|CHRM3|CHRM4|CHRM5', 'HMGCR',\n",
+       "       'HDAC1|HDAC2|HDAC3|HDAC9', 'ERBB2', 'DNMT1|DNMT3A',\n",
+       "       'GABRA1|GABRA2|GABRA3|GABRA4|GABRA5|GABRA6', 'TUBB', 'KIF11',\n",
+       "       'PSMA1|PSMA2|PSMA3|PSMA4|PSMA5|PSMA6|PSMA7|PSMA8|PSMB1|PSMB10|PSMB11|PSMB2|PSMB3|PSMB4|PSMB5|PSMB6|PSMB7|PSMB8|PSMB9|PSMD1|PSMD2|RELA',\n",
+       "       'SQLE', 'GABRA1', 'KCNT2|TRPV4', 'AURKA|AURKB',\n",
+       "       'DRD2|GRIN2A|GRIN2B|GRIN2C|GRIN2D|GRIN3A', 'CFTR',\n",
+       "       'CACNA1C|CACNA1S|CACNA2D1|CACNG1|HTR3A|KCNA5',\n",
+       "       'ADRA1A|ADRA1B|ADRA2A|ADRA2B|ADRA2C|CHRM1|CHRM2|CHRM3|CHRM4|CHRM5|DRD1|DRD2|DRD3|DRD4|DRD5|HRH1|HTR1A|HTR1B|HTR1D|HTR1E|HTR2A|HTR2C|HTR3A|HTR6|HTR7',\n",
+       "       'EGFR|NR1I2', 'ADRA1A|ADRA2A|HRH1|HTR1A|HTR2A|HTR2B|HTR2C|SLC6A4',\n",
+       "       'EGFR|ERBB2', 'HIF1A', 'ESR1|ESR2|MAP1A|MAP2', 'SCN4A|SCN9A',\n",
+       "       'BIRC2|XIAP', 'AKT1|AKT2|AKT3|PRKG1', 'ACE',\n",
+       "       'HTR1A|HTR1B|HTR1D|HTR1E|HTR1F|HTR2A|HTR2B|HTR2C|HTR5A|HTR6|HTR7',\n",
+       "       'CYSLTR1|CYSLTR2', 'GAST', 'HTR1A', 'PSMB1', 'MET', 'NAE1|UBA3',\n",
+       "       'VDR', 'HRH1', 'HTR1A|HTR2A', 'AURKA|FLT3|KDR|PDGFRA|PTK2|SRC',\n",
+       "       'BIRC2|BIRC3|BIRC7|XIAP', 'ABCB1|ABCB4', 'KCNH2',\n",
+       "       'ABCB11|CAMLG|FPR1|PPIA|PPIF|PPP3CA|PPP3R2|SLC10A1|SLCO1B1|SLCO1B3',\n",
+       "       'FGFR3|KIT|PDGFRA|PDGFRB', 'FLT3|PIM1|PIM2|PIM3', 'PSEN1',\n",
+       "       'HSPA1A', 'ATP1A1', 'RELA', 'AVPR1A|AVPR2', 'DPP4',\n",
+       "       'BCL2|BCL2L1|BCL2L2'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Metadata_target\"].unique()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Calculate mAP for assessing phenotypic activity of compounds\n",
     "\n",
-    "Phenotypic activity of a perturbation reflects the average extent to which its replicate profiles are more similar to each other compared to control profiles (see Figure 1E in the [pre-print](https://doi.org/10.1101/2024.04.01.587631)).\n",
+    "Phenotypic activity of a perturbation reflects the average extent to which its replicate profiles are more similar to each other compared to control profiles (see Figure 1E)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=514x640>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure_url = \"https://www.biorxiv.org/content/biorxiv/early/2024/04/02/2024.04.01.587631/F1.large.jpg\"\n",
+    "response = requests.get(figure_url)\n",
+    "image = Image.open(BytesIO(response.content))\n",
     "\n",
+    "size = (514, 640)\n",
+    "image = image.resize(size)\n",
+    "display(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Here, we treat different doses of each compound as replicates and assess how well we can retrieve them by similarity against the group of negative controls (DMSO).\n",
     "\n",
-    "To ensure correct grouping of profiles, we will add a dummy column that is equal to row index for all compound replicates and to -1 for all DMSO replicates. "
+    "To ensure correct grouping of profiles, we can add a dummy column that is equal to row index for all compound replicates and to -1 for all DMSO replicates. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -971,14 +1055,17 @@
        "[384 rows x 508 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# make deafult value equal to row index\n",
     "df[\"Metadata_treatment_index\"] = df.index\n",
+    "# make index equal to -1 for all DMSO treatment replicates\n",
     "df.loc[df[\"Metadata_broad_sample\"] == \"DMSO\", \"Metadata_treatment_index\"] = -1\n",
+    "# now all treatment replicates differ in the index column, except for DMSO replicates\n",
     "df"
    ]
   },
@@ -999,14 +1086,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# positive pairs are replicates of the same treatment\n",
     "pos_sameby = [\"Metadata_broad_sample\"]\n",
     "pos_diffby = []\n",
     "\n",
     "neg_sameby = []\n",
+    "# negative pairs are replicates of different treatments \n",
     "neg_diffby = [\"Metadata_broad_sample\", \"Metadata_treatment_index\"]"
    ]
   },
@@ -1019,13 +1108,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ed2301c774fb40a693c2eca1905ee301",
+       "model_id": "1d31b7d0cc4c47ca8598bebec375b895",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1039,7 +1128,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "467c254ad9cc414eba0f1bd9887457d1",
+       "model_id": "aa1475e1749f433390abdd865fdee7f9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1410,7 +1499,7 @@
        "[360 rows x 18 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1433,13 +1522,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "79a1c25da230422ca6eeda791da76663",
+       "model_id": "5495ad647caf4f0b94f306299c3258d7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1453,7 +1542,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7011f708da8f417d9ffdf43a26e5df08",
+       "model_id": "ce7f604d72f54c2c81a0fc5a4eb30214",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1491,7 +1580,7 @@
        "      <th>corrected_p_value</th>\n",
        "      <th>below_p</th>\n",
        "      <th>below_corrected_p</th>\n",
-       "      <th>-log10_p_value</th>\n",
+       "      <th>-log10(p-value)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2140,93 +2229,93 @@
        "56  BRD-K82677201-001-01-6                0.724665  0.000100   \n",
        "57  BRD-K82746043-001-15-1                0.592890  0.000100   \n",
        "\n",
-       "    corrected_p_value  below_p  below_corrected_p  -log10_p_value  \n",
-       "0            0.016390     True               True        1.785430  \n",
-       "1            0.001365     True               True        2.865004  \n",
-       "2            0.000276     True               True        3.558835  \n",
-       "3            0.000400     True               True        3.397983  \n",
-       "4            0.001491     True               True        2.826441  \n",
-       "5            0.000276     True               True        3.558835  \n",
-       "6            0.000276     True               True        3.558835  \n",
-       "7            0.000276     True               True        3.558835  \n",
-       "8            0.018700     True               True        1.728154  \n",
-       "9            0.078692    False              False        1.104069  \n",
-       "10           0.041909     True               True        1.377693  \n",
-       "11           0.000276     True               True        3.558835  \n",
-       "12           0.000276     True               True        3.558835  \n",
-       "13           0.000906     True               True        3.042795  \n",
-       "14           0.000400     True               True        3.397983  \n",
-       "15           0.001365     True               True        2.865004  \n",
-       "16           0.016390     True               True        1.785430  \n",
-       "17           0.000276     True               True        3.558835  \n",
-       "18           0.001526     True               True        2.816399  \n",
-       "19           0.032295     True               True        1.490867  \n",
-       "20           0.000276     True               True        3.558835  \n",
-       "21           0.000276     True               True        3.558835  \n",
-       "22           0.050929     True              False        1.293031  \n",
-       "23           0.000276     True               True        3.558835  \n",
-       "24           0.000773     True               True        3.111677  \n",
-       "25           0.000400     True               True        3.397983  \n",
-       "26           0.000276     True               True        3.558835  \n",
-       "27           0.017768     True               True        1.750351  \n",
-       "28           0.000276     True               True        3.558835  \n",
-       "29           0.000276     True               True        3.558835  \n",
-       "30           0.000276     True               True        3.558835  \n",
-       "31           0.021226     True               True        1.673134  \n",
-       "32           0.016390     True               True        1.785430  \n",
-       "33           0.033681     True               True        1.472612  \n",
-       "34           0.000400     True               True        3.397983  \n",
-       "35           0.078692    False              False        1.104069  \n",
-       "36           0.000276     True               True        3.558835  \n",
-       "37           0.000400     True               True        3.397983  \n",
-       "38           0.001526     True               True        2.816399  \n",
-       "39           0.006931     True               True        2.159203  \n",
-       "40           0.000276     True               True        3.558835  \n",
-       "41           0.050584     True              False        1.295988  \n",
-       "42           0.016390     True               True        1.785430  \n",
-       "43           0.018700     True               True        1.728154  \n",
-       "44           0.002677     True               True        2.572408  \n",
-       "45           0.003915     True               True        2.407312  \n",
-       "46           0.000400     True               True        3.397983  \n",
-       "47           0.001526     True               True        2.816399  \n",
-       "48           0.016390     True               True        1.785430  \n",
-       "49           0.000276     True               True        3.558835  \n",
-       "50           0.000276     True               True        3.558835  \n",
-       "51           0.051988    False              False        1.284100  \n",
-       "52           0.000906     True               True        3.042795  \n",
-       "53           0.000276     True               True        3.558835  \n",
-       "54           0.000400     True               True        3.397983  \n",
-       "55           0.000400     True               True        3.397983  \n",
-       "56           0.000276     True               True        3.558835  \n",
-       "57           0.000276     True               True        3.558835  "
+       "    corrected_p_value  below_p  below_corrected_p  -log10(p-value)  \n",
+       "0            0.016390     True               True         1.785430  \n",
+       "1            0.001365     True               True         2.865004  \n",
+       "2            0.000276     True               True         3.558835  \n",
+       "3            0.000400     True               True         3.397983  \n",
+       "4            0.001491     True               True         2.826441  \n",
+       "5            0.000276     True               True         3.558835  \n",
+       "6            0.000276     True               True         3.558835  \n",
+       "7            0.000276     True               True         3.558835  \n",
+       "8            0.018700     True               True         1.728154  \n",
+       "9            0.078692    False              False         1.104069  \n",
+       "10           0.041909     True               True         1.377693  \n",
+       "11           0.000276     True               True         3.558835  \n",
+       "12           0.000276     True               True         3.558835  \n",
+       "13           0.000906     True               True         3.042795  \n",
+       "14           0.000400     True               True         3.397983  \n",
+       "15           0.001365     True               True         2.865004  \n",
+       "16           0.016390     True               True         1.785430  \n",
+       "17           0.000276     True               True         3.558835  \n",
+       "18           0.001526     True               True         2.816399  \n",
+       "19           0.032295     True               True         1.490867  \n",
+       "20           0.000276     True               True         3.558835  \n",
+       "21           0.000276     True               True         3.558835  \n",
+       "22           0.050929     True              False         1.293031  \n",
+       "23           0.000276     True               True         3.558835  \n",
+       "24           0.000773     True               True         3.111677  \n",
+       "25           0.000400     True               True         3.397983  \n",
+       "26           0.000276     True               True         3.558835  \n",
+       "27           0.017768     True               True         1.750351  \n",
+       "28           0.000276     True               True         3.558835  \n",
+       "29           0.000276     True               True         3.558835  \n",
+       "30           0.000276     True               True         3.558835  \n",
+       "31           0.021226     True               True         1.673134  \n",
+       "32           0.016390     True               True         1.785430  \n",
+       "33           0.033681     True               True         1.472612  \n",
+       "34           0.000400     True               True         3.397983  \n",
+       "35           0.078692    False              False         1.104069  \n",
+       "36           0.000276     True               True         3.558835  \n",
+       "37           0.000400     True               True         3.397983  \n",
+       "38           0.001526     True               True         2.816399  \n",
+       "39           0.006931     True               True         2.159203  \n",
+       "40           0.000276     True               True         3.558835  \n",
+       "41           0.050584     True              False         1.295988  \n",
+       "42           0.016390     True               True         1.785430  \n",
+       "43           0.018700     True               True         1.728154  \n",
+       "44           0.002677     True               True         2.572408  \n",
+       "45           0.003915     True               True         2.407312  \n",
+       "46           0.000400     True               True         3.397983  \n",
+       "47           0.001526     True               True         2.816399  \n",
+       "48           0.016390     True               True         1.785430  \n",
+       "49           0.000276     True               True         3.558835  \n",
+       "50           0.000276     True               True         3.558835  \n",
+       "51           0.051988    False              False         1.284100  \n",
+       "52           0.000906     True               True         3.042795  \n",
+       "53           0.000276     True               True         3.558835  \n",
+       "54           0.000400     True               True         3.397983  \n",
+       "55           0.000400     True               True         3.397983  \n",
+       "56           0.000276     True               True         3.558835  \n",
+       "57           0.000276     True               True         3.558835  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "map_scores = map.mean_average_precision(ap_scores, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
-    "map_scores[\"-log10_p_value\"] = -map_scores[\"corrected_p_value\"].apply(np.log10)\n",
-    "map_scores"
+    "map_scores[\"-log10(p-value)\"] = -map_scores[\"corrected_p_value\"].apply(np.log10)\n",
+    "map_scores.head(10)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we can plot the results:"
+    "Finally, we can plot the results and filter out phenotypicall inactive compounds with corrected p-value >0.05."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2236,19 +2325,2684 @@
     }
    ],
    "source": [
-    "plt.scatter(data=map_scores, x=\"mean_average_precision\", y=\"-log10_p_value\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
-    "plt.xlabel(\"mean_average_precision\")\n",
-    "plt.ylabel(\"-log10_p_value\")\n",
+    "plt.scatter(data=map_scores, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
+    "plt.xlabel(\"mAP\")\n",
+    "plt.ylabel(\"-log10(p-value)\")\n",
     "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
-    "plt.text(0.75, 1.5, f\"Retrieved = {100*map_scores.below_corrected_p.mean():.2f}%\", va=\"center\", ha=\"left\")\n",
+    "plt.text(0.5, 1.5, f\"Phenotypically active = {100*map_scores.below_corrected_p.mean():.2f}%\", va=\"center\", ha=\"left\")\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculate mAP for assessing phenotypic consistency of compounds grouped by targets\n",
+    "\n",
+    "Phenotypic consitency of a group of perturbations reflects the average extent to which members of this group are more similar to each other compared to other groups (see Figure 1F).\n",
+    "\n",
+    "When computing phenotypic consistency, each perturbation’s replicate profiles are usually aggregated into a consensus profile by taking the median of each feature to reduce profile noise and improve computational efficiency."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_target</th>\n",
+       "      <th>Cells_AreaShape_Eccentricity</th>\n",
+       "      <th>Cells_AreaShape_Extent</th>\n",
+       "      <th>Cells_AreaShape_FormFactor</th>\n",
+       "      <th>Cells_AreaShape_Orientation</th>\n",
+       "      <th>Cells_AreaShape_Solidity</th>\n",
+       "      <th>Cells_AreaShape_Zernike_0_0</th>\n",
+       "      <th>Cells_AreaShape_Zernike_1_1</th>\n",
+       "      <th>Cells_AreaShape_Zernike_2_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BRD-A69636825-003-04-7</td>\n",
+       "      <td>[CACNA1C, CACNA1S, CACNA2D1, CACNG1, HTR3A, KC...</td>\n",
+       "      <td>-0.326365</td>\n",
+       "      <td>0.651610</td>\n",
+       "      <td>0.211280</td>\n",
+       "      <td>0.092412</td>\n",
+       "      <td>0.456915</td>\n",
+       "      <td>0.486515</td>\n",
+       "      <td>0.435545</td>\n",
+       "      <td>0.863160</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.175200</td>\n",
+       "      <td>0.557360</td>\n",
+       "      <td>-0.859465</td>\n",
+       "      <td>0.409045</td>\n",
+       "      <td>0.201909</td>\n",
+       "      <td>-1.003185</td>\n",
+       "      <td>-1.405850</td>\n",
+       "      <td>-1.495100</td>\n",
+       "      <td>-0.867225</td>\n",
+       "      <td>-0.066115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BRD-A69815203-001-07-6</td>\n",
+       "      <td>[ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...</td>\n",
+       "      <td>2.487450</td>\n",
+       "      <td>-2.872750</td>\n",
+       "      <td>0.616635</td>\n",
+       "      <td>-0.451942</td>\n",
+       "      <td>-2.260100</td>\n",
+       "      <td>-3.300900</td>\n",
+       "      <td>0.316320</td>\n",
+       "      <td>-1.825400</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.681800</td>\n",
+       "      <td>-0.197230</td>\n",
+       "      <td>-4.717350</td>\n",
+       "      <td>0.644170</td>\n",
+       "      <td>1.324100</td>\n",
+       "      <td>0.103070</td>\n",
+       "      <td>0.986025</td>\n",
+       "      <td>1.346200</td>\n",
+       "      <td>0.773450</td>\n",
+       "      <td>-2.749350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BRD-A70858459-001-01-7</td>\n",
+       "      <td>[ESR1, ESR2, MAP1A, MAP2]</td>\n",
+       "      <td>-0.920210</td>\n",
+       "      <td>1.461550</td>\n",
+       "      <td>0.445630</td>\n",
+       "      <td>-0.394235</td>\n",
+       "      <td>1.528450</td>\n",
+       "      <td>1.116100</td>\n",
+       "      <td>-0.054990</td>\n",
+       "      <td>1.061270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.238875</td>\n",
+       "      <td>0.326475</td>\n",
+       "      <td>0.064563</td>\n",
+       "      <td>0.187646</td>\n",
+       "      <td>0.200447</td>\n",
+       "      <td>-0.695660</td>\n",
+       "      <td>0.100225</td>\n",
+       "      <td>0.401885</td>\n",
+       "      <td>0.114583</td>\n",
+       "      <td>-0.245753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>[HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...</td>\n",
+       "      <td>0.045435</td>\n",
+       "      <td>0.099755</td>\n",
+       "      <td>0.103628</td>\n",
+       "      <td>0.592620</td>\n",
+       "      <td>-0.352200</td>\n",
+       "      <td>0.202930</td>\n",
+       "      <td>-0.059855</td>\n",
+       "      <td>-0.353755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.069575</td>\n",
+       "      <td>-0.475915</td>\n",
+       "      <td>-0.174002</td>\n",
+       "      <td>0.217965</td>\n",
+       "      <td>0.090715</td>\n",
+       "      <td>-0.154695</td>\n",
+       "      <td>0.165235</td>\n",
+       "      <td>-0.160191</td>\n",
+       "      <td>0.242195</td>\n",
+       "      <td>-0.126886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BRD-A73368467-003-17-6</td>\n",
+       "      <td>[HRH1]</td>\n",
+       "      <td>-0.062074</td>\n",
+       "      <td>-0.314820</td>\n",
+       "      <td>0.526190</td>\n",
+       "      <td>-0.502485</td>\n",
+       "      <td>-0.444675</td>\n",
+       "      <td>-0.191225</td>\n",
+       "      <td>0.145019</td>\n",
+       "      <td>0.018870</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.527805</td>\n",
+       "      <td>-1.204250</td>\n",
+       "      <td>0.615420</td>\n",
+       "      <td>-0.187645</td>\n",
+       "      <td>0.321880</td>\n",
+       "      <td>1.013235</td>\n",
+       "      <td>0.793675</td>\n",
+       "      <td>0.682925</td>\n",
+       "      <td>1.075500</td>\n",
+       "      <td>0.844115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BRD-A81233518-004-16-1</td>\n",
+       "      <td>[CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]</td>\n",
+       "      <td>-0.612415</td>\n",
+       "      <td>-0.128128</td>\n",
+       "      <td>-0.741865</td>\n",
+       "      <td>0.178005</td>\n",
+       "      <td>0.006800</td>\n",
+       "      <td>0.282935</td>\n",
+       "      <td>0.587865</td>\n",
+       "      <td>0.561290</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.659600</td>\n",
+       "      <td>-0.702090</td>\n",
+       "      <td>-0.011905</td>\n",
+       "      <td>-0.000570</td>\n",
+       "      <td>0.066570</td>\n",
+       "      <td>0.015971</td>\n",
+       "      <td>0.257335</td>\n",
+       "      <td>0.140519</td>\n",
+       "      <td>0.471360</td>\n",
+       "      <td>-0.283820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BRD-A82035391-001-02-7</td>\n",
+       "      <td>[AVPR1A, AVPR2]</td>\n",
+       "      <td>-0.300770</td>\n",
+       "      <td>0.132704</td>\n",
+       "      <td>-0.619200</td>\n",
+       "      <td>0.053390</td>\n",
+       "      <td>-0.329085</td>\n",
+       "      <td>0.107972</td>\n",
+       "      <td>0.157675</td>\n",
+       "      <td>0.834860</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.682600</td>\n",
+       "      <td>0.840775</td>\n",
+       "      <td>0.287105</td>\n",
+       "      <td>-0.617285</td>\n",
+       "      <td>-0.596950</td>\n",
+       "      <td>0.194805</td>\n",
+       "      <td>-1.037480</td>\n",
+       "      <td>-0.871205</td>\n",
+       "      <td>-0.846345</td>\n",
+       "      <td>0.533585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>BRD-A82156122-001-01-9</td>\n",
+       "      <td>[DPP4]</td>\n",
+       "      <td>-0.229737</td>\n",
+       "      <td>-0.045755</td>\n",
+       "      <td>-1.031130</td>\n",
+       "      <td>0.078795</td>\n",
+       "      <td>-0.541225</td>\n",
+       "      <td>0.393505</td>\n",
+       "      <td>-0.494920</td>\n",
+       "      <td>0.976335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.154289</td>\n",
+       "      <td>0.216080</td>\n",
+       "      <td>0.636485</td>\n",
+       "      <td>0.088675</td>\n",
+       "      <td>-0.438930</td>\n",
+       "      <td>-0.251265</td>\n",
+       "      <td>-0.333184</td>\n",
+       "      <td>-0.601420</td>\n",
+       "      <td>-0.130211</td>\n",
+       "      <td>0.183651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>BRD-K50691590-001-02-2</td>\n",
+       "      <td>[PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSM...</td>\n",
+       "      <td>7.777100</td>\n",
+       "      <td>-2.558850</td>\n",
+       "      <td>-0.169902</td>\n",
+       "      <td>-0.768970</td>\n",
+       "      <td>6.395400</td>\n",
+       "      <td>-7.443450</td>\n",
+       "      <td>-7.568300</td>\n",
+       "      <td>-21.230000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.809700</td>\n",
+       "      <td>0.732385</td>\n",
+       "      <td>-7.893800</td>\n",
+       "      <td>-6.190550</td>\n",
+       "      <td>-1.162450</td>\n",
+       "      <td>2.364450</td>\n",
+       "      <td>1.785100</td>\n",
+       "      <td>1.329300</td>\n",
+       "      <td>-1.494850</td>\n",
+       "      <td>1.846500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>BRD-K60230970-001-10-0</td>\n",
+       "      <td>[PSMB1]</td>\n",
+       "      <td>-4.508950</td>\n",
+       "      <td>10.006400</td>\n",
+       "      <td>2.551850</td>\n",
+       "      <td>-0.706140</td>\n",
+       "      <td>13.484500</td>\n",
+       "      <td>6.264200</td>\n",
+       "      <td>-5.839750</td>\n",
+       "      <td>-11.688000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.048700</td>\n",
+       "      <td>0.381675</td>\n",
+       "      <td>-3.348650</td>\n",
+       "      <td>-6.486350</td>\n",
+       "      <td>-0.308715</td>\n",
+       "      <td>3.929950</td>\n",
+       "      <td>1.430250</td>\n",
+       "      <td>3.869900</td>\n",
+       "      <td>-5.724050</td>\n",
+       "      <td>6.475100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>BRD-K67789209-001-01-0</td>\n",
+       "      <td>[HTR1A, HTR2A]</td>\n",
+       "      <td>0.052475</td>\n",
+       "      <td>-0.040268</td>\n",
+       "      <td>0.666435</td>\n",
+       "      <td>0.548785</td>\n",
+       "      <td>0.178140</td>\n",
+       "      <td>0.230900</td>\n",
+       "      <td>-0.015085</td>\n",
+       "      <td>-0.344318</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.077800</td>\n",
+       "      <td>-0.781525</td>\n",
+       "      <td>0.235365</td>\n",
+       "      <td>-0.045767</td>\n",
+       "      <td>0.177765</td>\n",
+       "      <td>0.637345</td>\n",
+       "      <td>0.130025</td>\n",
+       "      <td>-0.011241</td>\n",
+       "      <td>0.533865</td>\n",
+       "      <td>0.416050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>BRD-K67844266-003-01-9</td>\n",
+       "      <td>[NAE1, UBA3]</td>\n",
+       "      <td>0.077450</td>\n",
+       "      <td>3.715650</td>\n",
+       "      <td>-1.087885</td>\n",
+       "      <td>0.005565</td>\n",
+       "      <td>4.808450</td>\n",
+       "      <td>0.042300</td>\n",
+       "      <td>-0.246730</td>\n",
+       "      <td>-1.929100</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.102012</td>\n",
+       "      <td>1.663355</td>\n",
+       "      <td>-0.582000</td>\n",
+       "      <td>-0.398745</td>\n",
+       "      <td>1.070995</td>\n",
+       "      <td>1.386330</td>\n",
+       "      <td>1.302950</td>\n",
+       "      <td>2.515300</td>\n",
+       "      <td>0.468750</td>\n",
+       "      <td>-0.502195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>BRD-K68103045-001-02-9</td>\n",
+       "      <td>[GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6]</td>\n",
+       "      <td>0.245735</td>\n",
+       "      <td>-0.142770</td>\n",
+       "      <td>0.757980</td>\n",
+       "      <td>0.636800</td>\n",
+       "      <td>-0.116950</td>\n",
+       "      <td>-0.329765</td>\n",
+       "      <td>-0.183954</td>\n",
+       "      <td>-0.042453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.590850</td>\n",
+       "      <td>-0.164918</td>\n",
+       "      <td>0.120890</td>\n",
+       "      <td>-0.352405</td>\n",
+       "      <td>-0.109732</td>\n",
+       "      <td>0.364360</td>\n",
+       "      <td>0.170652</td>\n",
+       "      <td>-0.019672</td>\n",
+       "      <td>0.283860</td>\n",
+       "      <td>0.404025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>BRD-K68132782-003-13-8</td>\n",
+       "      <td>[SQLE]</td>\n",
+       "      <td>-0.115830</td>\n",
+       "      <td>-0.127212</td>\n",
+       "      <td>0.552555</td>\n",
+       "      <td>0.065825</td>\n",
+       "      <td>-0.123745</td>\n",
+       "      <td>-0.011705</td>\n",
+       "      <td>0.427760</td>\n",
+       "      <td>-0.108485</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.463495</td>\n",
+       "      <td>0.093568</td>\n",
+       "      <td>0.442330</td>\n",
+       "      <td>0.244282</td>\n",
+       "      <td>0.630595</td>\n",
+       "      <td>0.216720</td>\n",
+       "      <td>-0.148982</td>\n",
+       "      <td>-0.025293</td>\n",
+       "      <td>0.054689</td>\n",
+       "      <td>0.263120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>BRD-K68164687-001-01-6</td>\n",
+       "      <td>[TUBB]</td>\n",
+       "      <td>1.410420</td>\n",
+       "      <td>2.547000</td>\n",
+       "      <td>-2.946600</td>\n",
+       "      <td>-0.618420</td>\n",
+       "      <td>3.138550</td>\n",
+       "      <td>-0.396105</td>\n",
+       "      <td>1.149465</td>\n",
+       "      <td>-6.490200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-3.897050</td>\n",
+       "      <td>6.267000</td>\n",
+       "      <td>-6.635950</td>\n",
+       "      <td>-8.808600</td>\n",
+       "      <td>-8.821800</td>\n",
+       "      <td>-1.763250</td>\n",
+       "      <td>1.752600</td>\n",
+       "      <td>3.754700</td>\n",
+       "      <td>-1.052100</td>\n",
+       "      <td>-9.717150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>BRD-K68232413-001-01-2</td>\n",
+       "      <td>[GABRA1]</td>\n",
+       "      <td>0.126708</td>\n",
+       "      <td>0.166565</td>\n",
+       "      <td>0.583315</td>\n",
+       "      <td>-0.532045</td>\n",
+       "      <td>0.047595</td>\n",
+       "      <td>0.174960</td>\n",
+       "      <td>0.359635</td>\n",
+       "      <td>0.103767</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.428960</td>\n",
+       "      <td>-0.174345</td>\n",
+       "      <td>0.131420</td>\n",
+       "      <td>1.046350</td>\n",
+       "      <td>1.698700</td>\n",
+       "      <td>-0.022657</td>\n",
+       "      <td>-0.170655</td>\n",
+       "      <td>-0.407506</td>\n",
+       "      <td>0.049480</td>\n",
+       "      <td>0.468805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>BRD-K68488863-001-04-9</td>\n",
+       "      <td>[AURKA, FLT3, KDR, PDGFRA, PTK2, SRC]</td>\n",
+       "      <td>1.437900</td>\n",
+       "      <td>-0.565585</td>\n",
+       "      <td>-0.035150</td>\n",
+       "      <td>0.857750</td>\n",
+       "      <td>-0.824100</td>\n",
+       "      <td>-1.062155</td>\n",
+       "      <td>0.435550</td>\n",
+       "      <td>-2.471550</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.792025</td>\n",
+       "      <td>0.219445</td>\n",
+       "      <td>-0.027473</td>\n",
+       "      <td>-1.568100</td>\n",
+       "      <td>-1.196800</td>\n",
+       "      <td>1.167565</td>\n",
+       "      <td>1.191870</td>\n",
+       "      <td>1.725600</td>\n",
+       "      <td>0.708345</td>\n",
+       "      <td>0.004007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>BRD-K68532323-003-02-8</td>\n",
+       "      <td>[KIF11]</td>\n",
+       "      <td>-2.707550</td>\n",
+       "      <td>2.707100</td>\n",
+       "      <td>-8.441350</td>\n",
+       "      <td>0.735130</td>\n",
+       "      <td>2.538900</td>\n",
+       "      <td>2.048850</td>\n",
+       "      <td>1.948550</td>\n",
+       "      <td>-2.051800</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.277210</td>\n",
+       "      <td>6.326200</td>\n",
+       "      <td>1.095750</td>\n",
+       "      <td>-0.069796</td>\n",
+       "      <td>0.806905</td>\n",
+       "      <td>-0.931870</td>\n",
+       "      <td>-0.132730</td>\n",
+       "      <td>1.866050</td>\n",
+       "      <td>-1.291700</td>\n",
+       "      <td>-1.867900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>BRD-K68552125-001-05-3</td>\n",
+       "      <td>[KCNT2, TRPV4]</td>\n",
+       "      <td>-5.459300</td>\n",
+       "      <td>5.695200</td>\n",
+       "      <td>2.569400</td>\n",
+       "      <td>-1.958950</td>\n",
+       "      <td>7.423450</td>\n",
+       "      <td>3.947450</td>\n",
+       "      <td>0.014595</td>\n",
+       "      <td>-8.178600</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.330120</td>\n",
+       "      <td>-2.802300</td>\n",
+       "      <td>-2.065600</td>\n",
+       "      <td>-7.149400</td>\n",
+       "      <td>-3.275900</td>\n",
+       "      <td>6.976850</td>\n",
+       "      <td>4.534550</td>\n",
+       "      <td>4.842250</td>\n",
+       "      <td>0.854165</td>\n",
+       "      <td>-0.969015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>BRD-K68747584-001-02-0</td>\n",
+       "      <td>[AURKA, AURKB]</td>\n",
+       "      <td>1.629300</td>\n",
+       "      <td>-1.494500</td>\n",
+       "      <td>-7.914850</td>\n",
+       "      <td>-0.827525</td>\n",
+       "      <td>-3.036550</td>\n",
+       "      <td>-3.044650</td>\n",
+       "      <td>0.093925</td>\n",
+       "      <td>-0.221685</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.207195</td>\n",
+       "      <td>8.463450</td>\n",
+       "      <td>-0.705630</td>\n",
+       "      <td>-0.403320</td>\n",
+       "      <td>-0.767400</td>\n",
+       "      <td>-1.273770</td>\n",
+       "      <td>-0.658235</td>\n",
+       "      <td>0.702570</td>\n",
+       "      <td>-1.869850</td>\n",
+       "      <td>-3.957450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>BRD-K68938568-001-01-7</td>\n",
+       "      <td>[BIRC2, BIRC3, BIRC7, XIAP]</td>\n",
+       "      <td>1.021975</td>\n",
+       "      <td>-1.233680</td>\n",
+       "      <td>-0.055290</td>\n",
+       "      <td>0.094870</td>\n",
+       "      <td>-1.508100</td>\n",
+       "      <td>-1.625400</td>\n",
+       "      <td>-0.267168</td>\n",
+       "      <td>0.259418</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.692215</td>\n",
+       "      <td>0.584290</td>\n",
+       "      <td>1.226250</td>\n",
+       "      <td>0.588680</td>\n",
+       "      <td>1.099500</td>\n",
+       "      <td>0.114581</td>\n",
+       "      <td>0.032505</td>\n",
+       "      <td>0.292280</td>\n",
+       "      <td>0.046877</td>\n",
+       "      <td>0.720565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>BRD-K69236721-001-02-7</td>\n",
+       "      <td>[CFTR]</td>\n",
+       "      <td>-0.241255</td>\n",
+       "      <td>-0.167479</td>\n",
+       "      <td>0.922020</td>\n",
+       "      <td>-0.299425</td>\n",
+       "      <td>-0.087035</td>\n",
+       "      <td>0.070245</td>\n",
+       "      <td>-0.097815</td>\n",
+       "      <td>0.433940</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.522105</td>\n",
+       "      <td>-0.051832</td>\n",
+       "      <td>-0.408905</td>\n",
+       "      <td>0.229405</td>\n",
+       "      <td>0.558905</td>\n",
+       "      <td>-0.410785</td>\n",
+       "      <td>0.138148</td>\n",
+       "      <td>0.103984</td>\n",
+       "      <td>0.247400</td>\n",
+       "      <td>0.328564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>BRD-K70330367-003-07-9</td>\n",
+       "      <td>[DRD2, GRIN2A, GRIN2B, GRIN2C, GRIN2D, GRIN3A]</td>\n",
+       "      <td>0.128625</td>\n",
+       "      <td>0.322145</td>\n",
+       "      <td>0.462110</td>\n",
+       "      <td>0.237160</td>\n",
+       "      <td>0.002715</td>\n",
+       "      <td>0.374645</td>\n",
+       "      <td>0.824375</td>\n",
+       "      <td>-0.490540</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.438780</td>\n",
+       "      <td>-0.304260</td>\n",
+       "      <td>-0.066394</td>\n",
+       "      <td>-0.653895</td>\n",
+       "      <td>-0.862500</td>\n",
+       "      <td>0.080967</td>\n",
+       "      <td>-0.281715</td>\n",
+       "      <td>-0.356915</td>\n",
+       "      <td>0.010415</td>\n",
+       "      <td>0.452110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>BRD-K70358946-001-15-7</td>\n",
+       "      <td>[ADRA1A, ADRA1B, ADRA2A, ADRA2B, ADRA2C, CHRM1...</td>\n",
+       "      <td>0.020480</td>\n",
+       "      <td>0.280960</td>\n",
+       "      <td>0.201028</td>\n",
+       "      <td>-0.177224</td>\n",
+       "      <td>0.141425</td>\n",
+       "      <td>0.452695</td>\n",
+       "      <td>-0.296370</td>\n",
+       "      <td>0.669775</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.226835</td>\n",
+       "      <td>-0.296180</td>\n",
+       "      <td>-1.044930</td>\n",
+       "      <td>-1.262620</td>\n",
+       "      <td>-1.232685</td>\n",
+       "      <td>-0.900705</td>\n",
+       "      <td>-0.230250</td>\n",
+       "      <td>-0.480575</td>\n",
+       "      <td>0.164065</td>\n",
+       "      <td>-0.074795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>BRD-K70401845-003-09-6</td>\n",
+       "      <td>[EGFR, NR1I2]</td>\n",
+       "      <td>2.164250</td>\n",
+       "      <td>-1.853250</td>\n",
+       "      <td>-1.967100</td>\n",
+       "      <td>0.307925</td>\n",
+       "      <td>-1.830400</td>\n",
+       "      <td>-2.712300</td>\n",
+       "      <td>-0.008760</td>\n",
+       "      <td>-0.084900</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.208145</td>\n",
+       "      <td>3.868550</td>\n",
+       "      <td>-0.753250</td>\n",
+       "      <td>0.855255</td>\n",
+       "      <td>-0.335050</td>\n",
+       "      <td>-1.362150</td>\n",
+       "      <td>-1.839250</td>\n",
+       "      <td>-1.343350</td>\n",
+       "      <td>-1.796950</td>\n",
+       "      <td>-4.749500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>BRD-K70463136-001-01-5</td>\n",
+       "      <td>[HIF1A]</td>\n",
+       "      <td>2.191800</td>\n",
+       "      <td>-1.505500</td>\n",
+       "      <td>-3.346450</td>\n",
+       "      <td>-0.483690</td>\n",
+       "      <td>-0.946445</td>\n",
+       "      <td>-2.778650</td>\n",
+       "      <td>0.129450</td>\n",
+       "      <td>-0.047165</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.533510</td>\n",
+       "      <td>2.235500</td>\n",
+       "      <td>-1.789000</td>\n",
+       "      <td>0.236270</td>\n",
+       "      <td>0.108268</td>\n",
+       "      <td>-0.478755</td>\n",
+       "      <td>-0.652825</td>\n",
+       "      <td>-0.025295</td>\n",
+       "      <td>-0.716160</td>\n",
+       "      <td>-0.445430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>BRD-K70557564-305-03-6</td>\n",
+       "      <td>[ABCB1, ABCB4]</td>\n",
+       "      <td>-0.103667</td>\n",
+       "      <td>0.079621</td>\n",
+       "      <td>0.342375</td>\n",
+       "      <td>0.161715</td>\n",
+       "      <td>0.010880</td>\n",
+       "      <td>0.318060</td>\n",
+       "      <td>0.459880</td>\n",
+       "      <td>0.287720</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.262635</td>\n",
+       "      <td>-0.128570</td>\n",
+       "      <td>0.339765</td>\n",
+       "      <td>-0.322085</td>\n",
+       "      <td>-0.470385</td>\n",
+       "      <td>0.113840</td>\n",
+       "      <td>-0.029797</td>\n",
+       "      <td>-0.129276</td>\n",
+       "      <td>0.260420</td>\n",
+       "      <td>0.474815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>BRD-K70778732-003-26-7</td>\n",
+       "      <td>[ADRA1A, ADRA2A, HRH1, HTR1A, HTR2A, HTR2B, HT...</td>\n",
+       "      <td>0.144625</td>\n",
+       "      <td>-0.062230</td>\n",
+       "      <td>0.664970</td>\n",
+       "      <td>-0.153800</td>\n",
+       "      <td>0.008159</td>\n",
+       "      <td>-0.008455</td>\n",
+       "      <td>0.596140</td>\n",
+       "      <td>-0.245270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.053541</td>\n",
+       "      <td>0.160210</td>\n",
+       "      <td>-0.145157</td>\n",
+       "      <td>0.324945</td>\n",
+       "      <td>-0.047550</td>\n",
+       "      <td>-0.766600</td>\n",
+       "      <td>-0.688035</td>\n",
+       "      <td>-0.784095</td>\n",
+       "      <td>-0.291670</td>\n",
+       "      <td>-0.017363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>BRD-K70912147-001-01-8</td>\n",
+       "      <td>[SCN4A, SCN9A]</td>\n",
+       "      <td>0.235497</td>\n",
+       "      <td>0.034777</td>\n",
+       "      <td>0.591370</td>\n",
+       "      <td>-0.716795</td>\n",
+       "      <td>-0.023120</td>\n",
+       "      <td>-0.202931</td>\n",
+       "      <td>-0.252567</td>\n",
+       "      <td>0.457522</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.663715</td>\n",
+       "      <td>0.060585</td>\n",
+       "      <td>-0.345720</td>\n",
+       "      <td>0.625860</td>\n",
+       "      <td>0.620355</td>\n",
+       "      <td>-0.573280</td>\n",
+       "      <td>-0.720540</td>\n",
+       "      <td>-0.722265</td>\n",
+       "      <td>-0.257816</td>\n",
+       "      <td>-0.163612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>BRD-K70914287-300-02-8</td>\n",
+       "      <td>[EGFR, ERBB2]</td>\n",
+       "      <td>0.354524</td>\n",
+       "      <td>-0.275470</td>\n",
+       "      <td>0.235450</td>\n",
+       "      <td>0.692250</td>\n",
+       "      <td>-0.373965</td>\n",
+       "      <td>-0.206835</td>\n",
+       "      <td>-0.821455</td>\n",
+       "      <td>0.339605</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.547135</td>\n",
+       "      <td>0.158863</td>\n",
+       "      <td>-0.795380</td>\n",
+       "      <td>0.593255</td>\n",
+       "      <td>-0.090712</td>\n",
+       "      <td>-0.940430</td>\n",
+       "      <td>-0.449660</td>\n",
+       "      <td>-0.446850</td>\n",
+       "      <td>-0.153649</td>\n",
+       "      <td>-1.377700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>BRD-K71035033-001-07-1</td>\n",
+       "      <td>[FGFR3, KIT, PDGFRA, PDGFRB]</td>\n",
+       "      <td>-0.556740</td>\n",
+       "      <td>0.524400</td>\n",
+       "      <td>0.112045</td>\n",
+       "      <td>-0.086755</td>\n",
+       "      <td>0.361720</td>\n",
+       "      <td>0.433180</td>\n",
+       "      <td>-0.361091</td>\n",
+       "      <td>0.877305</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.455890</td>\n",
+       "      <td>-0.498130</td>\n",
+       "      <td>0.415775</td>\n",
+       "      <td>0.342680</td>\n",
+       "      <td>0.834700</td>\n",
+       "      <td>-0.012443</td>\n",
+       "      <td>0.260045</td>\n",
+       "      <td>0.278225</td>\n",
+       "      <td>0.434905</td>\n",
+       "      <td>0.356610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>BRD-K71221037-001-01-6</td>\n",
+       "      <td>[BIRC2, XIAP]</td>\n",
+       "      <td>1.180020</td>\n",
+       "      <td>-1.258350</td>\n",
+       "      <td>-0.187847</td>\n",
+       "      <td>0.321595</td>\n",
+       "      <td>-1.716150</td>\n",
+       "      <td>-1.286565</td>\n",
+       "      <td>-0.291015</td>\n",
+       "      <td>-0.103765</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.078725</td>\n",
+       "      <td>0.587655</td>\n",
+       "      <td>0.357165</td>\n",
+       "      <td>1.731700</td>\n",
+       "      <td>1.300700</td>\n",
+       "      <td>-1.007465</td>\n",
+       "      <td>-0.671785</td>\n",
+       "      <td>-0.601432</td>\n",
+       "      <td>-0.283855</td>\n",
+       "      <td>-0.337245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>BRD-K71289571-001-11-4</td>\n",
+       "      <td>[CYSLTR1, CYSLTR2]</td>\n",
+       "      <td>-0.080632</td>\n",
+       "      <td>0.217810</td>\n",
+       "      <td>0.450755</td>\n",
+       "      <td>0.038573</td>\n",
+       "      <td>-0.005439</td>\n",
+       "      <td>0.291390</td>\n",
+       "      <td>-0.121175</td>\n",
+       "      <td>0.377335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.136862</td>\n",
+       "      <td>-0.512265</td>\n",
+       "      <td>0.135083</td>\n",
+       "      <td>0.877580</td>\n",
+       "      <td>1.453550</td>\n",
+       "      <td>-0.203165</td>\n",
+       "      <td>0.493000</td>\n",
+       "      <td>0.407505</td>\n",
+       "      <td>0.513030</td>\n",
+       "      <td>0.204351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>BRD-K71480163-001-01-4</td>\n",
+       "      <td>[AKT1, AKT2, AKT3, PRKG1]</td>\n",
+       "      <td>-0.145263</td>\n",
+       "      <td>0.535385</td>\n",
+       "      <td>-0.187114</td>\n",
+       "      <td>-0.339755</td>\n",
+       "      <td>0.180860</td>\n",
+       "      <td>0.541150</td>\n",
+       "      <td>0.811725</td>\n",
+       "      <td>0.410355</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.005545</td>\n",
+       "      <td>0.634770</td>\n",
+       "      <td>0.648390</td>\n",
+       "      <td>0.172200</td>\n",
+       "      <td>-0.256040</td>\n",
+       "      <td>0.029160</td>\n",
+       "      <td>-1.086205</td>\n",
+       "      <td>-0.910565</td>\n",
+       "      <td>-0.752630</td>\n",
+       "      <td>-0.632420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>BRD-K72215350-001-06-5</td>\n",
+       "      <td>[GAST]</td>\n",
+       "      <td>0.471630</td>\n",
+       "      <td>-1.353545</td>\n",
+       "      <td>-0.619568</td>\n",
+       "      <td>0.232770</td>\n",
+       "      <td>-2.020750</td>\n",
+       "      <td>-1.169450</td>\n",
+       "      <td>-0.395645</td>\n",
+       "      <td>-0.174520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.353880</td>\n",
+       "      <td>-0.383021</td>\n",
+       "      <td>-1.919050</td>\n",
+       "      <td>0.422770</td>\n",
+       "      <td>-0.120706</td>\n",
+       "      <td>1.258530</td>\n",
+       "      <td>0.674490</td>\n",
+       "      <td>1.292800</td>\n",
+       "      <td>0.653655</td>\n",
+       "      <td>-2.583750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>BRD-K72222507-003-16-8</td>\n",
+       "      <td>[ACE]</td>\n",
+       "      <td>-0.090870</td>\n",
+       "      <td>-0.014643</td>\n",
+       "      <td>0.221170</td>\n",
+       "      <td>0.346739</td>\n",
+       "      <td>-0.024475</td>\n",
+       "      <td>0.139190</td>\n",
+       "      <td>-0.014600</td>\n",
+       "      <td>0.358470</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.542065</td>\n",
+       "      <td>-0.469180</td>\n",
+       "      <td>1.024750</td>\n",
+       "      <td>-0.181349</td>\n",
+       "      <td>0.000732</td>\n",
+       "      <td>0.653135</td>\n",
+       "      <td>0.029800</td>\n",
+       "      <td>-0.025294</td>\n",
+       "      <td>0.304690</td>\n",
+       "      <td>0.790020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>BRD-K72414522-001-06-7</td>\n",
+       "      <td>[KCNH2]</td>\n",
+       "      <td>-1.042440</td>\n",
+       "      <td>0.744960</td>\n",
+       "      <td>-0.714040</td>\n",
+       "      <td>-0.294695</td>\n",
+       "      <td>1.052550</td>\n",
+       "      <td>-0.049433</td>\n",
+       "      <td>1.171350</td>\n",
+       "      <td>0.047168</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.640910</td>\n",
+       "      <td>4.936850</td>\n",
+       "      <td>0.643810</td>\n",
+       "      <td>-1.311220</td>\n",
+       "      <td>-0.839090</td>\n",
+       "      <td>0.104182</td>\n",
+       "      <td>0.262755</td>\n",
+       "      <td>0.829060</td>\n",
+       "      <td>-0.838548</td>\n",
+       "      <td>-1.612110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>BRD-K73196317-003-14-8</td>\n",
+       "      <td>[HTR1A]</td>\n",
+       "      <td>-0.287335</td>\n",
+       "      <td>0.225135</td>\n",
+       "      <td>0.270234</td>\n",
+       "      <td>-0.457900</td>\n",
+       "      <td>0.082952</td>\n",
+       "      <td>0.383750</td>\n",
+       "      <td>0.416569</td>\n",
+       "      <td>0.216970</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.075717</td>\n",
+       "      <td>0.096258</td>\n",
+       "      <td>0.076012</td>\n",
+       "      <td>0.053776</td>\n",
+       "      <td>0.081934</td>\n",
+       "      <td>-0.248475</td>\n",
+       "      <td>-0.178780</td>\n",
+       "      <td>-0.258555</td>\n",
+       "      <td>0.101564</td>\n",
+       "      <td>0.207023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>BRD-K73237276-001-01-0</td>\n",
+       "      <td>[VDR]</td>\n",
+       "      <td>0.167024</td>\n",
+       "      <td>-0.848375</td>\n",
+       "      <td>0.039545</td>\n",
+       "      <td>0.095122</td>\n",
+       "      <td>-0.920620</td>\n",
+       "      <td>-0.673840</td>\n",
+       "      <td>0.304640</td>\n",
+       "      <td>0.306585</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.102961</td>\n",
+       "      <td>0.581595</td>\n",
+       "      <td>0.404330</td>\n",
+       "      <td>0.219112</td>\n",
+       "      <td>0.647420</td>\n",
+       "      <td>0.442170</td>\n",
+       "      <td>-0.411735</td>\n",
+       "      <td>-0.132086</td>\n",
+       "      <td>-0.138025</td>\n",
+       "      <td>0.498860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>BRD-K73319509-001-08-0</td>\n",
+       "      <td>[MET]</td>\n",
+       "      <td>-0.041595</td>\n",
+       "      <td>0.140935</td>\n",
+       "      <td>0.413410</td>\n",
+       "      <td>-0.599740</td>\n",
+       "      <td>0.054390</td>\n",
+       "      <td>0.263425</td>\n",
+       "      <td>-0.398075</td>\n",
+       "      <td>-0.150935</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.871865</td>\n",
+       "      <td>-0.438220</td>\n",
+       "      <td>0.197356</td>\n",
+       "      <td>0.149885</td>\n",
+       "      <td>-0.220930</td>\n",
+       "      <td>-0.269090</td>\n",
+       "      <td>-0.219415</td>\n",
+       "      <td>-0.250125</td>\n",
+       "      <td>0.174480</td>\n",
+       "      <td>0.223719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>[CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]</td>\n",
+       "      <td>0.026877</td>\n",
+       "      <td>-0.165650</td>\n",
+       "      <td>-0.181990</td>\n",
+       "      <td>-0.143430</td>\n",
+       "      <td>0.013598</td>\n",
+       "      <td>-0.078052</td>\n",
+       "      <td>-0.373260</td>\n",
+       "      <td>0.311305</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.518620</td>\n",
+       "      <td>-0.562750</td>\n",
+       "      <td>0.657090</td>\n",
+       "      <td>0.748865</td>\n",
+       "      <td>0.076081</td>\n",
+       "      <td>0.037513</td>\n",
+       "      <td>0.509250</td>\n",
+       "      <td>0.393450</td>\n",
+       "      <td>0.653660</td>\n",
+       "      <td>-0.168288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>BRD-K75958547-238-01-0</td>\n",
+       "      <td>[HMGCR]</td>\n",
+       "      <td>1.245290</td>\n",
+       "      <td>0.258995</td>\n",
+       "      <td>-0.000370</td>\n",
+       "      <td>-0.254469</td>\n",
+       "      <td>0.756080</td>\n",
+       "      <td>-0.427330</td>\n",
+       "      <td>-0.093925</td>\n",
+       "      <td>-0.094336</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.705540</td>\n",
+       "      <td>-0.976735</td>\n",
+       "      <td>-0.286188</td>\n",
+       "      <td>-0.855840</td>\n",
+       "      <td>-1.180700</td>\n",
+       "      <td>0.574950</td>\n",
+       "      <td>0.999570</td>\n",
+       "      <td>0.727890</td>\n",
+       "      <td>0.763035</td>\n",
+       "      <td>-0.487505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>BRD-K76908866-001-07-6</td>\n",
+       "      <td>[ERBB2]</td>\n",
+       "      <td>0.557385</td>\n",
+       "      <td>-0.543620</td>\n",
+       "      <td>-0.594665</td>\n",
+       "      <td>0.712370</td>\n",
+       "      <td>-1.068870</td>\n",
+       "      <td>-0.406515</td>\n",
+       "      <td>-0.352330</td>\n",
+       "      <td>-0.108482</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.502780</td>\n",
+       "      <td>0.071355</td>\n",
+       "      <td>0.265125</td>\n",
+       "      <td>0.139015</td>\n",
+       "      <td>-0.204103</td>\n",
+       "      <td>-0.033055</td>\n",
+       "      <td>-0.398195</td>\n",
+       "      <td>-0.199536</td>\n",
+       "      <td>-0.174484</td>\n",
+       "      <td>-0.556290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>BRD-K77908580-001-09-6</td>\n",
+       "      <td>[HDAC1, HDAC2, HDAC3, HDAC9]</td>\n",
+       "      <td>1.027085</td>\n",
+       "      <td>-0.005490</td>\n",
+       "      <td>-3.732000</td>\n",
+       "      <td>-0.393795</td>\n",
+       "      <td>0.006799</td>\n",
+       "      <td>-0.592535</td>\n",
+       "      <td>-0.100737</td>\n",
+       "      <td>0.136787</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.920500</td>\n",
+       "      <td>-1.007045</td>\n",
+       "      <td>-1.197430</td>\n",
+       "      <td>-0.853550</td>\n",
+       "      <td>-1.547250</td>\n",
+       "      <td>-0.178835</td>\n",
+       "      <td>1.934100</td>\n",
+       "      <td>1.894200</td>\n",
+       "      <td>1.617250</td>\n",
+       "      <td>-1.901300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>BRD-K79254416-001-22-6</td>\n",
+       "      <td>[DNMT1, DNMT3A]</td>\n",
+       "      <td>-1.382900</td>\n",
+       "      <td>1.577800</td>\n",
+       "      <td>-3.091250</td>\n",
+       "      <td>-0.195245</td>\n",
+       "      <td>0.494990</td>\n",
+       "      <td>1.462800</td>\n",
+       "      <td>-0.750890</td>\n",
+       "      <td>1.943300</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.280694</td>\n",
+       "      <td>1.288375</td>\n",
+       "      <td>0.900240</td>\n",
+       "      <td>1.921100</td>\n",
+       "      <td>1.718450</td>\n",
+       "      <td>-1.650930</td>\n",
+       "      <td>-0.316930</td>\n",
+       "      <td>-0.297900</td>\n",
+       "      <td>-0.499990</td>\n",
+       "      <td>-3.373800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>BRD-K80700417-001-04-2</td>\n",
+       "      <td>[FLT3, PIM1, PIM2, PIM3]</td>\n",
+       "      <td>-0.637375</td>\n",
+       "      <td>0.498775</td>\n",
+       "      <td>-0.002929</td>\n",
+       "      <td>0.106054</td>\n",
+       "      <td>0.087033</td>\n",
+       "      <td>0.398060</td>\n",
+       "      <td>0.169837</td>\n",
+       "      <td>0.382055</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.628235</td>\n",
+       "      <td>-0.427445</td>\n",
+       "      <td>-0.059985</td>\n",
+       "      <td>0.288905</td>\n",
+       "      <td>0.085592</td>\n",
+       "      <td>-0.503085</td>\n",
+       "      <td>-0.243790</td>\n",
+       "      <td>-0.553645</td>\n",
+       "      <td>-0.088544</td>\n",
+       "      <td>0.063442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>BRD-K81258678-001-01-0</td>\n",
+       "      <td>[RELA]</td>\n",
+       "      <td>-0.290530</td>\n",
+       "      <td>2.601900</td>\n",
+       "      <td>0.852815</td>\n",
+       "      <td>0.241650</td>\n",
+       "      <td>5.584950</td>\n",
+       "      <td>2.324650</td>\n",
+       "      <td>2.234700</td>\n",
+       "      <td>-4.466700</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.285650</td>\n",
+       "      <td>13.551000</td>\n",
+       "      <td>-1.326100</td>\n",
+       "      <td>4.612750</td>\n",
+       "      <td>5.419350</td>\n",
+       "      <td>5.724250</td>\n",
+       "      <td>-8.781900</td>\n",
+       "      <td>-5.519550</td>\n",
+       "      <td>-9.482150</td>\n",
+       "      <td>-11.058500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>BRD-K81957469-001-01-0</td>\n",
+       "      <td>[PSEN1]</td>\n",
+       "      <td>-1.073170</td>\n",
+       "      <td>1.045150</td>\n",
+       "      <td>0.311975</td>\n",
+       "      <td>-0.457700</td>\n",
+       "      <td>1.278300</td>\n",
+       "      <td>1.064075</td>\n",
+       "      <td>0.403425</td>\n",
+       "      <td>0.377335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.010772</td>\n",
+       "      <td>-0.308305</td>\n",
+       "      <td>-0.007785</td>\n",
+       "      <td>-0.863870</td>\n",
+       "      <td>-0.750570</td>\n",
+       "      <td>-0.602995</td>\n",
+       "      <td>0.371105</td>\n",
+       "      <td>0.559265</td>\n",
+       "      <td>0.471365</td>\n",
+       "      <td>0.064110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>BRD-K82135108-001-04-3</td>\n",
+       "      <td>[HSPA1A]</td>\n",
+       "      <td>0.803115</td>\n",
+       "      <td>0.329465</td>\n",
+       "      <td>-3.880350</td>\n",
+       "      <td>-0.744140</td>\n",
+       "      <td>0.645912</td>\n",
+       "      <td>-1.757450</td>\n",
+       "      <td>-0.694445</td>\n",
+       "      <td>-4.429020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-3.787800</td>\n",
+       "      <td>3.698950</td>\n",
+       "      <td>-4.905050</td>\n",
+       "      <td>0.290050</td>\n",
+       "      <td>0.425045</td>\n",
+       "      <td>0.344295</td>\n",
+       "      <td>-1.414005</td>\n",
+       "      <td>0.488985</td>\n",
+       "      <td>-3.562550</td>\n",
+       "      <td>-1.630150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>BRD-K82677201-001-01-6</td>\n",
+       "      <td>[ATP1A1]</td>\n",
+       "      <td>1.137800</td>\n",
+       "      <td>-0.619580</td>\n",
+       "      <td>-1.351550</td>\n",
+       "      <td>0.202985</td>\n",
+       "      <td>-0.658175</td>\n",
+       "      <td>-0.890450</td>\n",
+       "      <td>-0.177140</td>\n",
+       "      <td>0.646190</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.345960</td>\n",
+       "      <td>0.582945</td>\n",
+       "      <td>-0.146985</td>\n",
+       "      <td>-0.677920</td>\n",
+       "      <td>-1.008060</td>\n",
+       "      <td>0.059615</td>\n",
+       "      <td>-0.268170</td>\n",
+       "      <td>-0.033725</td>\n",
+       "      <td>-0.158858</td>\n",
+       "      <td>-0.046745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>[BCL2, BCL2L1, BCL2L2]</td>\n",
+       "      <td>0.154225</td>\n",
+       "      <td>-1.293200</td>\n",
+       "      <td>-2.563600</td>\n",
+       "      <td>-0.018505</td>\n",
+       "      <td>-1.728350</td>\n",
+       "      <td>-0.999075</td>\n",
+       "      <td>-0.328488</td>\n",
+       "      <td>0.127351</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-7.051600</td>\n",
+       "      <td>2.185700</td>\n",
+       "      <td>1.613200</td>\n",
+       "      <td>-1.670500</td>\n",
+       "      <td>-1.670150</td>\n",
+       "      <td>1.785400</td>\n",
+       "      <td>-3.656900</td>\n",
+       "      <td>-3.720950</td>\n",
+       "      <td>-3.130250</td>\n",
+       "      <td>1.099200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>51 rows × 495 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_broad_sample                                    Metadata_target  \\\n",
+       "1   BRD-A69636825-003-04-7  [CACNA1C, CACNA1S, CACNA2D1, CACNG1, HTR3A, KC...   \n",
+       "2   BRD-A69815203-001-07-6  [ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...   \n",
+       "3   BRD-A70858459-001-01-7                          [ESR1, ESR2, MAP1A, MAP2]   \n",
+       "4   BRD-A72309220-001-04-1  [HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...   \n",
+       "6   BRD-A73368467-003-17-6                                             [HRH1]   \n",
+       "8   BRD-A81233518-004-16-1                [CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]   \n",
+       "9   BRD-A82035391-001-02-7                                    [AVPR1A, AVPR2]   \n",
+       "10  BRD-A82156122-001-01-9                                             [DPP4]   \n",
+       "11  BRD-K50691590-001-02-2  [PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSM...   \n",
+       "12  BRD-K60230970-001-10-0                                            [PSMB1]   \n",
+       "13  BRD-K67789209-001-01-0                                     [HTR1A, HTR2A]   \n",
+       "14  BRD-K67844266-003-01-9                                       [NAE1, UBA3]   \n",
+       "15  BRD-K68103045-001-02-9   [GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6]   \n",
+       "16  BRD-K68132782-003-13-8                                             [SQLE]   \n",
+       "17  BRD-K68164687-001-01-6                                             [TUBB]   \n",
+       "18  BRD-K68232413-001-01-2                                           [GABRA1]   \n",
+       "19  BRD-K68488863-001-04-9              [AURKA, FLT3, KDR, PDGFRA, PTK2, SRC]   \n",
+       "20  BRD-K68532323-003-02-8                                            [KIF11]   \n",
+       "21  BRD-K68552125-001-05-3                                     [KCNT2, TRPV4]   \n",
+       "23  BRD-K68747584-001-02-0                                     [AURKA, AURKB]   \n",
+       "24  BRD-K68938568-001-01-7                        [BIRC2, BIRC3, BIRC7, XIAP]   \n",
+       "25  BRD-K69236721-001-02-7                                             [CFTR]   \n",
+       "27  BRD-K70330367-003-07-9     [DRD2, GRIN2A, GRIN2B, GRIN2C, GRIN2D, GRIN3A]   \n",
+       "28  BRD-K70358946-001-15-7  [ADRA1A, ADRA1B, ADRA2A, ADRA2B, ADRA2C, CHRM1...   \n",
+       "29  BRD-K70401845-003-09-6                                      [EGFR, NR1I2]   \n",
+       "30  BRD-K70463136-001-01-5                                            [HIF1A]   \n",
+       "31  BRD-K70557564-305-03-6                                     [ABCB1, ABCB4]   \n",
+       "32  BRD-K70778732-003-26-7  [ADRA1A, ADRA2A, HRH1, HTR1A, HTR2A, HTR2B, HT...   \n",
+       "33  BRD-K70912147-001-01-8                                     [SCN4A, SCN9A]   \n",
+       "34  BRD-K70914287-300-02-8                                      [EGFR, ERBB2]   \n",
+       "35  BRD-K71035033-001-07-1                       [FGFR3, KIT, PDGFRA, PDGFRB]   \n",
+       "36  BRD-K71221037-001-01-6                                      [BIRC2, XIAP]   \n",
+       "38  BRD-K71289571-001-11-4                                 [CYSLTR1, CYSLTR2]   \n",
+       "39  BRD-K71480163-001-01-4                          [AKT1, AKT2, AKT3, PRKG1]   \n",
+       "40  BRD-K72215350-001-06-5                                             [GAST]   \n",
+       "41  BRD-K72222507-003-16-8                                              [ACE]   \n",
+       "42  BRD-K72414522-001-06-7                                            [KCNH2]   \n",
+       "43  BRD-K73196317-003-14-8                                            [HTR1A]   \n",
+       "44  BRD-K73237276-001-01-0                                              [VDR]   \n",
+       "45  BRD-K73319509-001-08-0                                              [MET]   \n",
+       "46  BRD-K74363950-004-01-0                [CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]   \n",
+       "47  BRD-K75958547-238-01-0                                            [HMGCR]   \n",
+       "48  BRD-K76908866-001-07-6                                            [ERBB2]   \n",
+       "49  BRD-K77908580-001-09-6                       [HDAC1, HDAC2, HDAC3, HDAC9]   \n",
+       "50  BRD-K79254416-001-22-6                                    [DNMT1, DNMT3A]   \n",
+       "51  BRD-K80700417-001-04-2                           [FLT3, PIM1, PIM2, PIM3]   \n",
+       "53  BRD-K81258678-001-01-0                                             [RELA]   \n",
+       "54  BRD-K81957469-001-01-0                                            [PSEN1]   \n",
+       "55  BRD-K82135108-001-04-3                                           [HSPA1A]   \n",
+       "56  BRD-K82677201-001-01-6                                           [ATP1A1]   \n",
+       "57  BRD-K82746043-001-15-1                             [BCL2, BCL2L1, BCL2L2]   \n",
+       "\n",
+       "    Cells_AreaShape_Eccentricity  Cells_AreaShape_Extent  \\\n",
+       "1                      -0.326365                0.651610   \n",
+       "2                       2.487450               -2.872750   \n",
+       "3                      -0.920210                1.461550   \n",
+       "4                       0.045435                0.099755   \n",
+       "6                      -0.062074               -0.314820   \n",
+       "8                      -0.612415               -0.128128   \n",
+       "9                      -0.300770                0.132704   \n",
+       "10                     -0.229737               -0.045755   \n",
+       "11                      7.777100               -2.558850   \n",
+       "12                     -4.508950               10.006400   \n",
+       "13                      0.052475               -0.040268   \n",
+       "14                      0.077450                3.715650   \n",
+       "15                      0.245735               -0.142770   \n",
+       "16                     -0.115830               -0.127212   \n",
+       "17                      1.410420                2.547000   \n",
+       "18                      0.126708                0.166565   \n",
+       "19                      1.437900               -0.565585   \n",
+       "20                     -2.707550                2.707100   \n",
+       "21                     -5.459300                5.695200   \n",
+       "23                      1.629300               -1.494500   \n",
+       "24                      1.021975               -1.233680   \n",
+       "25                     -0.241255               -0.167479   \n",
+       "27                      0.128625                0.322145   \n",
+       "28                      0.020480                0.280960   \n",
+       "29                      2.164250               -1.853250   \n",
+       "30                      2.191800               -1.505500   \n",
+       "31                     -0.103667                0.079621   \n",
+       "32                      0.144625               -0.062230   \n",
+       "33                      0.235497                0.034777   \n",
+       "34                      0.354524               -0.275470   \n",
+       "35                     -0.556740                0.524400   \n",
+       "36                      1.180020               -1.258350   \n",
+       "38                     -0.080632                0.217810   \n",
+       "39                     -0.145263                0.535385   \n",
+       "40                      0.471630               -1.353545   \n",
+       "41                     -0.090870               -0.014643   \n",
+       "42                     -1.042440                0.744960   \n",
+       "43                     -0.287335                0.225135   \n",
+       "44                      0.167024               -0.848375   \n",
+       "45                     -0.041595                0.140935   \n",
+       "46                      0.026877               -0.165650   \n",
+       "47                      1.245290                0.258995   \n",
+       "48                      0.557385               -0.543620   \n",
+       "49                      1.027085               -0.005490   \n",
+       "50                     -1.382900                1.577800   \n",
+       "51                     -0.637375                0.498775   \n",
+       "53                     -0.290530                2.601900   \n",
+       "54                     -1.073170                1.045150   \n",
+       "55                      0.803115                0.329465   \n",
+       "56                      1.137800               -0.619580   \n",
+       "57                      0.154225               -1.293200   \n",
+       "\n",
+       "    Cells_AreaShape_FormFactor  Cells_AreaShape_Orientation  \\\n",
+       "1                     0.211280                     0.092412   \n",
+       "2                     0.616635                    -0.451942   \n",
+       "3                     0.445630                    -0.394235   \n",
+       "4                     0.103628                     0.592620   \n",
+       "6                     0.526190                    -0.502485   \n",
+       "8                    -0.741865                     0.178005   \n",
+       "9                    -0.619200                     0.053390   \n",
+       "10                   -1.031130                     0.078795   \n",
+       "11                   -0.169902                    -0.768970   \n",
+       "12                    2.551850                    -0.706140   \n",
+       "13                    0.666435                     0.548785   \n",
+       "14                   -1.087885                     0.005565   \n",
+       "15                    0.757980                     0.636800   \n",
+       "16                    0.552555                     0.065825   \n",
+       "17                   -2.946600                    -0.618420   \n",
+       "18                    0.583315                    -0.532045   \n",
+       "19                   -0.035150                     0.857750   \n",
+       "20                   -8.441350                     0.735130   \n",
+       "21                    2.569400                    -1.958950   \n",
+       "23                   -7.914850                    -0.827525   \n",
+       "24                   -0.055290                     0.094870   \n",
+       "25                    0.922020                    -0.299425   \n",
+       "27                    0.462110                     0.237160   \n",
+       "28                    0.201028                    -0.177224   \n",
+       "29                   -1.967100                     0.307925   \n",
+       "30                   -3.346450                    -0.483690   \n",
+       "31                    0.342375                     0.161715   \n",
+       "32                    0.664970                    -0.153800   \n",
+       "33                    0.591370                    -0.716795   \n",
+       "34                    0.235450                     0.692250   \n",
+       "35                    0.112045                    -0.086755   \n",
+       "36                   -0.187847                     0.321595   \n",
+       "38                    0.450755                     0.038573   \n",
+       "39                   -0.187114                    -0.339755   \n",
+       "40                   -0.619568                     0.232770   \n",
+       "41                    0.221170                     0.346739   \n",
+       "42                   -0.714040                    -0.294695   \n",
+       "43                    0.270234                    -0.457900   \n",
+       "44                    0.039545                     0.095122   \n",
+       "45                    0.413410                    -0.599740   \n",
+       "46                   -0.181990                    -0.143430   \n",
+       "47                   -0.000370                    -0.254469   \n",
+       "48                   -0.594665                     0.712370   \n",
+       "49                   -3.732000                    -0.393795   \n",
+       "50                   -3.091250                    -0.195245   \n",
+       "51                   -0.002929                     0.106054   \n",
+       "53                    0.852815                     0.241650   \n",
+       "54                    0.311975                    -0.457700   \n",
+       "55                   -3.880350                    -0.744140   \n",
+       "56                   -1.351550                     0.202985   \n",
+       "57                   -2.563600                    -0.018505   \n",
+       "\n",
+       "    Cells_AreaShape_Solidity  Cells_AreaShape_Zernike_0_0  \\\n",
+       "1                   0.456915                     0.486515   \n",
+       "2                  -2.260100                    -3.300900   \n",
+       "3                   1.528450                     1.116100   \n",
+       "4                  -0.352200                     0.202930   \n",
+       "6                  -0.444675                    -0.191225   \n",
+       "8                   0.006800                     0.282935   \n",
+       "9                  -0.329085                     0.107972   \n",
+       "10                 -0.541225                     0.393505   \n",
+       "11                  6.395400                    -7.443450   \n",
+       "12                 13.484500                     6.264200   \n",
+       "13                  0.178140                     0.230900   \n",
+       "14                  4.808450                     0.042300   \n",
+       "15                 -0.116950                    -0.329765   \n",
+       "16                 -0.123745                    -0.011705   \n",
+       "17                  3.138550                    -0.396105   \n",
+       "18                  0.047595                     0.174960   \n",
+       "19                 -0.824100                    -1.062155   \n",
+       "20                  2.538900                     2.048850   \n",
+       "21                  7.423450                     3.947450   \n",
+       "23                 -3.036550                    -3.044650   \n",
+       "24                 -1.508100                    -1.625400   \n",
+       "25                 -0.087035                     0.070245   \n",
+       "27                  0.002715                     0.374645   \n",
+       "28                  0.141425                     0.452695   \n",
+       "29                 -1.830400                    -2.712300   \n",
+       "30                 -0.946445                    -2.778650   \n",
+       "31                  0.010880                     0.318060   \n",
+       "32                  0.008159                    -0.008455   \n",
+       "33                 -0.023120                    -0.202931   \n",
+       "34                 -0.373965                    -0.206835   \n",
+       "35                  0.361720                     0.433180   \n",
+       "36                 -1.716150                    -1.286565   \n",
+       "38                 -0.005439                     0.291390   \n",
+       "39                  0.180860                     0.541150   \n",
+       "40                 -2.020750                    -1.169450   \n",
+       "41                 -0.024475                     0.139190   \n",
+       "42                  1.052550                    -0.049433   \n",
+       "43                  0.082952                     0.383750   \n",
+       "44                 -0.920620                    -0.673840   \n",
+       "45                  0.054390                     0.263425   \n",
+       "46                  0.013598                    -0.078052   \n",
+       "47                  0.756080                    -0.427330   \n",
+       "48                 -1.068870                    -0.406515   \n",
+       "49                  0.006799                    -0.592535   \n",
+       "50                  0.494990                     1.462800   \n",
+       "51                  0.087033                     0.398060   \n",
+       "53                  5.584950                     2.324650   \n",
+       "54                  1.278300                     1.064075   \n",
+       "55                  0.645912                    -1.757450   \n",
+       "56                 -0.658175                    -0.890450   \n",
+       "57                 -1.728350                    -0.999075   \n",
+       "\n",
+       "    Cells_AreaShape_Zernike_1_1  Cells_AreaShape_Zernike_2_0  ...  \\\n",
+       "1                      0.435545                     0.863160  ...   \n",
+       "2                      0.316320                    -1.825400  ...   \n",
+       "3                     -0.054990                     1.061270  ...   \n",
+       "4                     -0.059855                    -0.353755  ...   \n",
+       "6                      0.145019                     0.018870  ...   \n",
+       "8                      0.587865                     0.561290  ...   \n",
+       "9                      0.157675                     0.834860  ...   \n",
+       "10                    -0.494920                     0.976335  ...   \n",
+       "11                    -7.568300                   -21.230000  ...   \n",
+       "12                    -5.839750                   -11.688000  ...   \n",
+       "13                    -0.015085                    -0.344318  ...   \n",
+       "14                    -0.246730                    -1.929100  ...   \n",
+       "15                    -0.183954                    -0.042453  ...   \n",
+       "16                     0.427760                    -0.108485  ...   \n",
+       "17                     1.149465                    -6.490200  ...   \n",
+       "18                     0.359635                     0.103767  ...   \n",
+       "19                     0.435550                    -2.471550  ...   \n",
+       "20                     1.948550                    -2.051800  ...   \n",
+       "21                     0.014595                    -8.178600  ...   \n",
+       "23                     0.093925                    -0.221685  ...   \n",
+       "24                    -0.267168                     0.259418  ...   \n",
+       "25                    -0.097815                     0.433940  ...   \n",
+       "27                     0.824375                    -0.490540  ...   \n",
+       "28                    -0.296370                     0.669775  ...   \n",
+       "29                    -0.008760                    -0.084900  ...   \n",
+       "30                     0.129450                    -0.047165  ...   \n",
+       "31                     0.459880                     0.287720  ...   \n",
+       "32                     0.596140                    -0.245270  ...   \n",
+       "33                    -0.252567                     0.457522  ...   \n",
+       "34                    -0.821455                     0.339605  ...   \n",
+       "35                    -0.361091                     0.877305  ...   \n",
+       "36                    -0.291015                    -0.103765  ...   \n",
+       "38                    -0.121175                     0.377335  ...   \n",
+       "39                     0.811725                     0.410355  ...   \n",
+       "40                    -0.395645                    -0.174520  ...   \n",
+       "41                    -0.014600                     0.358470  ...   \n",
+       "42                     1.171350                     0.047168  ...   \n",
+       "43                     0.416569                     0.216970  ...   \n",
+       "44                     0.304640                     0.306585  ...   \n",
+       "45                    -0.398075                    -0.150935  ...   \n",
+       "46                    -0.373260                     0.311305  ...   \n",
+       "47                    -0.093925                    -0.094336  ...   \n",
+       "48                    -0.352330                    -0.108482  ...   \n",
+       "49                    -0.100737                     0.136787  ...   \n",
+       "50                    -0.750890                     1.943300  ...   \n",
+       "51                     0.169837                     0.382055  ...   \n",
+       "53                     2.234700                    -4.466700  ...   \n",
+       "54                     0.403425                     0.377335  ...   \n",
+       "55                    -0.694445                    -4.429020  ...   \n",
+       "56                    -0.177140                     0.646190  ...   \n",
+       "57                    -0.328488                     0.127351  ...   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "1                                         0.175200   \n",
+       "2                                        -2.681800   \n",
+       "3                                         0.238875   \n",
+       "4                                         1.069575   \n",
+       "6                                         0.527805   \n",
+       "8                                         0.659600   \n",
+       "9                                        -1.682600   \n",
+       "10                                        0.154289   \n",
+       "11                                       -5.809700   \n",
+       "12                                       -5.048700   \n",
+       "13                                        1.077800   \n",
+       "14                                        0.102012   \n",
+       "15                                       -0.590850   \n",
+       "16                                       -0.463495   \n",
+       "17                                       -3.897050   \n",
+       "18                                        0.428960   \n",
+       "19                                        0.792025   \n",
+       "20                                       -0.277210   \n",
+       "21                                       -0.330120   \n",
+       "23                                        0.207195   \n",
+       "24                                       -0.692215   \n",
+       "25                                       -0.522105   \n",
+       "27                                        0.438780   \n",
+       "28                                        0.226835   \n",
+       "29                                       -0.208145   \n",
+       "30                                       -0.533510   \n",
+       "31                                       -0.262635   \n",
+       "32                                       -0.053541   \n",
+       "33                                        0.663715   \n",
+       "34                                        0.547135   \n",
+       "35                                        0.455890   \n",
+       "36                                        1.078725   \n",
+       "38                                        0.136862   \n",
+       "39                                        1.005545   \n",
+       "40                                        0.353880   \n",
+       "41                                        0.542065   \n",
+       "42                                        0.640910   \n",
+       "43                                       -0.075717   \n",
+       "44                                        0.102961   \n",
+       "45                                        0.871865   \n",
+       "46                                       -0.518620   \n",
+       "47                                       -0.705540   \n",
+       "48                                        0.502780   \n",
+       "49                                       -1.920500   \n",
+       "50                                        0.280694   \n",
+       "51                                        0.628235   \n",
+       "53                                       -5.285650   \n",
+       "54                                       -0.010772   \n",
+       "55                                       -3.787800   \n",
+       "56                                        0.345960   \n",
+       "57                                       -7.051600   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "1                                          0.557360   \n",
+       "2                                         -0.197230   \n",
+       "3                                          0.326475   \n",
+       "4                                         -0.475915   \n",
+       "6                                         -1.204250   \n",
+       "8                                         -0.702090   \n",
+       "9                                          0.840775   \n",
+       "10                                         0.216080   \n",
+       "11                                         0.732385   \n",
+       "12                                         0.381675   \n",
+       "13                                        -0.781525   \n",
+       "14                                         1.663355   \n",
+       "15                                        -0.164918   \n",
+       "16                                         0.093568   \n",
+       "17                                         6.267000   \n",
+       "18                                        -0.174345   \n",
+       "19                                         0.219445   \n",
+       "20                                         6.326200   \n",
+       "21                                        -2.802300   \n",
+       "23                                         8.463450   \n",
+       "24                                         0.584290   \n",
+       "25                                        -0.051832   \n",
+       "27                                        -0.304260   \n",
+       "28                                        -0.296180   \n",
+       "29                                         3.868550   \n",
+       "30                                         2.235500   \n",
+       "31                                        -0.128570   \n",
+       "32                                         0.160210   \n",
+       "33                                         0.060585   \n",
+       "34                                         0.158863   \n",
+       "35                                        -0.498130   \n",
+       "36                                         0.587655   \n",
+       "38                                        -0.512265   \n",
+       "39                                         0.634770   \n",
+       "40                                        -0.383021   \n",
+       "41                                        -0.469180   \n",
+       "42                                         4.936850   \n",
+       "43                                         0.096258   \n",
+       "44                                         0.581595   \n",
+       "45                                        -0.438220   \n",
+       "46                                        -0.562750   \n",
+       "47                                        -0.976735   \n",
+       "48                                         0.071355   \n",
+       "49                                        -1.007045   \n",
+       "50                                         1.288375   \n",
+       "51                                        -0.427445   \n",
+       "53                                        13.551000   \n",
+       "54                                        -0.308305   \n",
+       "55                                         3.698950   \n",
+       "56                                         0.582945   \n",
+       "57                                         2.185700   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "1                                       -0.859465   \n",
+       "2                                       -4.717350   \n",
+       "3                                        0.064563   \n",
+       "4                                       -0.174002   \n",
+       "6                                        0.615420   \n",
+       "8                                       -0.011905   \n",
+       "9                                        0.287105   \n",
+       "10                                       0.636485   \n",
+       "11                                      -7.893800   \n",
+       "12                                      -3.348650   \n",
+       "13                                       0.235365   \n",
+       "14                                      -0.582000   \n",
+       "15                                       0.120890   \n",
+       "16                                       0.442330   \n",
+       "17                                      -6.635950   \n",
+       "18                                       0.131420   \n",
+       "19                                      -0.027473   \n",
+       "20                                       1.095750   \n",
+       "21                                      -2.065600   \n",
+       "23                                      -0.705630   \n",
+       "24                                       1.226250   \n",
+       "25                                      -0.408905   \n",
+       "27                                      -0.066394   \n",
+       "28                                      -1.044930   \n",
+       "29                                      -0.753250   \n",
+       "30                                      -1.789000   \n",
+       "31                                       0.339765   \n",
+       "32                                      -0.145157   \n",
+       "33                                      -0.345720   \n",
+       "34                                      -0.795380   \n",
+       "35                                       0.415775   \n",
+       "36                                       0.357165   \n",
+       "38                                       0.135083   \n",
+       "39                                       0.648390   \n",
+       "40                                      -1.919050   \n",
+       "41                                       1.024750   \n",
+       "42                                       0.643810   \n",
+       "43                                       0.076012   \n",
+       "44                                       0.404330   \n",
+       "45                                       0.197356   \n",
+       "46                                       0.657090   \n",
+       "47                                      -0.286188   \n",
+       "48                                       0.265125   \n",
+       "49                                      -1.197430   \n",
+       "50                                       0.900240   \n",
+       "51                                      -0.059985   \n",
+       "53                                      -1.326100   \n",
+       "54                                      -0.007785   \n",
+       "55                                      -4.905050   \n",
+       "56                                      -0.146985   \n",
+       "57                                       1.613200   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "1                                           0.409045   \n",
+       "2                                           0.644170   \n",
+       "3                                           0.187646   \n",
+       "4                                           0.217965   \n",
+       "6                                          -0.187645   \n",
+       "8                                          -0.000570   \n",
+       "9                                          -0.617285   \n",
+       "10                                          0.088675   \n",
+       "11                                         -6.190550   \n",
+       "12                                         -6.486350   \n",
+       "13                                         -0.045767   \n",
+       "14                                         -0.398745   \n",
+       "15                                         -0.352405   \n",
+       "16                                          0.244282   \n",
+       "17                                         -8.808600   \n",
+       "18                                          1.046350   \n",
+       "19                                         -1.568100   \n",
+       "20                                         -0.069796   \n",
+       "21                                         -7.149400   \n",
+       "23                                         -0.403320   \n",
+       "24                                          0.588680   \n",
+       "25                                          0.229405   \n",
+       "27                                         -0.653895   \n",
+       "28                                         -1.262620   \n",
+       "29                                          0.855255   \n",
+       "30                                          0.236270   \n",
+       "31                                         -0.322085   \n",
+       "32                                          0.324945   \n",
+       "33                                          0.625860   \n",
+       "34                                          0.593255   \n",
+       "35                                          0.342680   \n",
+       "36                                          1.731700   \n",
+       "38                                          0.877580   \n",
+       "39                                          0.172200   \n",
+       "40                                          0.422770   \n",
+       "41                                         -0.181349   \n",
+       "42                                         -1.311220   \n",
+       "43                                          0.053776   \n",
+       "44                                          0.219112   \n",
+       "45                                          0.149885   \n",
+       "46                                          0.748865   \n",
+       "47                                         -0.855840   \n",
+       "48                                          0.139015   \n",
+       "49                                         -0.853550   \n",
+       "50                                          1.921100   \n",
+       "51                                          0.288905   \n",
+       "53                                          4.612750   \n",
+       "54                                         -0.863870   \n",
+       "55                                          0.290050   \n",
+       "56                                         -0.677920   \n",
+       "57                                         -1.670500   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "1                                          0.201909   \n",
+       "2                                          1.324100   \n",
+       "3                                          0.200447   \n",
+       "4                                          0.090715   \n",
+       "6                                          0.321880   \n",
+       "8                                          0.066570   \n",
+       "9                                         -0.596950   \n",
+       "10                                        -0.438930   \n",
+       "11                                        -1.162450   \n",
+       "12                                        -0.308715   \n",
+       "13                                         0.177765   \n",
+       "14                                         1.070995   \n",
+       "15                                        -0.109732   \n",
+       "16                                         0.630595   \n",
+       "17                                        -8.821800   \n",
+       "18                                         1.698700   \n",
+       "19                                        -1.196800   \n",
+       "20                                         0.806905   \n",
+       "21                                        -3.275900   \n",
+       "23                                        -0.767400   \n",
+       "24                                         1.099500   \n",
+       "25                                         0.558905   \n",
+       "27                                        -0.862500   \n",
+       "28                                        -1.232685   \n",
+       "29                                        -0.335050   \n",
+       "30                                         0.108268   \n",
+       "31                                        -0.470385   \n",
+       "32                                        -0.047550   \n",
+       "33                                         0.620355   \n",
+       "34                                        -0.090712   \n",
+       "35                                         0.834700   \n",
+       "36                                         1.300700   \n",
+       "38                                         1.453550   \n",
+       "39                                        -0.256040   \n",
+       "40                                        -0.120706   \n",
+       "41                                         0.000732   \n",
+       "42                                        -0.839090   \n",
+       "43                                         0.081934   \n",
+       "44                                         0.647420   \n",
+       "45                                        -0.220930   \n",
+       "46                                         0.076081   \n",
+       "47                                        -1.180700   \n",
+       "48                                        -0.204103   \n",
+       "49                                        -1.547250   \n",
+       "50                                         1.718450   \n",
+       "51                                         0.085592   \n",
+       "53                                         5.419350   \n",
+       "54                                        -0.750570   \n",
+       "55                                         0.425045   \n",
+       "56                                        -1.008060   \n",
+       "57                                        -1.670150   \n",
+       "\n",
+       "    Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "1                           -1.003185                           -1.405850   \n",
+       "2                            0.103070                            0.986025   \n",
+       "3                           -0.695660                            0.100225   \n",
+       "4                           -0.154695                            0.165235   \n",
+       "6                            1.013235                            0.793675   \n",
+       "8                            0.015971                            0.257335   \n",
+       "9                            0.194805                           -1.037480   \n",
+       "10                          -0.251265                           -0.333184   \n",
+       "11                           2.364450                            1.785100   \n",
+       "12                           3.929950                            1.430250   \n",
+       "13                           0.637345                            0.130025   \n",
+       "14                           1.386330                            1.302950   \n",
+       "15                           0.364360                            0.170652   \n",
+       "16                           0.216720                           -0.148982   \n",
+       "17                          -1.763250                            1.752600   \n",
+       "18                          -0.022657                           -0.170655   \n",
+       "19                           1.167565                            1.191870   \n",
+       "20                          -0.931870                           -0.132730   \n",
+       "21                           6.976850                            4.534550   \n",
+       "23                          -1.273770                           -0.658235   \n",
+       "24                           0.114581                            0.032505   \n",
+       "25                          -0.410785                            0.138148   \n",
+       "27                           0.080967                           -0.281715   \n",
+       "28                          -0.900705                           -0.230250   \n",
+       "29                          -1.362150                           -1.839250   \n",
+       "30                          -0.478755                           -0.652825   \n",
+       "31                           0.113840                           -0.029797   \n",
+       "32                          -0.766600                           -0.688035   \n",
+       "33                          -0.573280                           -0.720540   \n",
+       "34                          -0.940430                           -0.449660   \n",
+       "35                          -0.012443                            0.260045   \n",
+       "36                          -1.007465                           -0.671785   \n",
+       "38                          -0.203165                            0.493000   \n",
+       "39                           0.029160                           -1.086205   \n",
+       "40                           1.258530                            0.674490   \n",
+       "41                           0.653135                            0.029800   \n",
+       "42                           0.104182                            0.262755   \n",
+       "43                          -0.248475                           -0.178780   \n",
+       "44                           0.442170                           -0.411735   \n",
+       "45                          -0.269090                           -0.219415   \n",
+       "46                           0.037513                            0.509250   \n",
+       "47                           0.574950                            0.999570   \n",
+       "48                          -0.033055                           -0.398195   \n",
+       "49                          -0.178835                            1.934100   \n",
+       "50                          -1.650930                           -0.316930   \n",
+       "51                          -0.503085                           -0.243790   \n",
+       "53                           5.724250                           -8.781900   \n",
+       "54                          -0.602995                            0.371105   \n",
+       "55                           0.344295                           -1.414005   \n",
+       "56                           0.059615                           -0.268170   \n",
+       "57                           1.785400                           -3.656900   \n",
+       "\n",
+       "    Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "1                            -1.495100                          -0.867225   \n",
+       "2                             1.346200                           0.773450   \n",
+       "3                             0.401885                           0.114583   \n",
+       "4                            -0.160191                           0.242195   \n",
+       "6                             0.682925                           1.075500   \n",
+       "8                             0.140519                           0.471360   \n",
+       "9                            -0.871205                          -0.846345   \n",
+       "10                           -0.601420                          -0.130211   \n",
+       "11                            1.329300                          -1.494850   \n",
+       "12                            3.869900                          -5.724050   \n",
+       "13                           -0.011241                           0.533865   \n",
+       "14                            2.515300                           0.468750   \n",
+       "15                           -0.019672                           0.283860   \n",
+       "16                           -0.025293                           0.054689   \n",
+       "17                            3.754700                          -1.052100   \n",
+       "18                           -0.407506                           0.049480   \n",
+       "19                            1.725600                           0.708345   \n",
+       "20                            1.866050                          -1.291700   \n",
+       "21                            4.842250                           0.854165   \n",
+       "23                            0.702570                          -1.869850   \n",
+       "24                            0.292280                           0.046877   \n",
+       "25                            0.103984                           0.247400   \n",
+       "27                           -0.356915                           0.010415   \n",
+       "28                           -0.480575                           0.164065   \n",
+       "29                           -1.343350                          -1.796950   \n",
+       "30                           -0.025295                          -0.716160   \n",
+       "31                           -0.129276                           0.260420   \n",
+       "32                           -0.784095                          -0.291670   \n",
+       "33                           -0.722265                          -0.257816   \n",
+       "34                           -0.446850                          -0.153649   \n",
+       "35                            0.278225                           0.434905   \n",
+       "36                           -0.601432                          -0.283855   \n",
+       "38                            0.407505                           0.513030   \n",
+       "39                           -0.910565                          -0.752630   \n",
+       "40                            1.292800                           0.653655   \n",
+       "41                           -0.025294                           0.304690   \n",
+       "42                            0.829060                          -0.838548   \n",
+       "43                           -0.258555                           0.101564   \n",
+       "44                           -0.132086                          -0.138025   \n",
+       "45                           -0.250125                           0.174480   \n",
+       "46                            0.393450                           0.653660   \n",
+       "47                            0.727890                           0.763035   \n",
+       "48                           -0.199536                          -0.174484   \n",
+       "49                            1.894200                           1.617250   \n",
+       "50                           -0.297900                          -0.499990   \n",
+       "51                           -0.553645                          -0.088544   \n",
+       "53                           -5.519550                          -9.482150   \n",
+       "54                            0.559265                           0.471365   \n",
+       "55                            0.488985                          -3.562550   \n",
+       "56                           -0.033725                          -0.158858   \n",
+       "57                           -3.720950                          -3.130250   \n",
+       "\n",
+       "    Nuclei_Texture_Variance_RNA_10_0  \n",
+       "1                          -0.066115  \n",
+       "2                          -2.749350  \n",
+       "3                          -0.245753  \n",
+       "4                          -0.126886  \n",
+       "6                           0.844115  \n",
+       "8                          -0.283820  \n",
+       "9                           0.533585  \n",
+       "10                          0.183651  \n",
+       "11                          1.846500  \n",
+       "12                          6.475100  \n",
+       "13                          0.416050  \n",
+       "14                         -0.502195  \n",
+       "15                          0.404025  \n",
+       "16                          0.263120  \n",
+       "17                         -9.717150  \n",
+       "18                          0.468805  \n",
+       "19                          0.004007  \n",
+       "20                         -1.867900  \n",
+       "21                         -0.969015  \n",
+       "23                         -3.957450  \n",
+       "24                          0.720565  \n",
+       "25                          0.328564  \n",
+       "27                          0.452110  \n",
+       "28                         -0.074795  \n",
+       "29                         -4.749500  \n",
+       "30                         -0.445430  \n",
+       "31                          0.474815  \n",
+       "32                         -0.017363  \n",
+       "33                         -0.163612  \n",
+       "34                         -1.377700  \n",
+       "35                          0.356610  \n",
+       "36                         -0.337245  \n",
+       "38                          0.204351  \n",
+       "39                         -0.632420  \n",
+       "40                         -2.583750  \n",
+       "41                          0.790020  \n",
+       "42                         -1.612110  \n",
+       "43                          0.207023  \n",
+       "44                          0.498860  \n",
+       "45                          0.223719  \n",
+       "46                         -0.168288  \n",
+       "47                         -0.487505  \n",
+       "48                         -0.556290  \n",
+       "49                         -1.901300  \n",
+       "50                         -3.373800  \n",
+       "51                          0.063442  \n",
+       "53                        -11.058500  \n",
+       "54                          0.064110  \n",
+       "55                         -1.630150  \n",
+       "56                         -0.046745  \n",
+       "57                          1.099200  \n",
+       "\n",
+       "[51 rows x 495 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# aggregate replicates per perturbation using sample id and target\n",
+    "df_agg = aggregate(df, strata=[\"Metadata_broad_sample\", \"Metadata_target\"])\n",
+    "df_agg = df_agg[df_agg[\"Metadata_target\"].isna() == False]\n",
+    "df_agg['Metadata_target'] = df_agg['Metadata_target'].str.split('|')\n",
+    "df_agg.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "641127b3e44e4bdd88162da28f03bade",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2af2dca7bb474bdeabfed5a4acda10d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>average_precision</th>\n",
+       "      <th>n_pos_pairs</th>\n",
+       "      <th>n_total_pairs</th>\n",
+       "      <th>Metadata_target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>BRD-A69636825-003-04-7</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>HTR3A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.380556</td>\n",
+       "      <td>4</td>\n",
+       "      <td>50</td>\n",
+       "      <td>HTR1A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>HTR1B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>HTR1D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>HTR1E</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.083882</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>CHRM4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.083882</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>CHRM5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>BRD-K76908866-001-07-6</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>ERBB2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>BRD-K80700417-001-04-2</td>\n",
+       "      <td>0.020833</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>FLT3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>BRD-K81258678-001-01-0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>RELA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>70 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_broad_sample  average_precision  n_pos_pairs  n_total_pairs  \\\n",
+       "56  BRD-A69636825-003-04-7           0.500000            1             50   \n",
+       "36  BRD-A72309220-001-04-1           0.380556            4             50   \n",
+       "41  BRD-A72309220-001-04-1           0.125000            1             47   \n",
+       "43  BRD-A72309220-001-04-1           0.125000            1             47   \n",
+       "45  BRD-A72309220-001-04-1           0.125000            1             47   \n",
+       "..                     ...                ...          ...            ...   \n",
+       "19  BRD-K74363950-004-01-0           0.083882            2             50   \n",
+       "22  BRD-K74363950-004-01-0           0.083882            2             50   \n",
+       "28  BRD-K76908866-001-07-6           0.500000            1             50   \n",
+       "30  BRD-K80700417-001-04-2           0.020833            1             50   \n",
+       "67  BRD-K81258678-001-01-0           0.100000            1             50   \n",
+       "\n",
+       "   Metadata_target  \n",
+       "56           HTR3A  \n",
+       "36           HTR1A  \n",
+       "41           HTR1B  \n",
+       "43           HTR1D  \n",
+       "45           HTR1E  \n",
+       "..             ...  \n",
+       "19           CHRM4  \n",
+       "22           CHRM5  \n",
+       "28           ERBB2  \n",
+       "30            FLT3  \n",
+       "67            RELA  \n",
+       "\n",
+       "[70 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# positive pairs are compounds that share a target\n",
+    "pos_sameby = [\"Metadata_target\"]\n",
+    "pos_diffby = []\n",
+    "\n",
+    "neg_sameby = []\n",
+    "# negative pairs are compounds that do not share a target\n",
+    "neg_diffby = [\"Metadata_target\"]\n",
+    "\n",
+    "metadata = df_agg.filter(regex=\"^Metadata\")\n",
+    "profiles = df_agg.filter(regex=\"^(?!Metadata)\").values\n",
+    "\n",
+    "ap_scores = map.multilabel.average_precision(\n",
+    "        metadata,\n",
+    "        profiles,\n",
+    "        pos_sameby=pos_sameby,\n",
+    "        pos_diffby=pos_diffby,\n",
+    "        neg_sameby=neg_sameby,\n",
+    "        neg_diffby=neg_diffby,\n",
+    "        multilabel_col='Metadata_target')\n",
+    "ap_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80e8224e98444cce976ad30694362c83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45defb90f221478a9c3a268e331157f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_target</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>p_value</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>below_p</th>\n",
+       "      <th>below_corrected_p</th>\n",
+       "      <th>-log10(p-value)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ADRA1A</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.092591</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ADRA2A</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.092591</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AURKA</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.001900</td>\n",
+       "      <td>0.011019</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.957862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BIRC2</td>\n",
+       "      <td>0.047727</td>\n",
+       "      <td>0.405359</td>\n",
+       "      <td>0.534337</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.272184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHRM1</td>\n",
+       "      <td>0.081192</td>\n",
+       "      <td>0.504350</td>\n",
+       "      <td>0.541709</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.266234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CHRM2</td>\n",
+       "      <td>0.081192</td>\n",
+       "      <td>0.504350</td>\n",
+       "      <td>0.541709</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.266234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CHRM3</td>\n",
+       "      <td>0.081192</td>\n",
+       "      <td>0.504350</td>\n",
+       "      <td>0.541709</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.266234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CHRM4</td>\n",
+       "      <td>0.081192</td>\n",
+       "      <td>0.504350</td>\n",
+       "      <td>0.541709</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.266234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>CHRM5</td>\n",
+       "      <td>0.081192</td>\n",
+       "      <td>0.504350</td>\n",
+       "      <td>0.541709</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.266234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>DRD2</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.000800</td>\n",
+       "      <td>0.005799</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.236615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.000700</td>\n",
+       "      <td>0.005799</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.236615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>ERBB2</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.037996</td>\n",
+       "      <td>0.157413</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.802960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>FLT3</td>\n",
+       "      <td>0.021528</td>\n",
+       "      <td>0.992301</td>\n",
+       "      <td>0.992301</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.003357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>GABRA1</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.078092</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>HRH1</td>\n",
+       "      <td>0.117663</td>\n",
+       "      <td>0.338666</td>\n",
+       "      <td>0.516911</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.286584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>HTR1A</td>\n",
+       "      <td>0.262612</td>\n",
+       "      <td>0.066193</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>HTR1B</td>\n",
+       "      <td>0.229167</td>\n",
+       "      <td>0.094991</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>HTR1D</td>\n",
+       "      <td>0.229167</td>\n",
+       "      <td>0.094991</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>HTR1E</td>\n",
+       "      <td>0.229167</td>\n",
+       "      <td>0.094991</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>HTR2A</td>\n",
+       "      <td>0.340241</td>\n",
+       "      <td>0.011099</td>\n",
+       "      <td>0.053645</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.270474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>HTR2B</td>\n",
+       "      <td>0.066964</td>\n",
+       "      <td>0.384462</td>\n",
+       "      <td>0.534337</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.272184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>HTR2C</td>\n",
+       "      <td>0.225362</td>\n",
+       "      <td>0.099690</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>HTR3A</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.000800</td>\n",
+       "      <td>0.005799</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.236615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>HTR6</td>\n",
+       "      <td>0.229167</td>\n",
+       "      <td>0.094991</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>HTR7</td>\n",
+       "      <td>0.229167</td>\n",
+       "      <td>0.094991</td>\n",
+       "      <td>0.170059</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>PDGFRA</td>\n",
+       "      <td>0.028571</td>\n",
+       "      <td>0.856014</td>\n",
+       "      <td>0.886586</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.052279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>PSMB1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.002900</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.537645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>RELA</td>\n",
+       "      <td>0.150000</td>\n",
+       "      <td>0.133387</td>\n",
+       "      <td>0.214901</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.667762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>XIAP</td>\n",
+       "      <td>0.047727</td>\n",
+       "      <td>0.405359</td>\n",
+       "      <td>0.534337</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.272184</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Metadata_target  mean_average_precision   p_value  corrected_p_value  \\\n",
+       "0           ADRA1A                0.238095  0.092591           0.170059   \n",
+       "1           ADRA2A                0.238095  0.092591           0.170059   \n",
+       "2            AURKA                0.625000  0.001900           0.011019   \n",
+       "3            BIRC2                0.047727  0.405359           0.534337   \n",
+       "4            CHRM1                0.081192  0.504350           0.541709   \n",
+       "5            CHRM2                0.081192  0.504350           0.541709   \n",
+       "6            CHRM3                0.081192  0.504350           0.541709   \n",
+       "7            CHRM4                0.081192  0.504350           0.541709   \n",
+       "8            CHRM5                0.081192  0.504350           0.541709   \n",
+       "9             DRD2                0.750000  0.000800           0.005799   \n",
+       "10            EGFR                0.750000  0.000700           0.005799   \n",
+       "11           ERBB2                0.500000  0.037996           0.157413   \n",
+       "12            FLT3                0.021528  0.992301           0.992301   \n",
+       "13          GABRA1                0.238095  0.078092           0.170059   \n",
+       "14            HRH1                0.117663  0.338666           0.516911   \n",
+       "15           HTR1A                0.262612  0.066193           0.170059   \n",
+       "16           HTR1B                0.229167  0.094991           0.170059   \n",
+       "17           HTR1D                0.229167  0.094991           0.170059   \n",
+       "18           HTR1E                0.229167  0.094991           0.170059   \n",
+       "19           HTR2A                0.340241  0.011099           0.053645   \n",
+       "20           HTR2B                0.066964  0.384462           0.534337   \n",
+       "21           HTR2C                0.225362  0.099690           0.170059   \n",
+       "22           HTR3A                0.750000  0.000800           0.005799   \n",
+       "23            HTR6                0.229167  0.094991           0.170059   \n",
+       "24            HTR7                0.229167  0.094991           0.170059   \n",
+       "25          PDGFRA                0.028571  0.856014           0.886586   \n",
+       "26           PSMB1                1.000000  0.000100           0.002900   \n",
+       "27            RELA                0.150000  0.133387           0.214901   \n",
+       "28            XIAP                0.047727  0.405359           0.534337   \n",
+       "\n",
+       "    below_p  below_corrected_p  -log10(p-value)  \n",
+       "0     False              False         0.769399  \n",
+       "1     False              False         0.769399  \n",
+       "2      True               True         1.957862  \n",
+       "3     False              False         0.272184  \n",
+       "4     False              False         0.266234  \n",
+       "5     False              False         0.266234  \n",
+       "6     False              False         0.266234  \n",
+       "7     False              False         0.266234  \n",
+       "8     False              False         0.266234  \n",
+       "9      True               True         2.236615  \n",
+       "10     True               True         2.236615  \n",
+       "11     True              False         0.802960  \n",
+       "12    False              False         0.003357  \n",
+       "13    False              False         0.769399  \n",
+       "14    False              False         0.286584  \n",
+       "15    False              False         0.769399  \n",
+       "16    False              False         0.769399  \n",
+       "17    False              False         0.769399  \n",
+       "18    False              False         0.769399  \n",
+       "19     True              False         1.270474  \n",
+       "20    False              False         0.272184  \n",
+       "21    False              False         0.769399  \n",
+       "22     True               True         2.236615  \n",
+       "23    False              False         0.769399  \n",
+       "24    False              False         0.769399  \n",
+       "25    False              False         0.052279  \n",
+       "26     True               True         2.537645  \n",
+       "27    False              False         0.667762  \n",
+       "28    False              False         0.272184  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map_scores = map.mean_average_precision(ap_scores, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
+    "map_scores[\"-log10(p-value)\"] = -map_scores[\"corrected_p_value\"].apply(np.log10)\n",
+    "map_scores.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kk09cljN//nxTpUoVk5aW5lz3hx9+aOLi4ozdbjctWrQwK1eudNbnvzeePn3aObZ69WrTvXt3ExQUZKpXr2569+5tTp06ZYwx5ssvvzRdu3Y1oaGhJjw83Nx4441mz549BV7v5s2bCyy/JD0XJi8vz7zwwgumYcOGJiAgwERHR5vnnnvO+fzPP/9sevbsaQIDA014eLgZPny4OXPmjPP5/J/zpEmTTGRkpAkPDzcjR4402dnZzppp06aZ2NhYY7fbTa1atcyf//xnl+3/wAMPXLR2yJAhBX5O+/fvN8YYs3XrVtO3b19TtWpVU6tWLTNo0CBz4sQJl3Xcf//95pFHHjFhYWEmIiLCjB8/3vl8TEyMy3JjYmIK3ValKf/3/2KOHz9u/P39zbvvvuvW8l988UXToEGDAuOPPvqoGTRoUIH1JycnG0nm7NmzzrqRI0caY4xZs2aN6dChg8nNzS10Xe7svz0KN0V57bXXzN13313i+sWLF5snn3zSfPbZZ26Fm127dpnExETnlJeXV+J1Em4Aayks3Nx8882mffv2xpjzb+7+/v4mPj7e/Pjjj2bjxo2mWbNmZuDAgc76999/30RFRZl58+aZffv2mXnz5pnw8HAze/ZsY8z/dvZNmzY1ixYtMrt27TK33XabiYmJMTk5OcYYYzZs2GB8fHzMhAkTzK5du8ysWbNMUFCQmTVrljHGmN9++83UrVvXTJgwwfneZYwxw4cPNzfccEOB/u+66y6XddetW9d8+umnZseOHWbYsGEmODjYnDx50hhTMNxs3rzZ2O12M2LECLNlyxazbds2M3XqVOeO+NNPPzXz5s0zu3fvNps3bzY33XSTadWqlfO9tLhwU5KeC/Poo4+asLAwM3v2bLNnzx7z3XffmZkzZxpjjElPTzdRUVHm1ltvNVu3bjXLli0zDRo0MEOGDHH5OYeEhJj77rvP7Ny50/znP/8xVapUMTNmzDDGGPPjjz8aX19fM2fOHHPgwAGzadMm869//cs5/4XhprjalJQUExcXZ4YPH+78OeXm5prTp0+bmjVrmnHjxpmdO3eaTZs2mV69epmePXu6rCMkJMQ888wz5tdffzXvvPOOsdls5uuvvzbGnA8QksysWbNMYmKiOX78eJHbq3nz5qZq1apFTn379i1y3guVNNxMnjzZhIaGmszMzBItN9+TTz5pOnTo4DKW//NLTU0tsH6Hw2GioqLMwoULTUZGhomLizPTp0832dnZpnXr1mbDhg1Frstr4Wbv3r0mODjYo3ndCTcX/nVyMefOnTOpqanO6fDhw4QbwEIuDDcOh8MsXbrU2O128/DDDxtjzr+5S3I5MjFt2jQTERHhfNywYUMzZ84cl+VOnDjRxMXFGWP+t7N/8803nc9v377dSDI7d+40xhgzcOBA06tXL5dlPPLII6Z58+bOxzExMeaVV15xqfnhhx+Mr6+v80hScnKy8fPzcx6ZyV/3888/75wnJyfH1K1b17zwwgvGmILvjQMGDDBdu3YtwdY778SJE0aS2bp1q8s6iwo3F+v599LS0ozdbneGmd+bMWOGCQsLM+np6c6xL774wvj4+JikpCRjzPmfc0xMjMtf9bfffrvp37+/McaYefPmmZCQkCKPHF0YbtypzTdx4kTTu3dvl7H8/cmuXbuc811zzTUuNZ06dTKPPfaY83FJ9nXGGHPgwAGze/fuIqcjR45cdBnGlDzcNGvWzIwYMaJEy8y3e/duExIS4gyYxhhz8uRJEx0dbVatWlXk+r/77jvTsWNHU79+fefRtwkTJpgHHnjAbNu2zXTp0sU0btzYTJ061WU+d8LNJZ9QfKFPP/20XD7XbNu2raKiotSrVy+tWbOm2NqEhASFhoY6p+jo6DLvD0D5WrRokapVq6bAwED94Q9/UP/+/fXMM884n69SpYoaNmzofBwVFeU8aTIjI0N79+7V0KFDVa1aNef03HPPae/evS7rad26tcsyJDmXs3PnTnXt2tWlvmvXrtq9e7fz+/cKc9VVV6lFixZ65513JEnvv/++YmJi1K1bN5e6uLg457/9/PzUsWNH7dy5s9BlbtmyRddff32R69y9e7cGDBigK6+8UiEhIapfv74k6dChQ0XO40nP+Xbu3KmsrKwie9q5c6fatGmjqlWrOse6du0qh8OhXbt2OcdatGjh8pU/F/4ce/XqpZiYGF155ZUaPHiwPvjgA2VmZha6Pndq8/30009asWKFy+9I06ZNJcnl9+TC35Hf9+iOmJgYxcbGFjnVqVPH7WUWZd26ddq5c6dbVzwfPXpUffv21e23367hw4c7x4cPH66BAwcW+bsgSddcc41+/PFH7d+/X9OmTdP+/fv17rvv6rnnntPgwYP117/+Vd99950mTJign3/+2aPX5FG4adeundq3b++c2rVrp6ioKD3xxBN64oknPGqkJKKiojR9+nTNmzdP8+bNU3R0tHr06KFNmzYVOc+4ceOUmprqnA4fPlxm/QHwjp49e2rLli3avXu3zp49q3feecdlR+nv7+9Sb7PZZIyRJKWnp0uSZs6cqS1btjinbdu26fvvv3eZ78Ll5F9U4XA4Lrn/YcOGafbs2ZKkWbNm6Z577in2oo2LCQoKKvb5m266SadOndLMmTP1ww8/6IcffpCkIq96udSeL9ZPSRX2c8zf/sHBwdq0aZM+/PBDRUVF6emnn1abNm0KvXzdndp86enpuummm1x+R/J/5y7ckRfXoztatGjhEqR+P/3hD39we5lFefPNN9W2bVt16NChRPXHjh1Tz5491aVLF82YMcPlueXLl2vy5Mny8/OTn5+fhg4dqtTUVPn5+entt98udHn33nuvXnrpJTkcDm3evFm33367atWqpe7du2vVqlUevSaPrpbq16+fy2MfHx/VrFlTPXr0cCbZstCkSRM1adLE+bhLly7au3evXnnlFb333nuFzmO322W328usJwDeV7VqVcXGxno0b0REhGrXrq19+/bpzjvv9LiHZs2aFTiSvGbNGjVu3Nh5tCEgIKDQoziDBg3So48+qldffVU7duzQkCFDCtR8//33zp1obm6uNm7c6LzC5Pdat26tZcuW6dlnny3w3G+//aZdu3Zp5syZuvbaayVJq1evdu/FlrDnfI0aNVJQUJCWLVumYcOGFXi+WbNmmj17tjIyMpyhdM2aNfLx8XF5z78YPz8/xcfHKz4+XuPHj1f16tW1fPly3XrrrW7VFvZzat++vebNm6f69evLz8+jXaek8+GnuCN5+RYvXqycnJwiny+twJienq6PP/5YCQkJJao/evSoevbsqQ4dOmjWrFny8XE9RrJu3TqX17dw4UK98MILWrt2baFHm9566y2Fh4fr5ptv1unTpyXJ+bpzcnJKtK0K49FPaPz48R6trCxcddVVHv3HBIB8zz77rP72t78pNDRUffv2VVZWljZs2KDTp09r7NixJVrGQw89pE6dOmnixInq37+/1q1bp9dee03//ve/nTX169fXt99+qzvuuEN2u101atSQJIWFhenWW2/VI488ot69e6tu3boFlj9t2jQ1atRIzZo10yuvvKLTp0/r//7v/wrtZdy4cWrVqpVGjhyp++67TwEBAVqxYoVuv/12hYeH64orrtCMGTMUFRWlQ4cO6fHHH3d7m5Wk53yBgYF67LHH9OijjyogIEBdu3bViRMntH37dg0dOlR33nmnxo8fryFDhuiZZ57RiRMndP/992vw4MGKiIgoUT+LFi3Svn371K1bN4WFhWnx4sVyOByFhqOL1davX18//PCDDhw4oGrVqik8PFyjRo3SzJkzNWDAAD366KMKDw/Xnj17NHfuXL355psuH5cVp379+lq2bJm6du0qu92usLCwQusu9VLoQ4cO6dSpUzp06JDy8vKcN2SMjY1VtWrVnHUfffSRcnNzNWjQoALLWL9+ve666y4tW7ZMderU0dGjR9WjRw/FxMRo8uTJOnHihLM2/1YEv/9+yQ0bNsjHx0ctW7YssPzjx4/rueeec/5REBYWpmbNmmnKlCnq3bu3li1bpieffNKzDVDSE4cuPCn3YpMnVMKTrH4vPj7e/OlPfypxPVdLAdZS2NVSFyrshMb58+eb37/9ffDBB6Zt27YmICDAhIWFmW7dupnPPvvMGFPwBFtjjDl9+rSRZFasWOEcy78U3N/f39SrV89MmjTJZR3r1q0zrVu3Nna7vcD6ly1bZiSZjz/+2GU8f91z5swxV111lQkICDDNmzc3y5cvd9YUdrHFypUrTZcuXYzdbjfVq1c3ffr0cT6/dOlS06xZM2O3203r1q3NypUrXd6DL3ZC8cV6LkxeXp557rnnTExMjHP7/POf/3Q+X9JLwS/0wAMPmO7duxtjzp+k2r17dxMWFua8VP+jjz5y1l54kvDFanft2mWuvvpqExQU5HIp+K+//mr+9Kc/merVq5ugoCDTtGlTM2bMGONwOAqsI98tt9zictXX559/bmJjY42fn1+ZXgpe2CXtv/99NcaYuLg4lysHL5T/c89//fkn5xc2FaW4E5rvuOOOAicN//DDD6Zp06YmPDzcPPvssy7PubP/thnz3w+eL8LHx+einwEbY2Sz2Up8GCk9PV179uyRdP48npdfflk9e/ZUeHi46tWrp3Hjxuno0aN69913JUlTpkxRgwYN1KJFC507d05vvvmmpk6dqq+//rrYk+culJaWptDQUKWmpiokJKRE8wBAWXvvvff04IMP6tixYwoICHCOHzhwQA0aNNDmzZvVtm1b7zVYiKJ6BsqCO/vvEn8stWLFiktu7Pc2bNignj17Oh/nH/4dMmSIZs+ercTERJez97Ozs/XQQw/p6NGjqlKlilq3bq1vvvnGZRkAUJlkZmYqMTFRzz//vO69995KERIqY8+4vJT4yI1VcOQGQEXyzDPP6B//+Ie6deumhQsXupwPIVXMIzcX6xkoC+7svy8p3GRmZurQoUMFLh/8/XX+FQnhBgCAyqdMPpa60IkTJ3TPPffoyy+/LPR5Ty/dAgAAuFQe3cRvzJgxSklJ0Q8//KCgoCAtWbJE77zzjho1aqTPP/+8tHsEAAAoMY+O3CxfvlwLFy5Ux44d5ePjo5iYGPXq1UshISFKSEjQjTfeWNp9AgAAlIhHR24yMjJUq1YtSedvupN/I59WrVoV+1UIAAAAZc2jcNOkSRPnl5m1adNGb7zxho4eParp06c7v0wOAADAGzz6WOqBBx5QYmKipPNfxdC3b1998MEHCggIcH6RGgAAgDeUyn1uMjMz9csvv6hevXrO70qpqLgUHACAysed/bdHH0v9/osqq1Spovbt21f4YAMAAKzPo3Bz3XXXqUGDBnriiSe0Y8eO0u4JAADAYx6Fm2PHjumhhx7SqlWr1LJlS7Vt21aTJk3SkSNHSrs/AAAAt1zyOTf79+/XnDlz9OGHH+qXX35Rt27dtHz58tLqr9Rxzg0AAJVPuX23VL68vDx9+eWX+vvf/66ff/65Qn/9AuEGAIDKp8xPKM63Zs0ajRw5UlFRURo4cKBatmypL7744lIWCQAAcEk8us/NuHHjNHfuXB07dky9evXSv/71L91yyy2qUqVKafcHAADgFo/CzbfffqtHHnlEf/nLX7j8GwAAVCgehZs1a9Y4//3hhx/q5ptvVtWqVUutKQAAAE95FG4udO+996pz58668sorS6OfcpORkSFfX98C476+vgoMDHSpK4qPj4+CgoI8qs3MzFRR53LbbDaXj/jcqT179qwcDkeRfVwYQt2pPXfuXLEnirtTW6VKFdlsNklSVlaWcnNzS6U2KChIPj7nTyPLzs5WTk5OqdQGBgY6f1fcqc3JyVF2dnaRtXa7XX5+fm7X5ubmKisrq8jagIAA+fv7u12bl5enc+fOFVnr7++vgIAAt2sdDofOnj1bKrV+fn6y2+2SJGOMMjMzS6XWnf/3vEcUXst7BO8R5fEeUWLmElWrVs3s3bv3UhdTblJTU42kIqcbbrjBpb5KlSpF1nbv3t2ltkaNGkXWduzY0aU2JiamyNrmzZu71DZv3rzI2piYGJfajh07Fllbo0YNl9ru3bsXWVulShWX2htuuKHY7Xah2267rdja9PR0Z+2QIUOKrT1+/LizduTIkcXW7t+/31n78MMPF1u7bds2Z+348eOLrV2/fr2z9sUXXyy2dsWKFc7a1157rdjaRYsWOWtnzZpVbO3HH3/srP3444+LrZ01a5azdtGiRcXWvvbaa87aFStWFFv74osvOmvXr19fbO348eOdtdu2bSu29uGHH3bW7t+/v9jakSNHOmuPHz9ebO2QIUOctenp6cXW3nbbbS6/w8XV8h5xfuI94n8T7xHnp7J+j8jff6emppqLuaSrpQAAACqaS77PzerVq9WpUyfn4d+KLv86+WPHjhV6nTyHnAuv5ZBz+RxyPp52Tk/M36oth1IUVs1fj/VppvjmEZfNIWc+luI9QuI9Ih8fS7nWlttN/I4fP65du3ZJkpo0aaJatWp5uqhyw038UFHlOYz+OPU7/ZqcrjyHkU2SzSbNG9FF7eqFebs9APCqMr+J35kzZzR48GDVqVNH3bt3V/fu3VWnTh0NGjRIqampHjUNXO4OncrUzsQzynOc/3vDSPKx2bRke5J3GwOASsajcDNs2DD98MMPWrRokVJSUpSSkqJFixZpw4YNuvfee0u7R+Cy4OdjKzBmJPn7cGocALjDo0vBFy1apK+++krXXHONc6xPnz6aOXOm+vbtW2rNAZeTumFB6t64pr7bfUIOI/nYJH9fm25tX8fbrQFApeJRuLniiisUGhpaYDw0NFRhYZwbAHjCZrNp+qAOmvTVLq0/cEo1qwXood5NdGXNat5uDQAqFY/CzVNPPaWxY8fqvffeU2RkpCQpKSlJjzzyiP7+97+XaoPA5SQowFdP39Tc220AQKXm0dVS7dq10549e5SVlaV69epJkg4dOiS73a5GjRq51G7atKl0Oi0lXC0FAEDl487+26MjN/369fNkNgAAgDJ3yTfxq2w4cgMAQOVTJve5ucwyEAAAqKRKHG5atGihuXPnFnv7Z0navXu3RowYoeeff/6SmwMAAHBXic+5mTp1qh577DGNHDlSvXr1UseOHVW7dm0FBgbq9OnT2rFjh1avXq3t27dr9OjRGjFiRFn2DQAAUCi3z7lZvXq1PvroI3333Xc6ePCgzp49qxo1aqhdu3bq06eP7rzzzgp9rxvOuQEAoPIpty/OrIwINwAAVD5l/sWZAAAAFZXb97k5efKk3n77ba1bt05JSee/rTgyMlJxcXG65557VLNmzVJvEgAAoKTcOnLz448/qnHjxnr11VcVGhqqbt26qVu3bgoNDdXUqVPVtGlTbdiwoax6BQAAuCi3zrm5+uqr1aZNG02fPl02m83lOWOM7rvvPv38889at25dqTdaWjjnBgCAyqfMvn7hp59+0uzZswsEG+n8Nxo/+OCDateunXvdAgAAlCK3PpaKjIzU+vXri3x+/fr1ioiIuOSmAAAAPOXWkZuHH35Yf/3rX7Vx40Zdf/31ziCTnJysZcuWaebMmZo8eXKZNAoAAFASboWbUaNGqUaNGnrllVf073//W3l5eZIkX19fdejQQbNnz9Zf/vKXMmkUAACgJDy+iV9OTo5OnjwpSapRo4b8/f1LtbGywgnFAABUPmV2QvGF/P39FRUV5ensAAAAZaJU71C8d+9eXXfddaW5SAAAALeUarhJT0/XqlWrSnORAAAAbnHrY6lXX3212OePHj16Sc0AAABcKrfCzZgxYxQVFaWAgIBCn8/Ozi6VpgAAADzlVriJiYnRCy+8UOTl3lu2bFGHDh1KpTEAAABPuHXOTYcOHbRx48Yin7fZbPLwynIAAIBS4daRmwkTJigzM7PI55s3b679+/dfclMAAACecivcNG/evNjn/f39FRMTc0kNAQAAXIpSvRQcAADA2zy6Q3G7du1ks9kKjNtsNgUGBio2NlZ33323evbseckNAgAAuMOjIzd9+/bVvn37VLVqVfXs2VM9e/ZUtWrVtHfvXnXq1EmJiYmKj4/XwoULi13Ot99+q5tuukm1a9eWzWbTggULLrrulStXqn379rLb7YqNjdXs2bM9eQkAAMCiPAo3J0+e1EMPPaTvvvtOL730kl566SV9++23evjhh5WRkaGvv/5aTz31lCZOnFjscjIyMtSmTRtNmzatROvdv3+/brzxRvXs2VNbtmzRmDFjNGzYMH311VeevAwAAGBBHn0reGhoqDZu3KjY2FiX8T179qhDhw5KTU3VL7/8ok6dOunMmTMla8Rm0/z589WvX78iax577DF98cUX2rZtm3PsjjvuUEpKipYsWVKi9fCt4AAAVD7u7L89OnITGBiotWvXFhhfu3atAgMDJUkOh8P579Kybt06xcfHu4z16dNH69atK3KerKwspaWluUwAAMC6PDqh+P7779d9992njRs3qlOnTpKkH3/8UW+++aaeeOIJSdJXX32ltm3bllqjkpSUlKSIiAiXsYiICKWlpens2bMKCgoqME9CQoKeffbZUu0DAABUXB6Fm6eeekoNGjTQa6+9pvfee0+S1KRJE82cOVMDBw6UJN13330aMWJE6XXqoXHjxmns2LHOx2lpaYqOjvZiRwAAoCx5FG4k6c4779Sdd95Z5POFHUW5VJGRkUpOTnYZS05OVkhISJHrs9vtstvtpd4LAAComDwON5K0ceNG7dy5U5LUokULtWvXrlSaKkpcXJwWL17sMrZ06VLFxcWV6XoBAEDl4VG4OX78uO644w6tXLlS1atXlySlpKSoZ8+emjt3rmrWrFmi5aSnp2vPnj3Ox/v379eWLVsUHh6uevXqady4cTp69KjeffddSec/6nrttdf06KOP6v/+7/+0fPlyffzxx/riiy88eRkAAMCCPLpa6v7779eZM2e0fft2nTp1SqdOndK2bduUlpamv/3tbyVezoYNG9SuXTvnEZ+xY8eqXbt2evrppyVJiYmJOnTokLO+QYMG+uKLL7R06VK1adNGL730kt5880316dPHk5cBAAAsyOP73HzzzTfOK6XyrV+/Xr1791ZKSkpp9VfquM8NAACVT5nf58bhcMjf37/AuL+/vxwOhyeLBAAAKBUehZvrrrtODzzwgI4dO+YcO3r0qB588EFdf/31pdYcAACAuzwKN6+99prS0tJUv359NWzYUA0bNlSDBg2UlpamqVOnlnaPAAAAJebR1VLR0dHatGmTvvnmG/3yyy+SpGbNmhX4agQAAIDy5tEJxZUZJxQDAFD5uLP/LvGRm1dffbXEDbhzOTgAAEBpKvGRmwYNGpRsgTab9u3bd0lNlSWO3AAAUPmUyZGb/fv3Fzq+evVqdezYUYGBge51CQAAUAY8ulrqQjfccIPLJeEAAADedMnh5jI7HxkAAFRwlxxuAAAAKpJLDjdvvPGGIiIiSqMXAACAS+bRTfwuNHDgwNLoAwAAoFTwsRQAALAUwg0AALAUwg0AALCUSz7nBgDK25JtSZrwn+36LSNb7eqF6ZX+bRQVGuTttgBUEBy5AVCpbD50WiM/2KjE1HPKynXoxwOndNdb65Wb5/B2awAqCMINgEpl6Y5k+dhsyr99aJ7DaPfxdB34LcOrfQGoOAg3ACoVf18fFXZf9ABf33LvBUDFxDk3qNS+3/ebvtt1XO99f0hnc/IUXi1AX95/jZ5cuF0/HUlRreBAvTWko66oZi9yGb8kpembHcny8/XRTW1qq051zt2oyG5tX0czv9unrJw85RnJxyZd26imosP5uQE4z2Yusy+Hcucr01GxvbvugJ5euP2idT426ccn4wsNOKt+PaGhs3+UMZKRUVW7nz4b0UWNIoLLomWUkl+S0vTy17/q+JksdYwJ00O9mygogCM3gJW5s/8m3KBSOpeTp5bjv1Kuo2S/vh1jwvTpiC4FxrtPWqFDpzKV/7/A12bT9c1qacZdHUuzXQDAJXJn/805N6iUUjJzShxsJOn4mXNFjGfpwnifZ4yOpRZeCwCoHAg3qJRqBttVo1pAies7xIQXOt6mbqh8fWzOxz42qV109UttDwDgRYQbVEq+PjbNvKujQoP8Czznd0FYkaTaoYF6pX/bQpcz+fY2ig7734mo7euF6dG+TUq1VwBA+eKcG1Rq6Vm52nM8XWt2n9D3+37TH1pFamDn+tp6JEWLtyWpTd3q6tsysthlZOXm6dekdPn52tQkIlg+vwtHAADv44TiYhBuAACofDihGAAAXLYINwAAwFIINwAAwFIINwAAwFIINwAAwFIIN/BIbp5DuXkOb7cBAEABfCs43JKd69DTC7fpkw1HZGR0U5vaeuHPrRXoz5cWAgAqBo7cwC0vL/1VH204rDxj5DDSf346pomLdni7LQAAnAg3cMvXO5JcvmjSYaRvdiZ7ryEAAH6HcAO3VAvw0++/nKBqAJ9uAgAqDsIN3DKyZ6yMzn97tu2/KWf0dbFe7QkAgAvxJzfc0rdlpN75v6v08YbDcjiMbm1fV72aR3i7LQAAnAg3cFv3xjXVvXFNb7cBAECh+FgKAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYSoUIN9OmTVP9+vUVGBiozp07a/369UXWzp49WzabzWUKDAwsx24BAEBF5vVw89FHH2ns2LEaP368Nm3apDZt2qhPnz46fvx4kfOEhIQoMTHROR08eLAcOwYAABWZ18PNyy+/rOHDh+uee+5R8+bNNX36dFWpUkVvv/12kfPYbDZFRkY6p4iIiHLsGAAAVGReDTfZ2dnauHGj4uPjnWM+Pj6Kj4/XunXripwvPT1dMTExio6O1i233KLt27cXWZuVlaW0tDSXCQAAWJdXw83JkyeVl5dX4MhLRESEkpKSCp2nSZMmevvtt7Vw4UK9//77cjgc6tKli44cOVJofUJCgkJDQ51TdHR0qb8OAABQcXj9Yyl3xcXF6a677lLbtm3VvXt3ffbZZ6pZs6beeOONQuvHjRun1NRU53T48OFy7hgAAJQnP2+uvEaNGvL19VVycrLLeHJysiIjI0u0DH9/f7Vr10579uwp9Hm73S673X7JvQIAgMrBq0duAgIC1KFDBy1btsw55nA4tGzZMsXFxZVoGXl5edq6dauioqLKqk0AAFCJePXIjSSNHTtWQ4YMUceOHXXVVVdpypQpysjI0D333CNJuuuuu1SnTh0lJCRIkiZMmKCrr75asbGxSklJ0aRJk3Tw4EENGzbMmy8DAABUEF4PN/3799eJEyf09NNPKykpSW3bttWSJUucJxkfOnRIPj7/O8B0+vRpDR8+XElJSQoLC1OHDh20du1aNW/e3FsvAQAAVCA2Y4zxdhPlKS0tTaGhoUpNTVVISIi32wEAACXgzv670l0tBQAAUBzCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBQ/bzcA7zh+5pw+33JMWbkO9WhSUy1qh3q7JQAASgXh5jJ0+FSmbpm2RimZ2bLJppeX/qrpgzqoV/MIb7cGAMAlI9x4yVvf7dOnm44owM9HD8Y3Vo8mtQrULNmWpHV7Tyo0yF+Dro5RrZDAUln3K0t/VerZHDmMJBnZjPTU/K2EGwCAJRBuvODvC7bpve8POh/fPetHvTqgrW5uU8c5Nm3FHk36apf8fGwykj744ZC++Nu1igwN1Kg5G/XFz0mSzp809cl9cepQP7zE609MPae888lGkmQknUjPkjFGNpvtUl8eAABexQnFXvDBDwcLjP1j0U7nv89m5+mlr3dJknIdRnkOo5SzOZq1Zr+eW7TDGWwkySHpz9PXKScnp8Trb103VD4XZBhfH5uaRYUQbAAAlkC48YILDpo4ZWTnOv99Jiun0JpTGdl6d92BQpf5yjd7Srz+MfGN1bnBFc7HtYLtenVAuxLPDwBARcbHUl4QGuSn1LO5LmNNI0Oc/65R1a7osCAdSzmnPHM+5eQ5jDo1CNeCzUcLXWZ6Vl6J1x8U4KsPhnXWruQzOpeTp6aRIQoK8PXglQAAUPFw5MYLPhh2tex+/9v0ESF2zbqnk/Oxj49Nb93dSRGhdufY0Gsa6PYOddWzac1Cl/lQ7yZu9eDz34+i2tULI9gAACzFZowp5AMQ60pLS1NoaKhSU1MVEhJy8RnKSG6uQ1uOpKiK3VfNowq/x0xunkPHUs4pONBPYVUDnOO9Xl6p3ccznI+fvbm5hnRpUOY9AwDgLe7svwk3AACgwnNn/83HUgAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFL8vN2AFZ3KyNY3O5KV43Coe+OaqhtWxdstAQBw2SDclLJDv2XqT6+v0W/p2bJJCvT31fvDOqtDTJi3WwMA4LLAx1Kl7J9f7lRKZo4kyUjKys3TE/O3ercpAAAuI4SbUnbwZIbyHMb52GGko6czvdgRAACXF8JNKWteO0S+PjbnY18fmxpHBHuxIwAALi+Em1L2xA3NdGWNqs7HV1QN0KTb23ixIwAALi+cUFzKrqhm13/uv0abDp1Wbp5Ru3rVFRzo7+22AAC4bFSIIzfTpk1T/fr1FRgYqM6dO2v9+vXF1n/yySdq2rSpAgMD1apVKy1evLicOi3cmXM5uv/DTWo5/itd9Y9v9OnGI+rSsIa6Na5JsAEAoJx5Pdx89NFHGjt2rMaPH69NmzapTZs26tOnj44fP15o/dq1azVgwAANHTpUmzdvVr9+/dSvXz9t27atnDv/n7Ef/6TFPycqPStXx89k6akF27R4a6LX+gEA4HJmM8aYi5eVnc6dO6tTp0567bXXJEkOh0PR0dG6//779fjjjxeo79+/vzIyMrRo0SLn2NVXX622bdtq+vTpBeqzsrKUlZXlfJyWlqbo6GilpqYqJCTkkvvPys1T06eW6MKNaLNJN7SM0rQ721/y8gEAwPn9d2hoaIn23149cpOdna2NGzcqPj7eOebj46P4+HitW7eu0HnWrVvnUi9Jffr0KbI+ISFBoaGhzik6Orr0XoAkX5tNPjaby5hNkp+vrfAZAABAmfJquDl58qTy8vIUERHhMh4REaGkpKRC50lKSnKrfty4cUpNTXVOhw8fLp3m/8vP10cDO9dTfpTJzzkDr6pXqusBAAAlY/mrpex2u+x2e5muY/xNzVUz2K7lO4+raqCvRnSPVecrryjTdQIAgMJ5NdzUqFFDvr6+Sk5OdhlPTk5WZGRkofNERka6VV8e/Hx99LfrG+lv1zfyWg8AAOA8r34sFRAQoA4dOmjZsmXOMYfDoWXLlikuLq7QeeLi4lzqJWnp0qVF1gMAgMuL1z+WGjt2rIYMGaKOHTvqqquu0pQpU5SRkaF77rlHknTXXXepTp06SkhIkCQ98MAD6t69u1566SXdeOONmjt3rjZs2KAZM2Z482UAAIAKwuvhpn///jpx4oSefvppJSUlqW3btlqyZInzpOFDhw7Jx+d/B5i6dOmiOXPm6KmnntITTzyhRo0aacGCBWrZsqW3XgIAAKhAvH6fm/LmznXyAACgYqg097kBAAAobYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKV6/Q3F5y79nYVpampc7AQAAJZW/3y7JvYcvu3Bz5swZSVJ0dLSXOwEAAO46c+aMQkNDi6257L5+weFw6NixYwoODpbNZivRPGlpaYqOjtbhw4f5yoZywPYuX2zv8sX2Ln9s8/JVVtvbGKMzZ86odu3aLt85WZjL7siNj4+P6tat69G8ISEh/McoR2zv8sX2Ll9s7/LHNi9fZbG9L3bEJh8nFAMAAEsh3AAAAEsh3JSA3W7X+PHjZbfbvd3KZYHtXb7Y3uWL7V3+2OblqyJs78vuhGIAAGBtHLkBAACWQrgBAACWQrgBAACWQrgBAACWQrj5r2nTpql+/foKDAxU586dtX79+mLrP/nkEzVt2lSBgYFq1aqVFi9eXE6dWoM723vmzJm69tprFRYWprCwMMXHx1/05wNX7v5+55s7d65sNpv69etXtg1ajLvbOyUlRaNGjVJUVJTsdrsaN27Me4ob3N3eU6ZMUZMmTRQUFKTo6Gg9+OCDOnfuXDl1W7l9++23uummm1S7dm3ZbDYtWLDgovOsXLlS7du3l91uV2xsrGbPnl3mfcrAzJ071wQEBJi3337bbN++3QwfPtxUr17dJCcnF1q/Zs0a4+vra1588UWzY8cO89RTTxl/f3+zdevWcu68cnJ3ew8cONBMmzbNbN682ezcudPcfffdJjQ01Bw5cqScO6+c3N3e+fbv32/q1Kljrr32WnPLLbeUT7MW4O72zsrKMh07djQ33HCDWb16tdm/f79ZuXKl2bJlSzl3Xjm5u70/+OADY7fbzQcffGD2799vvvrqKxMVFWUefPDBcu68clq8eLF58sknzWeffWYkmfnz5xdbv2/fPlOlShUzduxYs2PHDjN16lTj6+trlixZUqZ9Em6MMVdddZUZNWqU83FeXp6pXbu2SUhIKLT+L3/5i7nxxhtdxjp37mzuvffeMu3TKtzd3r+Xm5trgoODzTvvvFNWLVqKJ9s7NzfXdOnSxbz55ptmyJAhhBs3uLu9X3/9dXPllVea7Ozs8mrRUtzd3qNGjTLXXXedy9jYsWNN165dy7RPKypJuHn00UdNixYtXMb69+9v+vTpU4adGXPZfyyVnZ2tjRs3Kj4+3jnm4+Oj+Ph4rVu3rtB51q1b51IvSX369CmyHv/jyfb+vczMTOXk5Cg8PLys2rQMT7f3hAkTVKtWLQ0dOrQ82rQMT7b3559/rri4OI0aNUoRERFq2bKl/vnPfyovL6+82q60PNneXbp00caNG50fXe3bt0+LFy/WDTfcUC49X268tb+87L448/dOnjypvLw8RUREuIxHRETol19+KXSepKSkQuuTkpLKrE+r8GR7/95jjz2m2rVrF/gPg4I82d6rV6/WW2+9pS1btpRDh9biyfbet2+fli9frjvvvFOLFy/Wnj17NHLkSOXk5Gj8+PHl0Xal5cn2HjhwoE6ePKlrrrlGxhjl5ubqvvvu0xNPPFEeLV92itpfpqWl6ezZswoKCiqT9V72R25QuTz//POaO3eu5s+fr8DAQG+3YzlnzpzR4MGDNXPmTNWoUcPb7VwWHA6HatWqpRkzZqhDhw7q37+/nnzySU2fPt3brVnSypUr9c9//lP//ve/tWnTJn322Wf64osvNHHiRG+3hlJ02R+5qVGjhnx9fZWcnOwynpycrMjIyELniYyMdKse/+PJ9s43efJkPf/88/rmm2/UunXrsmzTMtzd3nv37tWBAwd00003OcccDockyc/PT7t27VLDhg3LtulKzJPf76ioKPn7+8vX19c51qxZMyUlJSk7O1sBAQFl2nNl5sn2/vvf/67Bgwdr2LBhkqRWrVopIyNDf/3rX/Xkk0/Kx4e/+UtTUfvLkJCQMjtqI3HkRgEBAerQoYOWLVvmHHM4HFq2bJni4uIKnScuLs6lXpKWLl1aZD3+x5PtLUkvvviiJk6cqCVLlqhjx47l0aoluLu9mzZtqq1bt2rLli3O6eabb1bPnj21ZcsWRUdHl2f7lY4nv99du3bVnj17nCFSkn799VdFRUURbC7Ck+2dmZlZIMDkB0vDVy2WOq/tL8v0dOVKYu7cucZut5vZs2ebHTt2mL/+9a+mevXqJikpyRhjzODBg83jjz/urF+zZo3x8/MzkydPNjt37jTjx4/nUnA3uLu9n3/+eRMQEGA+/fRTk5iY6JzOnDnjrZdQqbi7vX+Pq6Xc4+72PnTokAkODjajR482u3btMosWLTK1atUyzz33nLdeQqXi7vYeP368CQ4ONh9++KHZt2+f+frrr03Dhg3NX/7yF2+9hErlzJkzZvPmzWbz5s1Gknn55ZfN5s2bzcGDB40xxjz++ONm8ODBzvr8S8EfeeQRs3PnTjNt2jQuBS9PU6dONfXq1TMBAQHmqquuMt9//73zue7du5shQ4a41H/88cemcePGJiAgwLRo0cJ88cUX5dxx5ebO9o6JiTGSCkzjx48v/8YrKXd/vy9EuHGfu9t77dq1pnPnzsZut5srr7zS/OMf/zC5ubnl3HXl5c72zsnJMc8884xp2LChCQwMNNHR0WbkyJHm9OnT5d94JbRixYpC34/zt/GQIUNM9+7dC8zTtm1bExAQYK688koza9asMu/TZgzH4QAAgHVc9ufcAAAAayHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcALCcPn36yNfXVz/++GOB5+6++27ZbDbZbDYFBAQoNjZWEyZMUG5urhc6BVAWCDcALOXQoUNau3atRo8erbfffrvQmr59+yoxMVG7d+/WQw89pGeeeUaTJk0q504BlBXCDYAKqUePHrr//vs1ZswYhYWFKSIiQjNnzlRGRobuueceBQcHKzY2Vl9++aXLfLNmzdIf//hHjRgxQh9++KHOnj1bYNl2u12RkZGKiYnRiBEjFB8fr88//7y8XhqAMka4AVBhvfPOO6pRo4bWr1+v+++/XyNGjNDtt9+uLl26aNOmTerdu7cGDx6szMxMSZIxRrNmzdKgQYPUtGlTxcbG6tNPP73oeoKCgpSdnV3WLwdAOSHcAKiw2rRpo6eeekqNGjXSuHHjFBgYqBo1amj48OFq1KiRnn76af3222/6+eefJUnffPONMjMz1adPH0nSoEGD9NZbbxW5fGOMvvnmG3311Ve67rrryuU1ASh7hBsAFVbr1q2d//b19dUVV1yhVq1aOcciIiIkScePH5ckvf322+rfv7/8/PwkSQMGDNCaNWu0d+9el+UuWrRI1apVU2BgoP7whz+of//+euaZZ8r41QAoL4QbABWWv7+/y2ObzeYyZrPZJEkOh0OnTp3S/Pnz9e9//1t+fn7y8/NTnTp1lJubW+DE4p49e2rLli3avXu3zp49q3feeUdVq1Yt+xcEoFz4ebsBACgNH3zwgerWrasFCxa4jH/99dd66aWXNGHCBPn6+kqSqlatqtjYWC90CaA8EG4AWMJbb72l2267TS1btnQZj46O1rhx47RkyRLdeOONXuoOQHniYykAld7evXv1008/6c9//nOB50JDQ3X99dcXe2IxAGuxGWOMt5sAAAAoLRy5AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlvL/F9RBjqdawWAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data=map_scores, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
+    "plt.xlabel(\"mAP\")\n",
+    "plt.ylabel(\"-log10(p-value)\")\n",
+    "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
+    "plt.text(0.5, 1.5, f\"Phenotypically consistent = {100*map_scores.below_corrected_p.mean():.2f}%\", va=\"center\", ha=\"left\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],

From 769c22296a9e58de9744c7401629f9668e9381cb Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Tue, 2 Jul 2024 14:55:15 -0400
Subject: [PATCH 18/30] refactor(example): clean up, better descroptions and 
 variable naming

---
 examples/demo.ipynb | 4057 +++++++++++--------------------------------
 1 file changed, 1001 insertions(+), 3056 deletions(-)

diff --git a/examples/demo.ipynb b/examples/demo.ipynb
index e89acbe..ee76743 100644
--- a/examples/demo.ipynb
+++ b/examples/demo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,8 +10,21 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from copairs import map\n",
-    "from pycytominer import aggregate\n"
+    "from copairs import map\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This example demostrates how to use `copairs` to:\n",
+    "- assess phenotypic activity of perturbations' replicates against DMSO control replicates and\n",
+    "- assess phenotypic consistncy of perturbations htat target the same gene against other perturbations.\n",
+    "\n",
+    "Citation:\n",
+    "> Kalinin, A. A. et al. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, 2024-04, (2024)."
    ]
   },
   {
@@ -20,13 +33,44 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# imports for showing Figure 1 from the paper\n",
+    "# thase imports are only needed for showing Figure 1 from the paper\n",
     "import requests\n",
-    "from PIL import Image\n",
     "from io import BytesIO\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from PIL import Image\n",
     "from IPython.display import display"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=514x640>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1_path = \"F1.large.jpg\"\n",
+    "fig1_url = \"https://www.biorxiv.org/content/biorxiv/early/2024/04/02/2024.04.01.587631/F1.large.jpg\"\n",
+    "\n",
+    "if not Path(fig1_path).is_file():\n",
+    "    image = Image.open(BytesIO(requests.get(fig1_url).content))\n",
+    "    image.save(fig1_path)\n",
+    "\n",
+    "image = Image.open(fig1_path).resize((514, 640))\n",
+    "display(image)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -40,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +548,7 @@
        "[384 rows x 507 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,9 +563,16 @@
     "df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that in this dataset, pertubations can target multiple genes. We can list these targets from the `Metadata_target` column."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -548,7 +599,7 @@
        "       'BCL2|BCL2L1|BCL2L2'], dtype=object)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -566,33 +617,6 @@
     "Phenotypic activity of a perturbation reflects the average extent to which its replicate profiles are more similar to each other compared to control profiles (see Figure 1E)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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",
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",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=514x640>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "figure_url = \"https://www.biorxiv.org/content/biorxiv/early/2024/04/02/2024.04.01.587631/F1.large.jpg\"\n",
-    "response = requests.get(figure_url)\n",
-    "image = Image.open(BytesIO(response.content))\n",
-    "\n",
-    "size = (514, 640)\n",
-    "image = image.resize(size)\n",
-    "display(image)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -604,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -628,6 +652,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>Metadata_treatment_index</th>\n",
        "      <th>Metadata_broad_sample</th>\n",
        "      <th>Metadata_mg_per_ml</th>\n",
        "      <th>Metadata_mmoles_per_liter</th>\n",
@@ -637,8 +662,8 @@
        "      <th>Metadata_broad_sample_type</th>\n",
        "      <th>Metadata_pert_type</th>\n",
        "      <th>Metadata_broad_id</th>\n",
-       "      <th>Metadata_InChIKey14</th>\n",
        "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
        "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
        "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
        "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
@@ -648,12 +673,12 @@
        "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
        "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
        "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
-       "      <th>Metadata_treatment_index</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>-1</td>\n",
        "      <td>DMSO</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -663,8 +688,8 @@
        "      <td>control</td>\n",
        "      <td>control</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
+       "      <td>-1.3544</td>\n",
        "      <td>-1.07770</td>\n",
        "      <td>2.26020</td>\n",
        "      <td>-0.377010</td>\n",
@@ -674,10 +699,10 @@
        "      <td>2.87500</td>\n",
        "      <td>2.3047</td>\n",
        "      <td>-0.92358</td>\n",
-       "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>-1</td>\n",
        "      <td>DMSO</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -687,8 +712,8 @@
        "      <td>control</td>\n",
        "      <td>control</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
+       "      <td>-2.3840</td>\n",
        "      <td>-0.73440</td>\n",
        "      <td>1.12090</td>\n",
        "      <td>-0.182500</td>\n",
@@ -698,10 +723,10 @@
        "      <td>2.35230</td>\n",
        "      <td>1.8672</td>\n",
        "      <td>-0.11820</td>\n",
-       "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>-1</td>\n",
        "      <td>DMSO</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -711,8 +736,8 @@
        "      <td>control</td>\n",
        "      <td>control</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
+       "      <td>-1.9493</td>\n",
        "      <td>-0.36148</td>\n",
        "      <td>0.44050</td>\n",
        "      <td>0.326660</td>\n",
@@ -722,10 +747,10 @@
        "      <td>0.77847</td>\n",
        "      <td>1.0651</td>\n",
        "      <td>-0.44810</td>\n",
-       "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td>-1</td>\n",
        "      <td>DMSO</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -735,8 +760,8 @@
        "      <td>control</td>\n",
        "      <td>control</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
+       "      <td>-2.2909</td>\n",
        "      <td>-0.46380</td>\n",
        "      <td>0.96434</td>\n",
        "      <td>1.132200</td>\n",
@@ -746,10 +771,10 @@
        "      <td>1.48110</td>\n",
        "      <td>1.2943</td>\n",
        "      <td>-0.83810</td>\n",
-       "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td>-1</td>\n",
        "      <td>DMSO</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -759,8 +784,8 @@
        "      <td>control</td>\n",
        "      <td>control</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
+       "      <td>-1.8955</td>\n",
        "      <td>-1.05350</td>\n",
        "      <td>1.64840</td>\n",
        "      <td>0.057781</td>\n",
@@ -770,7 +795,6 @@
        "      <td>0.90213</td>\n",
        "      <td>1.1016</td>\n",
        "      <td>0.53225</td>\n",
-       "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -798,6 +822,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379</th>\n",
+       "      <td>379</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>3.248700</td>\n",
        "      <td>3.333300</td>\n",
@@ -807,8 +832,8 @@
        "      <td>trt</td>\n",
        "      <td>trt</td>\n",
        "      <td>BRD-K82746043</td>\n",
-       "      <td>JLYAXFNOILIKPP</td>\n",
        "      <td>...</td>\n",
+       "      <td>-6.1522</td>\n",
        "      <td>1.81410</td>\n",
        "      <td>1.54220</td>\n",
        "      <td>-1.874700</td>\n",
@@ -818,10 +843,10 @@
        "      <td>-3.25160</td>\n",
        "      <td>-2.7683</td>\n",
        "      <td>1.40170</td>\n",
-       "      <td>379</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>380</th>\n",
+       "      <td>380</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>1.082900</td>\n",
        "      <td>1.111100</td>\n",
@@ -831,8 +856,8 @@
        "      <td>trt</td>\n",
        "      <td>trt</td>\n",
        "      <td>BRD-K82746043</td>\n",
-       "      <td>JLYAXFNOILIKPP</td>\n",
        "      <td>...</td>\n",
+       "      <td>-5.1586</td>\n",
        "      <td>1.50580</td>\n",
        "      <td>1.68420</td>\n",
        "      <td>-1.126400</td>\n",
@@ -842,10 +867,10 @@
        "      <td>-1.79020</td>\n",
        "      <td>-1.2474</td>\n",
        "      <td>1.17600</td>\n",
-       "      <td>380</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>381</th>\n",
+       "      <td>381</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>0.360970</td>\n",
        "      <td>0.370370</td>\n",
@@ -855,8 +880,8 @@
        "      <td>trt</td>\n",
        "      <td>trt</td>\n",
        "      <td>BRD-K82746043</td>\n",
-       "      <td>JLYAXFNOILIKPP</td>\n",
        "      <td>...</td>\n",
+       "      <td>-5.9475</td>\n",
        "      <td>1.42100</td>\n",
        "      <td>1.51020</td>\n",
        "      <td>-1.103600</td>\n",
@@ -866,10 +891,10 @@
        "      <td>-2.97620</td>\n",
        "      <td>-2.0026</td>\n",
        "      <td>0.91557</td>\n",
-       "      <td>381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>382</th>\n",
+       "      <td>382</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>0.120320</td>\n",
        "      <td>0.123460</td>\n",
@@ -879,8 +904,8 @@
        "      <td>trt</td>\n",
        "      <td>trt</td>\n",
        "      <td>BRD-K82746043</td>\n",
-       "      <td>JLYAXFNOILIKPP</td>\n",
        "      <td>...</td>\n",
+       "      <td>-8.4408</td>\n",
        "      <td>2.99620</td>\n",
        "      <td>2.55230</td>\n",
        "      <td>-2.275200</td>\n",
@@ -890,10 +915,10 @@
        "      <td>-4.19030</td>\n",
        "      <td>-3.8360</td>\n",
        "      <td>1.02240</td>\n",
-       "      <td>382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>383</th>\n",
+       "      <td>383</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>0.040108</td>\n",
        "      <td>0.041152</td>\n",
@@ -903,8 +928,8 @@
        "      <td>trt</td>\n",
        "      <td>trt</td>\n",
        "      <td>BRD-K82746043</td>\n",
-       "      <td>JLYAXFNOILIKPP</td>\n",
        "      <td>...</td>\n",
+       "      <td>-7.9510</td>\n",
        "      <td>2.55730</td>\n",
        "      <td>3.05790</td>\n",
        "      <td>-1.466300</td>\n",
@@ -914,7 +939,6 @@
        "      <td>-4.49940</td>\n",
        "      <td>-3.4922</td>\n",
        "      <td>1.01170</td>\n",
-       "      <td>383</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -922,57 +946,70 @@
        "</div>"
       ],
       "text/plain": [
-       "      Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
-       "0                      DMSO            0.000000                   0.000000   \n",
-       "1                      DMSO            0.000000                   0.000000   \n",
-       "2                      DMSO            0.000000                   0.000000   \n",
-       "3                      DMSO            0.000000                   0.000000   \n",
-       "4                      DMSO            0.000000                   0.000000   \n",
-       "..                      ...                 ...                        ...   \n",
-       "379  BRD-K82746043-001-15-1            3.248700                   3.333300   \n",
-       "380  BRD-K82746043-001-15-1            1.082900                   1.111100   \n",
-       "381  BRD-K82746043-001-15-1            0.360970                   0.370370   \n",
-       "382  BRD-K82746043-001-15-1            0.120320                   0.123460   \n",
-       "383  BRD-K82746043-001-15-1            0.040108                   0.041152   \n",
+       "     Metadata_treatment_index   Metadata_broad_sample  Metadata_mg_per_ml  \\\n",
+       "0                          -1                    DMSO            0.000000   \n",
+       "1                          -1                    DMSO            0.000000   \n",
+       "2                          -1                    DMSO            0.000000   \n",
+       "3                          -1                    DMSO            0.000000   \n",
+       "4                          -1                    DMSO            0.000000   \n",
+       "..                        ...                     ...                 ...   \n",
+       "379                       379  BRD-K82746043-001-15-1            3.248700   \n",
+       "380                       380  BRD-K82746043-001-15-1            1.082900   \n",
+       "381                       381  BRD-K82746043-001-15-1            0.360970   \n",
+       "382                       382  BRD-K82746043-001-15-1            0.120320   \n",
+       "383                       383  BRD-K82746043-001-15-1            0.040108   \n",
        "\n",
-       "    Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
-       "0                NaN                     NaN                A01   \n",
-       "1                NaN                     NaN                A02   \n",
-       "2                NaN                     NaN                A03   \n",
-       "3                NaN                     NaN                A04   \n",
-       "4                NaN                     NaN                A05   \n",
-       "..               ...                     ...                ...   \n",
-       "379    BRD-K82746043  BRD-K82746043-001-15-1                P20   \n",
-       "380    BRD-K82746043  BRD-K82746043-001-15-1                P21   \n",
-       "381    BRD-K82746043  BRD-K82746043-001-15-1                P22   \n",
-       "382    BRD-K82746043  BRD-K82746043-001-15-1                P23   \n",
-       "383    BRD-K82746043  BRD-K82746043-001-15-1                P24   \n",
+       "     Metadata_mmoles_per_liter Metadata_pert_id    Metadata_pert_mfc_id  \\\n",
+       "0                     0.000000              NaN                     NaN   \n",
+       "1                     0.000000              NaN                     NaN   \n",
+       "2                     0.000000              NaN                     NaN   \n",
+       "3                     0.000000              NaN                     NaN   \n",
+       "4                     0.000000              NaN                     NaN   \n",
+       "..                         ...              ...                     ...   \n",
+       "379                   3.333300    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "380                   1.111100    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "381                   0.370370    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "382                   0.123460    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "383                   0.041152    BRD-K82746043  BRD-K82746043-001-15-1   \n",
        "\n",
-       "    Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
-       "0                      control            control               NaN   \n",
-       "1                      control            control               NaN   \n",
-       "2                      control            control               NaN   \n",
-       "3                      control            control               NaN   \n",
-       "4                      control            control               NaN   \n",
-       "..                         ...                ...               ...   \n",
-       "379                        trt                trt     BRD-K82746043   \n",
-       "380                        trt                trt     BRD-K82746043   \n",
-       "381                        trt                trt     BRD-K82746043   \n",
-       "382                        trt                trt     BRD-K82746043   \n",
-       "383                        trt                trt     BRD-K82746043   \n",
+       "    Metadata_pert_well Metadata_broad_sample_type Metadata_pert_type  \\\n",
+       "0                  A01                    control            control   \n",
+       "1                  A02                    control            control   \n",
+       "2                  A03                    control            control   \n",
+       "3                  A04                    control            control   \n",
+       "4                  A05                    control            control   \n",
+       "..                 ...                        ...                ...   \n",
+       "379                P20                        trt                trt   \n",
+       "380                P21                        trt                trt   \n",
+       "381                P22                        trt                trt   \n",
+       "382                P23                        trt                trt   \n",
+       "383                P24                        trt                trt   \n",
+       "\n",
+       "    Metadata_broad_id  ... Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "0                 NaN  ...                                        -1.3544   \n",
+       "1                 NaN  ...                                        -2.3840   \n",
+       "2                 NaN  ...                                        -1.9493   \n",
+       "3                 NaN  ...                                        -2.2909   \n",
+       "4                 NaN  ...                                        -1.8955   \n",
+       "..                ...  ...                                            ...   \n",
+       "379     BRD-K82746043  ...                                        -6.1522   \n",
+       "380     BRD-K82746043  ...                                        -5.1586   \n",
+       "381     BRD-K82746043  ...                                        -5.9475   \n",
+       "382     BRD-K82746043  ...                                        -8.4408   \n",
+       "383     BRD-K82746043  ...                                        -7.9510   \n",
        "\n",
-       "    Metadata_InChIKey14  ... Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
-       "0                   NaN  ...                                        -1.07770   \n",
-       "1                   NaN  ...                                        -0.73440   \n",
-       "2                   NaN  ...                                        -0.36148   \n",
-       "3                   NaN  ...                                        -0.46380   \n",
-       "4                   NaN  ...                                        -1.05350   \n",
-       "..                  ...  ...                                             ...   \n",
-       "379      JLYAXFNOILIKPP  ...                                         1.81410   \n",
-       "380      JLYAXFNOILIKPP  ...                                         1.50580   \n",
-       "381      JLYAXFNOILIKPP  ...                                         1.42100   \n",
-       "382      JLYAXFNOILIKPP  ...                                         2.99620   \n",
-       "383      JLYAXFNOILIKPP  ...                                         2.55730   \n",
+       "    Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "0                                          -1.07770   \n",
+       "1                                          -0.73440   \n",
+       "2                                          -0.36148   \n",
+       "3                                          -0.46380   \n",
+       "4                                          -1.05350   \n",
+       "..                                              ...   \n",
+       "379                                         1.81410   \n",
+       "380                                         1.50580   \n",
+       "381                                         1.42100   \n",
+       "382                                         2.99620   \n",
+       "383                                         2.55730   \n",
        "\n",
        "    Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
        "0                                         2.26020   \n",
@@ -1039,34 +1076,36 @@
        "382                            -4.19030                            -3.8360   \n",
        "383                            -4.49940                            -3.4922   \n",
        "\n",
-       "     Nuclei_Texture_Variance_RNA_10_0  Metadata_treatment_index  \n",
-       "0                            -0.92358                        -1  \n",
-       "1                            -0.11820                        -1  \n",
-       "2                            -0.44810                        -1  \n",
-       "3                            -0.83810                        -1  \n",
-       "4                             0.53225                        -1  \n",
-       "..                                ...                       ...  \n",
-       "379                           1.40170                       379  \n",
-       "380                           1.17600                       380  \n",
-       "381                           0.91557                       381  \n",
-       "382                           1.02240                       382  \n",
-       "383                           1.01170                       383  \n",
+       "     Nuclei_Texture_Variance_RNA_10_0  \n",
+       "0                            -0.92358  \n",
+       "1                            -0.11820  \n",
+       "2                            -0.44810  \n",
+       "3                            -0.83810  \n",
+       "4                             0.53225  \n",
+       "..                                ...  \n",
+       "379                           1.40170  \n",
+       "380                           1.17600  \n",
+       "381                           0.91557  \n",
+       "382                           1.02240  \n",
+       "383                           1.01170  \n",
        "\n",
        "[384 rows x 508 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "df_activity = df.copy()\n",
     "# make deafult value equal to row index\n",
-    "df[\"Metadata_treatment_index\"] = df.index\n",
+    "df_activity[\"Metadata_treatment_index\"] = df_activity.index\n",
     "# make index equal to -1 for all DMSO treatment replicates\n",
-    "df.loc[df[\"Metadata_broad_sample\"] == \"DMSO\", \"Metadata_treatment_index\"] = -1\n",
+    "df_activity.loc[df[\"Metadata_broad_sample\"] == \"DMSO\", \"Metadata_treatment_index\"] = -1\n",
     "# now all treatment replicates differ in the index column, except for DMSO replicates\n",
-    "df"
+    "df_activity.insert(0, \"Metadata_treatment_index\", df_activity.pop(\"Metadata_treatment_index\"))\n",
+    "df_activity"
    ]
   },
   {
@@ -1075,18 +1114,18 @@
    "source": [
     "Next, we define the rules by which profiles are grouped based on metadata:\n",
     "\n",
-    "* Two profiles are a positive pair if they belong to the same group (i.e., they are replicates of the same compound). Therefore, they should share the same value in the metadata column that identifies the specific compound. We add this column to a list names `pos_sameby`.\n",
+    "* Two profiles are a positive pair if they belong to the same group that is not a control group. In this case, any two replicate profiles of the same compound are a positive pair. To define that using metadata columns, positive pairs should share the same value in the metadata column that identifies compounds (`Metadata_broad_sample`). We add this column to a list names `pos_sameby`.\n",
     "\n",
-    "* No metadata columns are additionally needed to tell apart replicates of the same compound here (although in theory, one could request them to be from different plate rows or columns, for instance). So we keep `pos_diffby` empty.\n",
+    "* In this case, profiles that form a positive pair do not need to be different in any of the metatada columns, so we keep `pos_diffby` empty. Although one could define them as being from different batches, for instance, to account for batch effects.\n",
     "\n",
-    "* Two profiles are a negative pair when one of them belongs to a group of compound replicates and another to a group of controls. That means they should be different both in the metadata column that identifies the specific compound and the treatment index columns that we created. The latter is needed to ensure that replicates of compounds are retrieved against only negative controls at this stage (and not against replicates of other compounds). We list these columns in `neg_diffby`.\n",
+    "* Two profiles are a negative pair when one of them belongs to a group of compound replicates and another to a group of DMSO controls. That means they should be different both in the metadata column that identifies the specific compound and the treatment index columns that we created. The latter is needed to ensure that replicates of compounds are retrieved against only DMSO controls at this stage (and not against replicates of other compounds). We list these columns in `neg_diffby`.\n",
     "\n",
-    "* No metadata columns are additionally needed to define negative pairs. So we keep `neg_sameby` empty."
+    "* Profiles that form a negative pair do not need to be same in any of the metatada columns, so we keep `neg_sameby` empty."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1103,18 +1142,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can use `average_precision` function to calculate the average precision score for each replicate of each compound."
+    "Now we can use `average_precision` function to calculate the average precision score for each replicate of each compound.\n",
+    "\n",
+    "It returns metadata with 3 new columns: number of positive and negative pairs for each replicate profile and the average precision score."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d31b7d0cc4c47ca8598bebec375b895",
+       "model_id": "aebfcde75ae74aee8390c1f731c392ae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1128,7 +1169,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aa1475e1749f433390abdd865fdee7f9",
+       "model_id": "0c13ce3b5c0f4c0fab21d5cd94780bee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1160,6 +1201,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>Metadata_treatment_index</th>\n",
        "      <th>Metadata_broad_sample</th>\n",
        "      <th>Metadata_mg_per_ml</th>\n",
        "      <th>Metadata_mmoles_per_liter</th>\n",
@@ -1174,7 +1216,6 @@
        "      <th>Metadata_target</th>\n",
        "      <th>Metadata_broad_date</th>\n",
        "      <th>Metadata_Well</th>\n",
-       "      <th>Metadata_treatment_index</th>\n",
        "      <th>n_pos_pairs</th>\n",
        "      <th>n_total_pairs</th>\n",
        "      <th>average_precision</th>\n",
@@ -1183,6 +1224,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
+       "      <td>6</td>\n",
        "      <td>BRD-K74363950-004-01-0</td>\n",
        "      <td>5.655600</td>\n",
        "      <td>10.000000</td>\n",
@@ -1197,13 +1239,13 @@
        "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>A07</td>\n",
-       "      <td>6</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.050922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
+       "      <td>7</td>\n",
        "      <td>BRD-K74363950-004-01-0</td>\n",
        "      <td>1.885200</td>\n",
        "      <td>3.333300</td>\n",
@@ -1218,13 +1260,13 @@
        "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>A08</td>\n",
-       "      <td>7</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.308904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
+       "      <td>8</td>\n",
        "      <td>BRD-K74363950-004-01-0</td>\n",
        "      <td>0.628400</td>\n",
        "      <td>1.111100</td>\n",
@@ -1239,13 +1281,13 @@
        "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>A09</td>\n",
-       "      <td>8</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.412513</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
+       "      <td>9</td>\n",
        "      <td>BRD-K74363950-004-01-0</td>\n",
        "      <td>0.209470</td>\n",
        "      <td>0.370370</td>\n",
@@ -1260,13 +1302,13 @@
        "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>A10</td>\n",
-       "      <td>9</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.377730</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
+       "      <td>10</td>\n",
        "      <td>BRD-K74363950-004-01-0</td>\n",
        "      <td>0.069823</td>\n",
        "      <td>0.123460</td>\n",
@@ -1281,7 +1323,6 @@
        "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>A11</td>\n",
-       "      <td>10</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.715591</td>\n",
@@ -1309,6 +1350,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379</th>\n",
+       "      <td>379</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>3.248700</td>\n",
        "      <td>3.333300</td>\n",
@@ -1323,13 +1365,13 @@
        "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>P20</td>\n",
-       "      <td>379</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.726786</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>380</th>\n",
+       "      <td>380</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>1.082900</td>\n",
        "      <td>1.111100</td>\n",
@@ -1344,13 +1386,13 @@
        "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>P21</td>\n",
-       "      <td>380</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.658824</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>381</th>\n",
+       "      <td>381</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>0.360970</td>\n",
        "      <td>0.370370</td>\n",
@@ -1365,13 +1407,13 @@
        "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>P22</td>\n",
-       "      <td>381</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.517619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>382</th>\n",
+       "      <td>382</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>0.120320</td>\n",
        "      <td>0.123460</td>\n",
@@ -1386,13 +1428,13 @@
        "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>P23</td>\n",
-       "      <td>382</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.543290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>383</th>\n",
+       "      <td>383</td>\n",
        "      <td>BRD-K82746043-001-15-1</td>\n",
        "      <td>0.040108</td>\n",
        "      <td>0.041152</td>\n",
@@ -1407,7 +1449,6 @@
        "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
        "      <td>broad_id_20170327</td>\n",
        "      <td>P24</td>\n",
-       "      <td>383</td>\n",
        "      <td>5</td>\n",
        "      <td>383</td>\n",
        "      <td>0.535714</td>\n",
@@ -1418,57 +1459,57 @@
        "</div>"
       ],
       "text/plain": [
-       "      Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
-       "6    BRD-K74363950-004-01-0            5.655600                  10.000000   \n",
-       "7    BRD-K74363950-004-01-0            1.885200                   3.333300   \n",
-       "8    BRD-K74363950-004-01-0            0.628400                   1.111100   \n",
-       "9    BRD-K74363950-004-01-0            0.209470                   0.370370   \n",
-       "10   BRD-K74363950-004-01-0            0.069823                   0.123460   \n",
-       "..                      ...                 ...                        ...   \n",
-       "379  BRD-K82746043-001-15-1            3.248700                   3.333300   \n",
-       "380  BRD-K82746043-001-15-1            1.082900                   1.111100   \n",
-       "381  BRD-K82746043-001-15-1            0.360970                   0.370370   \n",
-       "382  BRD-K82746043-001-15-1            0.120320                   0.123460   \n",
-       "383  BRD-K82746043-001-15-1            0.040108                   0.041152   \n",
+       "     Metadata_treatment_index   Metadata_broad_sample  Metadata_mg_per_ml  \\\n",
+       "6                           6  BRD-K74363950-004-01-0            5.655600   \n",
+       "7                           7  BRD-K74363950-004-01-0            1.885200   \n",
+       "8                           8  BRD-K74363950-004-01-0            0.628400   \n",
+       "9                           9  BRD-K74363950-004-01-0            0.209470   \n",
+       "10                         10  BRD-K74363950-004-01-0            0.069823   \n",
+       "..                        ...                     ...                 ...   \n",
+       "379                       379  BRD-K82746043-001-15-1            3.248700   \n",
+       "380                       380  BRD-K82746043-001-15-1            1.082900   \n",
+       "381                       381  BRD-K82746043-001-15-1            0.360970   \n",
+       "382                       382  BRD-K82746043-001-15-1            0.120320   \n",
+       "383                       383  BRD-K82746043-001-15-1            0.040108   \n",
        "\n",
-       "    Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
-       "6      BRD-K74363950  BRD-K74363950-004-01-0                A07   \n",
-       "7      BRD-K74363950  BRD-K74363950-004-01-0                A08   \n",
-       "8      BRD-K74363950  BRD-K74363950-004-01-0                A09   \n",
-       "9      BRD-K74363950  BRD-K74363950-004-01-0                A10   \n",
-       "10     BRD-K74363950  BRD-K74363950-004-01-0                A11   \n",
-       "..               ...                     ...                ...   \n",
-       "379    BRD-K82746043  BRD-K82746043-001-15-1                P20   \n",
-       "380    BRD-K82746043  BRD-K82746043-001-15-1                P21   \n",
-       "381    BRD-K82746043  BRD-K82746043-001-15-1                P22   \n",
-       "382    BRD-K82746043  BRD-K82746043-001-15-1                P23   \n",
-       "383    BRD-K82746043  BRD-K82746043-001-15-1                P24   \n",
+       "     Metadata_mmoles_per_liter Metadata_pert_id    Metadata_pert_mfc_id  \\\n",
+       "6                    10.000000    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "7                     3.333300    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "8                     1.111100    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "9                     0.370370    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "10                    0.123460    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "..                         ...              ...                     ...   \n",
+       "379                   3.333300    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "380                   1.111100    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "381                   0.370370    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "382                   0.123460    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "383                   0.041152    BRD-K82746043  BRD-K82746043-001-15-1   \n",
        "\n",
-       "    Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
-       "6                          trt                trt     BRD-K74363950   \n",
-       "7                          trt                trt     BRD-K74363950   \n",
-       "8                          trt                trt     BRD-K74363950   \n",
-       "9                          trt                trt     BRD-K74363950   \n",
-       "10                         trt                trt     BRD-K74363950   \n",
-       "..                         ...                ...               ...   \n",
-       "379                        trt                trt     BRD-K82746043   \n",
-       "380                        trt                trt     BRD-K82746043   \n",
-       "381                        trt                trt     BRD-K82746043   \n",
-       "382                        trt                trt     BRD-K82746043   \n",
-       "383                        trt                trt     BRD-K82746043   \n",
+       "    Metadata_pert_well Metadata_broad_sample_type Metadata_pert_type  \\\n",
+       "6                  A07                        trt                trt   \n",
+       "7                  A08                        trt                trt   \n",
+       "8                  A09                        trt                trt   \n",
+       "9                  A10                        trt                trt   \n",
+       "10                 A11                        trt                trt   \n",
+       "..                 ...                        ...                ...   \n",
+       "379                P20                        trt                trt   \n",
+       "380                P21                        trt                trt   \n",
+       "381                P22                        trt                trt   \n",
+       "382                P23                        trt                trt   \n",
+       "383                P24                        trt                trt   \n",
        "\n",
-       "    Metadata_InChIKey14                       Metadata_moa  \\\n",
-       "6        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
-       "7        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
-       "8        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
-       "9        ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
-       "10       ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
-       "..                  ...                                ...   \n",
-       "379      JLYAXFNOILIKPP                      BCL inhibitor   \n",
-       "380      JLYAXFNOILIKPP                      BCL inhibitor   \n",
-       "381      JLYAXFNOILIKPP                      BCL inhibitor   \n",
-       "382      JLYAXFNOILIKPP                      BCL inhibitor   \n",
-       "383      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "    Metadata_broad_id Metadata_InChIKey14                       Metadata_moa  \\\n",
+       "6       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "7       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "8       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "9       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "10      BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "..                ...                 ...                                ...   \n",
+       "379     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "380     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "381     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "382     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "383     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
        "\n",
        "                   Metadata_target Metadata_broad_date Metadata_Well  \\\n",
        "6    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A07   \n",
@@ -1483,52 +1524,54 @@
        "382             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P23   \n",
        "383             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P24   \n",
        "\n",
-       "     Metadata_treatment_index  n_pos_pairs  n_total_pairs  average_precision  \n",
-       "6                           6            5            383           0.050922  \n",
-       "7                           7            5            383           0.308904  \n",
-       "8                           8            5            383           0.412513  \n",
-       "9                           9            5            383           0.377730  \n",
-       "10                         10            5            383           0.715591  \n",
-       "..                        ...          ...            ...                ...  \n",
-       "379                       379            5            383           0.726786  \n",
-       "380                       380            5            383           0.658824  \n",
-       "381                       381            5            383           0.517619  \n",
-       "382                       382            5            383           0.543290  \n",
-       "383                       383            5            383           0.535714  \n",
+       "     n_pos_pairs  n_total_pairs  average_precision  \n",
+       "6              5            383           0.050922  \n",
+       "7              5            383           0.308904  \n",
+       "8              5            383           0.412513  \n",
+       "9              5            383           0.377730  \n",
+       "10             5            383           0.715591  \n",
+       "..           ...            ...                ...  \n",
+       "379            5            383           0.726786  \n",
+       "380            5            383           0.658824  \n",
+       "381            5            383           0.517619  \n",
+       "382            5            383           0.543290  \n",
+       "383            5            383           0.535714  \n",
        "\n",
        "[360 rows x 18 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "metadata = df.filter(regex=\"^Metadata\")\n",
-    "profiles = df.filter(regex=\"^(?!Metadata)\").values\n",
+    "metadata = df_activity.filter(regex=\"^Metadata\")\n",
+    "profiles = df_activity.filter(regex=\"^(?!Metadata)\").values\n",
     "\n",
-    "ap_scores = map.average_precision(metadata, profiles, pos_sameby, pos_diffby, neg_sameby, neg_diffby)\n",
-    "ap_scores = ap_scores.query(\"Metadata_broad_sample != 'DMSO'\") # remove DMSO\n",
-    "ap_scores"
+    "replicate_aps = map.average_precision(metadata, profiles, pos_sameby, pos_diffby, neg_sameby, neg_diffby)\n",
+    "replicate_aps = replicate_aps.query(\"Metadata_broad_sample != 'DMSO'\") # remove DMSO\n",
+    "replicate_aps"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At the next step, we average the AP scores, calculate p-value using permutation testing, and perform FDR correction to compare across compounds."
+    "At the next step, we average replicate AP scores at the per-compound level to obtain mAP values using `mean_average_precision`.\n",
+    "\n",
+    "It also calculates p-values using permutation testing, and performs FDR correction to compare across compounds."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5495ad647caf4f0b94f306299c3258d7",
+       "model_id": "d3c57cc5258f4dcb86670efd06eab762",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1542,7 +1585,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce7f604d72f54c2c81a0fc5a4eb30214",
+       "model_id": "ad8f608cec534e7a97a5c30c4257a74f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1684,621 +1727,45 @@
        "      <td>False</td>\n",
        "      <td>1.104069</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>BRD-A82156122-001-01-9</td>\n",
-       "      <td>0.082152</td>\n",
-       "      <td>0.038296</td>\n",
-       "      <td>0.041909</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.377693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>BRD-K50691590-001-02-2</td>\n",
-       "      <td>0.995162</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>BRD-K60230970-001-10-0</td>\n",
-       "      <td>0.951137</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>BRD-K67789209-001-01-0</td>\n",
-       "      <td>0.317370</td>\n",
-       "      <td>0.000500</td>\n",
-       "      <td>0.000906</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.042795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>BRD-K67844266-003-01-9</td>\n",
-       "      <td>0.356141</td>\n",
-       "      <td>0.000200</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.397983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>BRD-K68103045-001-02-9</td>\n",
-       "      <td>0.268105</td>\n",
-       "      <td>0.000800</td>\n",
-       "      <td>0.001365</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.865004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>BRD-K68132782-003-13-8</td>\n",
-       "      <td>0.192768</td>\n",
-       "      <td>0.012899</td>\n",
-       "      <td>0.016390</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.785430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>BRD-K68164687-001-01-6</td>\n",
-       "      <td>0.938532</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>BRD-K68232413-001-01-2</td>\n",
-       "      <td>0.256140</td>\n",
-       "      <td>0.001000</td>\n",
-       "      <td>0.001526</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.816399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>BRD-K68488863-001-04-9</td>\n",
-       "      <td>0.106826</td>\n",
-       "      <td>0.028397</td>\n",
-       "      <td>0.032295</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.490867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>BRD-K68532323-003-02-8</td>\n",
-       "      <td>0.701013</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>BRD-K68552125-001-05-3</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>BRD-K68693535-001-03-4</td>\n",
-       "      <td>0.072458</td>\n",
-       "      <td>0.048295</td>\n",
-       "      <td>0.050929</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>1.293031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>BRD-K68747584-001-02-0</td>\n",
-       "      <td>0.673878</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>BRD-K68938568-001-01-7</td>\n",
-       "      <td>0.336573</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>0.000773</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.111677</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>BRD-K69236721-001-02-7</td>\n",
-       "      <td>0.356350</td>\n",
-       "      <td>0.000200</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.397983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>BRD-K69247067-001-01-8</td>\n",
-       "      <td>0.551509</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>BRD-K70330367-003-07-9</td>\n",
-       "      <td>0.150161</td>\n",
-       "      <td>0.014399</td>\n",
-       "      <td>0.017768</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.750351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>BRD-K70358946-001-15-7</td>\n",
-       "      <td>0.477971</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>BRD-K70401845-003-09-6</td>\n",
-       "      <td>0.688838</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>BRD-K70463136-001-01-5</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>BRD-K70557564-305-03-6</td>\n",
-       "      <td>0.126098</td>\n",
-       "      <td>0.018298</td>\n",
-       "      <td>0.021226</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.673134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>BRD-K70778732-003-26-7</td>\n",
-       "      <td>0.206752</td>\n",
-       "      <td>0.012899</td>\n",
-       "      <td>0.016390</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.785430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>BRD-K70912147-001-01-8</td>\n",
-       "      <td>0.097564</td>\n",
-       "      <td>0.030197</td>\n",
-       "      <td>0.033681</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.472612</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>BRD-K70914287-300-02-8</td>\n",
-       "      <td>0.409013</td>\n",
-       "      <td>0.000200</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.397983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>BRD-K71035033-001-07-1</td>\n",
-       "      <td>0.052030</td>\n",
-       "      <td>0.078692</td>\n",
-       "      <td>0.078692</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>1.104069</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>BRD-K71221037-001-01-6</td>\n",
-       "      <td>0.726187</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>BRD-K71281111-001-04-3</td>\n",
-       "      <td>0.353957</td>\n",
-       "      <td>0.000200</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.397983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>BRD-K71289571-001-11-4</td>\n",
-       "      <td>0.257474</td>\n",
-       "      <td>0.001000</td>\n",
-       "      <td>0.001526</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.816399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>BRD-K71480163-001-01-4</td>\n",
-       "      <td>0.223272</td>\n",
-       "      <td>0.004900</td>\n",
-       "      <td>0.006931</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.159203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>BRD-K72215350-001-06-5</td>\n",
-       "      <td>0.466051</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>BRD-K72222507-003-16-8</td>\n",
-       "      <td>0.073473</td>\n",
-       "      <td>0.047095</td>\n",
-       "      <td>0.050584</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>1.295988</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>BRD-K72414522-001-06-7</td>\n",
-       "      <td>0.186676</td>\n",
-       "      <td>0.012899</td>\n",
-       "      <td>0.016390</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.785430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>BRD-K73196317-003-14-8</td>\n",
-       "      <td>0.136886</td>\n",
-       "      <td>0.015798</td>\n",
-       "      <td>0.018700</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.728154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>BRD-K73237276-001-01-0</td>\n",
-       "      <td>0.239427</td>\n",
-       "      <td>0.001800</td>\n",
-       "      <td>0.002677</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.572408</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>BRD-K73319509-001-08-0</td>\n",
-       "      <td>0.231632</td>\n",
-       "      <td>0.002700</td>\n",
-       "      <td>0.003915</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.407312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>BRD-K74363950-004-01-0</td>\n",
-       "      <td>0.408596</td>\n",
-       "      <td>0.000200</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.397983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>BRD-K75958547-238-01-0</td>\n",
-       "      <td>0.260311</td>\n",
-       "      <td>0.001000</td>\n",
-       "      <td>0.001526</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.816399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>BRD-K76908866-001-07-6</td>\n",
-       "      <td>0.172913</td>\n",
-       "      <td>0.012999</td>\n",
-       "      <td>0.016390</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1.785430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>BRD-K77908580-001-09-6</td>\n",
-       "      <td>0.509422</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>BRD-K79254416-001-22-6</td>\n",
-       "      <td>0.866124</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>BRD-K80700417-001-04-2</td>\n",
-       "      <td>0.070291</td>\n",
-       "      <td>0.050195</td>\n",
-       "      <td>0.051988</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>1.284100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>BRD-K81144366-003-19-7</td>\n",
-       "      <td>0.311539</td>\n",
-       "      <td>0.000500</td>\n",
-       "      <td>0.000906</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.042795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>BRD-K81258678-001-01-0</td>\n",
-       "      <td>0.758700</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>BRD-K81957469-001-01-0</td>\n",
-       "      <td>0.428269</td>\n",
-       "      <td>0.000200</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.397983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>BRD-K82135108-001-04-3</td>\n",
-       "      <td>0.419468</td>\n",
-       "      <td>0.000200</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.397983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>BRD-K82677201-001-01-6</td>\n",
-       "      <td>0.724665</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>BRD-K82746043-001-15-1</td>\n",
-       "      <td>0.592890</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.000276</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3.558835</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Metadata_broad_sample  mean_average_precision   p_value  \\\n",
-       "0   BRD-A69275535-001-01-5                0.203576  0.012899   \n",
-       "1   BRD-A69636825-003-04-7                0.269093  0.000800   \n",
-       "2   BRD-A69815203-001-07-6                0.862226  0.000100   \n",
-       "3   BRD-A70858459-001-01-7                0.351816  0.000200   \n",
-       "4   BRD-A72309220-001-04-1                0.263986  0.000900   \n",
-       "5   BRD-A72390365-001-15-2                0.554667  0.000100   \n",
-       "6   BRD-A73368467-003-17-6                0.788666  0.000100   \n",
-       "7   BRD-A74980173-001-11-9                0.500600  0.000100   \n",
-       "8   BRD-A81233518-004-16-1                0.140208  0.015598   \n",
-       "9   BRD-A82035391-001-02-7                0.052362  0.077692   \n",
-       "10  BRD-A82156122-001-01-9                0.082152  0.038296   \n",
-       "11  BRD-K50691590-001-02-2                0.995162  0.000100   \n",
-       "12  BRD-K60230970-001-10-0                0.951137  0.000100   \n",
-       "13  BRD-K67789209-001-01-0                0.317370  0.000500   \n",
-       "14  BRD-K67844266-003-01-9                0.356141  0.000200   \n",
-       "15  BRD-K68103045-001-02-9                0.268105  0.000800   \n",
-       "16  BRD-K68132782-003-13-8                0.192768  0.012899   \n",
-       "17  BRD-K68164687-001-01-6                0.938532  0.000100   \n",
-       "18  BRD-K68232413-001-01-2                0.256140  0.001000   \n",
-       "19  BRD-K68488863-001-04-9                0.106826  0.028397   \n",
-       "20  BRD-K68532323-003-02-8                0.701013  0.000100   \n",
-       "21  BRD-K68552125-001-05-3                1.000000  0.000100   \n",
-       "22  BRD-K68693535-001-03-4                0.072458  0.048295   \n",
-       "23  BRD-K68747584-001-02-0                0.673878  0.000100   \n",
-       "24  BRD-K68938568-001-01-7                0.336573  0.000400   \n",
-       "25  BRD-K69236721-001-02-7                0.356350  0.000200   \n",
-       "26  BRD-K69247067-001-01-8                0.551509  0.000100   \n",
-       "27  BRD-K70330367-003-07-9                0.150161  0.014399   \n",
-       "28  BRD-K70358946-001-15-7                0.477971  0.000100   \n",
-       "29  BRD-K70401845-003-09-6                0.688838  0.000100   \n",
-       "30  BRD-K70463136-001-01-5                1.000000  0.000100   \n",
-       "31  BRD-K70557564-305-03-6                0.126098  0.018298   \n",
-       "32  BRD-K70778732-003-26-7                0.206752  0.012899   \n",
-       "33  BRD-K70912147-001-01-8                0.097564  0.030197   \n",
-       "34  BRD-K70914287-300-02-8                0.409013  0.000200   \n",
-       "35  BRD-K71035033-001-07-1                0.052030  0.078692   \n",
-       "36  BRD-K71221037-001-01-6                0.726187  0.000100   \n",
-       "37  BRD-K71281111-001-04-3                0.353957  0.000200   \n",
-       "38  BRD-K71289571-001-11-4                0.257474  0.001000   \n",
-       "39  BRD-K71480163-001-01-4                0.223272  0.004900   \n",
-       "40  BRD-K72215350-001-06-5                0.466051  0.000100   \n",
-       "41  BRD-K72222507-003-16-8                0.073473  0.047095   \n",
-       "42  BRD-K72414522-001-06-7                0.186676  0.012899   \n",
-       "43  BRD-K73196317-003-14-8                0.136886  0.015798   \n",
-       "44  BRD-K73237276-001-01-0                0.239427  0.001800   \n",
-       "45  BRD-K73319509-001-08-0                0.231632  0.002700   \n",
-       "46  BRD-K74363950-004-01-0                0.408596  0.000200   \n",
-       "47  BRD-K75958547-238-01-0                0.260311  0.001000   \n",
-       "48  BRD-K76908866-001-07-6                0.172913  0.012999   \n",
-       "49  BRD-K77908580-001-09-6                0.509422  0.000100   \n",
-       "50  BRD-K79254416-001-22-6                0.866124  0.000100   \n",
-       "51  BRD-K80700417-001-04-2                0.070291  0.050195   \n",
-       "52  BRD-K81144366-003-19-7                0.311539  0.000500   \n",
-       "53  BRD-K81258678-001-01-0                0.758700  0.000100   \n",
-       "54  BRD-K81957469-001-01-0                0.428269  0.000200   \n",
-       "55  BRD-K82135108-001-04-3                0.419468  0.000200   \n",
-       "56  BRD-K82677201-001-01-6                0.724665  0.000100   \n",
-       "57  BRD-K82746043-001-15-1                0.592890  0.000100   \n",
+       "    Metadata_broad_sample  mean_average_precision   p_value  \\\n",
+       "0  BRD-A69275535-001-01-5                0.203576  0.012899   \n",
+       "1  BRD-A69636825-003-04-7                0.269093  0.000800   \n",
+       "2  BRD-A69815203-001-07-6                0.862226  0.000100   \n",
+       "3  BRD-A70858459-001-01-7                0.351816  0.000200   \n",
+       "4  BRD-A72309220-001-04-1                0.263986  0.000900   \n",
+       "5  BRD-A72390365-001-15-2                0.554667  0.000100   \n",
+       "6  BRD-A73368467-003-17-6                0.788666  0.000100   \n",
+       "7  BRD-A74980173-001-11-9                0.500600  0.000100   \n",
+       "8  BRD-A81233518-004-16-1                0.140208  0.015598   \n",
+       "9  BRD-A82035391-001-02-7                0.052362  0.077692   \n",
        "\n",
-       "    corrected_p_value  below_p  below_corrected_p  -log10(p-value)  \n",
-       "0            0.016390     True               True         1.785430  \n",
-       "1            0.001365     True               True         2.865004  \n",
-       "2            0.000276     True               True         3.558835  \n",
-       "3            0.000400     True               True         3.397983  \n",
-       "4            0.001491     True               True         2.826441  \n",
-       "5            0.000276     True               True         3.558835  \n",
-       "6            0.000276     True               True         3.558835  \n",
-       "7            0.000276     True               True         3.558835  \n",
-       "8            0.018700     True               True         1.728154  \n",
-       "9            0.078692    False              False         1.104069  \n",
-       "10           0.041909     True               True         1.377693  \n",
-       "11           0.000276     True               True         3.558835  \n",
-       "12           0.000276     True               True         3.558835  \n",
-       "13           0.000906     True               True         3.042795  \n",
-       "14           0.000400     True               True         3.397983  \n",
-       "15           0.001365     True               True         2.865004  \n",
-       "16           0.016390     True               True         1.785430  \n",
-       "17           0.000276     True               True         3.558835  \n",
-       "18           0.001526     True               True         2.816399  \n",
-       "19           0.032295     True               True         1.490867  \n",
-       "20           0.000276     True               True         3.558835  \n",
-       "21           0.000276     True               True         3.558835  \n",
-       "22           0.050929     True              False         1.293031  \n",
-       "23           0.000276     True               True         3.558835  \n",
-       "24           0.000773     True               True         3.111677  \n",
-       "25           0.000400     True               True         3.397983  \n",
-       "26           0.000276     True               True         3.558835  \n",
-       "27           0.017768     True               True         1.750351  \n",
-       "28           0.000276     True               True         3.558835  \n",
-       "29           0.000276     True               True         3.558835  \n",
-       "30           0.000276     True               True         3.558835  \n",
-       "31           0.021226     True               True         1.673134  \n",
-       "32           0.016390     True               True         1.785430  \n",
-       "33           0.033681     True               True         1.472612  \n",
-       "34           0.000400     True               True         3.397983  \n",
-       "35           0.078692    False              False         1.104069  \n",
-       "36           0.000276     True               True         3.558835  \n",
-       "37           0.000400     True               True         3.397983  \n",
-       "38           0.001526     True               True         2.816399  \n",
-       "39           0.006931     True               True         2.159203  \n",
-       "40           0.000276     True               True         3.558835  \n",
-       "41           0.050584     True              False         1.295988  \n",
-       "42           0.016390     True               True         1.785430  \n",
-       "43           0.018700     True               True         1.728154  \n",
-       "44           0.002677     True               True         2.572408  \n",
-       "45           0.003915     True               True         2.407312  \n",
-       "46           0.000400     True               True         3.397983  \n",
-       "47           0.001526     True               True         2.816399  \n",
-       "48           0.016390     True               True         1.785430  \n",
-       "49           0.000276     True               True         3.558835  \n",
-       "50           0.000276     True               True         3.558835  \n",
-       "51           0.051988    False              False         1.284100  \n",
-       "52           0.000906     True               True         3.042795  \n",
-       "53           0.000276     True               True         3.558835  \n",
-       "54           0.000400     True               True         3.397983  \n",
-       "55           0.000400     True               True         3.397983  \n",
-       "56           0.000276     True               True         3.558835  \n",
-       "57           0.000276     True               True         3.558835  "
+       "   corrected_p_value  below_p  below_corrected_p  -log10(p-value)  \n",
+       "0           0.016390     True               True         1.785430  \n",
+       "1           0.001365     True               True         2.865004  \n",
+       "2           0.000276     True               True         3.558835  \n",
+       "3           0.000400     True               True         3.397983  \n",
+       "4           0.001491     True               True         2.826441  \n",
+       "5           0.000276     True               True         3.558835  \n",
+       "6           0.000276     True               True         3.558835  \n",
+       "7           0.000276     True               True         3.558835  \n",
+       "8           0.018700     True               True         1.728154  \n",
+       "9           0.078692    False              False         1.104069  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "map_scores = map.mean_average_precision(ap_scores, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
-    "map_scores[\"-log10(p-value)\"] = -map_scores[\"corrected_p_value\"].apply(np.log10)\n",
-    "map_scores.head(10)"
+    "replicate_maps = map.mean_average_precision(replicate_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
+    "replicate_maps[\"-log10(p-value)\"] = -replicate_maps[\"corrected_p_value\"].apply(np.log10)\n",
+    "replicate_maps.head(10)"
    ]
   },
   {
@@ -2310,7 +1777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -2325,11 +1792,14 @@
     }
    ],
    "source": [
-    "plt.scatter(data=map_scores, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
+    "active_ratio = replicate_maps.below_corrected_p.mean()\n",
+    "\n",
+    "plt.scatter(data=replicate_maps, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
+    "# 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', \n",
     "plt.xlabel(\"mAP\")\n",
     "plt.ylabel(\"-log10(p-value)\")\n",
     "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
-    "plt.text(0.5, 1.5, f\"Phenotypically active = {100*map_scores.below_corrected_p.mean():.2f}%\", va=\"center\", ha=\"left\")\n",
+    "plt.text(0.5, 1.5, f\"Phenotypically active = {100*active_ratio:.2f}%\", va=\"center\", ha=\"left\")\n",
     "plt.show()"
    ]
   },
@@ -2337,16 +1807,356 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Calculate mAP for assessing phenotypic consistency of compounds grouped by targets\n",
-    "\n",
-    "Phenotypic consitency of a group of perturbations reflects the average extent to which members of this group are more similar to each other compared to other groups (see Figure 1F).\n",
-    "\n",
-    "When computing phenotypic consistency, each perturbation’s replicate profiles are usually aggregated into a consensus profile by taking the median of each feature to reduce profile noise and improve computational efficiency."
+    "## Calculate mAP for assessing phenotypic consistency of compounds grouped by targets\n",
+    "\n",
+    "Phenotypic consitency of a group of perturbations reflects the average extent to which members of this group are more similar to each other compared to other groups (see Figure 1F).\n",
+    "\n",
+    "First, we are going to filter out compounds that were not phenotypically active using mAP p-values from the previous section.\n",
+    "\n",
+    "Next, we will aggregate each compound’s replicate profiles into a \"consensus\" profile by taking the median of each feature to reduce profile noise and improve computational efficiency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>Metadata_InChIKey14</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>5.655600</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A07</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.51038</td>\n",
+       "      <td>-0.76402</td>\n",
+       "      <td>1.616400</td>\n",
+       "      <td>-0.49600</td>\n",
+       "      <td>-0.481360</td>\n",
+       "      <td>2.421100</td>\n",
+       "      <td>1.10790</td>\n",
+       "      <td>1.13820</td>\n",
+       "      <td>1.14320</td>\n",
+       "      <td>0.329230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>1.885200</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A08</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.23602</td>\n",
+       "      <td>-0.41129</td>\n",
+       "      <td>0.304960</td>\n",
+       "      <td>0.47884</td>\n",
+       "      <td>0.005852</td>\n",
+       "      <td>-0.710330</td>\n",
+       "      <td>0.41986</td>\n",
+       "      <td>-0.23888</td>\n",
+       "      <td>0.54949</td>\n",
+       "      <td>-0.092826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.628400</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A09</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.52939</td>\n",
+       "      <td>-0.54727</td>\n",
+       "      <td>0.722570</td>\n",
+       "      <td>0.73399</td>\n",
+       "      <td>0.223850</td>\n",
+       "      <td>0.035842</td>\n",
+       "      <td>0.33318</td>\n",
+       "      <td>0.39064</td>\n",
+       "      <td>0.42969</td>\n",
+       "      <td>-0.811390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.209470</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A10</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.58515</td>\n",
+       "      <td>-0.41533</td>\n",
+       "      <td>0.044874</td>\n",
+       "      <td>0.76374</td>\n",
+       "      <td>0.062913</td>\n",
+       "      <td>-0.656850</td>\n",
+       "      <td>0.18149</td>\n",
+       "      <td>-0.10960</td>\n",
+       "      <td>0.48699</td>\n",
+       "      <td>-0.345260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.069823</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A11</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.52686</td>\n",
+       "      <td>-0.57823</td>\n",
+       "      <td>0.591610</td>\n",
+       "      <td>0.85184</td>\n",
+       "      <td>0.560370</td>\n",
+       "      <td>0.039184</td>\n",
+       "      <td>0.59864</td>\n",
+       "      <td>0.44123</td>\n",
+       "      <td>0.75783</td>\n",
+       "      <td>-0.018031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.023274</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A12</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.48060</td>\n",
+       "      <td>-1.47220</td>\n",
+       "      <td>0.814150</td>\n",
+       "      <td>0.79463</td>\n",
+       "      <td>0.089249</td>\n",
+       "      <td>0.072240</td>\n",
+       "      <td>0.91828</td>\n",
+       "      <td>0.39626</td>\n",
+       "      <td>1.09120</td>\n",
+       "      <td>-0.243750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>BRD-K75958547-238-01-0</td>\n",
+       "      <td>4.615400</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>BRD-K75958547</td>\n",
+       "      <td>BRD-K75958547-238-01-0</td>\n",
+       "      <td>A13</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K75958547</td>\n",
+       "      <td>VGYFMXBACGZSIL</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.89680</td>\n",
+       "      <td>-0.97404</td>\n",
+       "      <td>-5.025000</td>\n",
+       "      <td>-10.41400</td>\n",
+       "      <td>-6.067500</td>\n",
+       "      <td>7.625700</td>\n",
+       "      <td>3.31830</td>\n",
+       "      <td>3.27410</td>\n",
+       "      <td>-2.12240</td>\n",
+       "      <td>2.299300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 507 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
+       "6   BRD-K74363950-004-01-0            5.655600                  10.000000   \n",
+       "7   BRD-K74363950-004-01-0            1.885200                   3.333300   \n",
+       "8   BRD-K74363950-004-01-0            0.628400                   1.111100   \n",
+       "9   BRD-K74363950-004-01-0            0.209470                   0.370370   \n",
+       "10  BRD-K74363950-004-01-0            0.069823                   0.123460   \n",
+       "11  BRD-K74363950-004-01-0            0.023274                   0.041152   \n",
+       "12  BRD-K75958547-238-01-0            4.615400                  10.000000   \n",
+       "\n",
+       "   Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
+       "6     BRD-K74363950  BRD-K74363950-004-01-0                A07   \n",
+       "7     BRD-K74363950  BRD-K74363950-004-01-0                A08   \n",
+       "8     BRD-K74363950  BRD-K74363950-004-01-0                A09   \n",
+       "9     BRD-K74363950  BRD-K74363950-004-01-0                A10   \n",
+       "10    BRD-K74363950  BRD-K74363950-004-01-0                A11   \n",
+       "11    BRD-K74363950  BRD-K74363950-004-01-0                A12   \n",
+       "12    BRD-K75958547  BRD-K75958547-238-01-0                A13   \n",
+       "\n",
+       "   Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
+       "6                         trt                trt     BRD-K74363950   \n",
+       "7                         trt                trt     BRD-K74363950   \n",
+       "8                         trt                trt     BRD-K74363950   \n",
+       "9                         trt                trt     BRD-K74363950   \n",
+       "10                        trt                trt     BRD-K74363950   \n",
+       "11                        trt                trt     BRD-K74363950   \n",
+       "12                        trt                trt     BRD-K75958547   \n",
+       "\n",
+       "   Metadata_InChIKey14  ... Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "6       ASMXXROZKSBQIH  ...                                       -0.51038   \n",
+       "7       ASMXXROZKSBQIH  ...                                       -0.23602   \n",
+       "8       ASMXXROZKSBQIH  ...                                       -0.52939   \n",
+       "9       ASMXXROZKSBQIH  ...                                       -0.58515   \n",
+       "10      ASMXXROZKSBQIH  ...                                       -0.52686   \n",
+       "11      ASMXXROZKSBQIH  ...                                       -0.48060   \n",
+       "12      VGYFMXBACGZSIL  ...                                       -5.89680   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "6                                         -0.76402   \n",
+       "7                                         -0.41129   \n",
+       "8                                         -0.54727   \n",
+       "9                                         -0.41533   \n",
+       "10                                        -0.57823   \n",
+       "11                                        -1.47220   \n",
+       "12                                        -0.97404   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "6                                       1.616400   \n",
+       "7                                       0.304960   \n",
+       "8                                       0.722570   \n",
+       "9                                       0.044874   \n",
+       "10                                      0.591610   \n",
+       "11                                      0.814150   \n",
+       "12                                     -5.025000   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "6                                          -0.49600   \n",
+       "7                                           0.47884   \n",
+       "8                                           0.73399   \n",
+       "9                                           0.76374   \n",
+       "10                                          0.85184   \n",
+       "11                                          0.79463   \n",
+       "12                                        -10.41400   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "6                                         -0.481360   \n",
+       "7                                          0.005852   \n",
+       "8                                          0.223850   \n",
+       "9                                          0.062913   \n",
+       "10                                         0.560370   \n",
+       "11                                         0.089249   \n",
+       "12                                        -6.067500   \n",
+       "\n",
+       "    Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "6                            2.421100                             1.10790   \n",
+       "7                           -0.710330                             0.41986   \n",
+       "8                            0.035842                             0.33318   \n",
+       "9                           -0.656850                             0.18149   \n",
+       "10                           0.039184                             0.59864   \n",
+       "11                           0.072240                             0.91828   \n",
+       "12                           7.625700                             3.31830   \n",
+       "\n",
+       "    Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "6                              1.13820                            1.14320   \n",
+       "7                             -0.23888                            0.54949   \n",
+       "8                              0.39064                            0.42969   \n",
+       "9                             -0.10960                            0.48699   \n",
+       "10                             0.44123                            0.75783   \n",
+       "11                             0.39626                            1.09120   \n",
+       "12                             3.27410                           -2.12240   \n",
+       "\n",
+       "    Nuclei_Texture_Variance_RNA_10_0  \n",
+       "6                           0.329230  \n",
+       "7                          -0.092826  \n",
+       "8                          -0.811390  \n",
+       "9                          -0.345260  \n",
+       "10                         -0.018031  \n",
+       "11                         -0.243750  \n",
+       "12                          2.299300  \n",
+       "\n",
+       "[7 rows x 507 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only keep active compounds, i.e. those with corrected p-value < 0.05\n",
+    "active_compounds = replicate_maps.query(\"below_corrected_p\")[\"Metadata_broad_sample\"]\n",
+    "df_consistent = df.query(\"Metadata_broad_sample in @active_compounds\")\n",
+    "df_consistent.head(7)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -2395,7 +2205,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>0</th>\n",
        "      <td>BRD-A69636825-003-04-7</td>\n",
        "      <td>[CACNA1C, CACNA1S, CACNA2D1, CACNG1, HTR3A, KC...</td>\n",
        "      <td>-0.326365</td>\n",
@@ -2414,1930 +2224,245 @@
        "      <td>0.201909</td>\n",
        "      <td>-1.003185</td>\n",
        "      <td>-1.405850</td>\n",
-       "      <td>-1.495100</td>\n",
-       "      <td>-0.867225</td>\n",
-       "      <td>-0.066115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>BRD-A69815203-001-07-6</td>\n",
-       "      <td>[ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...</td>\n",
-       "      <td>2.487450</td>\n",
-       "      <td>-2.872750</td>\n",
-       "      <td>0.616635</td>\n",
-       "      <td>-0.451942</td>\n",
-       "      <td>-2.260100</td>\n",
-       "      <td>-3.300900</td>\n",
-       "      <td>0.316320</td>\n",
-       "      <td>-1.825400</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-2.681800</td>\n",
-       "      <td>-0.197230</td>\n",
-       "      <td>-4.717350</td>\n",
-       "      <td>0.644170</td>\n",
-       "      <td>1.324100</td>\n",
-       "      <td>0.103070</td>\n",
-       "      <td>0.986025</td>\n",
-       "      <td>1.346200</td>\n",
-       "      <td>0.773450</td>\n",
-       "      <td>-2.749350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>BRD-A70858459-001-01-7</td>\n",
-       "      <td>[ESR1, ESR2, MAP1A, MAP2]</td>\n",
-       "      <td>-0.920210</td>\n",
-       "      <td>1.461550</td>\n",
-       "      <td>0.445630</td>\n",
-       "      <td>-0.394235</td>\n",
-       "      <td>1.528450</td>\n",
-       "      <td>1.116100</td>\n",
-       "      <td>-0.054990</td>\n",
-       "      <td>1.061270</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.238875</td>\n",
-       "      <td>0.326475</td>\n",
-       "      <td>0.064563</td>\n",
-       "      <td>0.187646</td>\n",
-       "      <td>0.200447</td>\n",
-       "      <td>-0.695660</td>\n",
-       "      <td>0.100225</td>\n",
-       "      <td>0.401885</td>\n",
-       "      <td>0.114583</td>\n",
-       "      <td>-0.245753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>BRD-A72309220-001-04-1</td>\n",
-       "      <td>[HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...</td>\n",
-       "      <td>0.045435</td>\n",
-       "      <td>0.099755</td>\n",
-       "      <td>0.103628</td>\n",
-       "      <td>0.592620</td>\n",
-       "      <td>-0.352200</td>\n",
-       "      <td>0.202930</td>\n",
-       "      <td>-0.059855</td>\n",
-       "      <td>-0.353755</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.069575</td>\n",
-       "      <td>-0.475915</td>\n",
-       "      <td>-0.174002</td>\n",
-       "      <td>0.217965</td>\n",
-       "      <td>0.090715</td>\n",
-       "      <td>-0.154695</td>\n",
-       "      <td>0.165235</td>\n",
-       "      <td>-0.160191</td>\n",
-       "      <td>0.242195</td>\n",
-       "      <td>-0.126886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>BRD-A73368467-003-17-6</td>\n",
-       "      <td>[HRH1]</td>\n",
-       "      <td>-0.062074</td>\n",
-       "      <td>-0.314820</td>\n",
-       "      <td>0.526190</td>\n",
-       "      <td>-0.502485</td>\n",
-       "      <td>-0.444675</td>\n",
-       "      <td>-0.191225</td>\n",
-       "      <td>0.145019</td>\n",
-       "      <td>0.018870</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.527805</td>\n",
-       "      <td>-1.204250</td>\n",
-       "      <td>0.615420</td>\n",
-       "      <td>-0.187645</td>\n",
-       "      <td>0.321880</td>\n",
-       "      <td>1.013235</td>\n",
-       "      <td>0.793675</td>\n",
-       "      <td>0.682925</td>\n",
-       "      <td>1.075500</td>\n",
-       "      <td>0.844115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>BRD-A81233518-004-16-1</td>\n",
-       "      <td>[CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]</td>\n",
-       "      <td>-0.612415</td>\n",
-       "      <td>-0.128128</td>\n",
-       "      <td>-0.741865</td>\n",
-       "      <td>0.178005</td>\n",
-       "      <td>0.006800</td>\n",
-       "      <td>0.282935</td>\n",
-       "      <td>0.587865</td>\n",
-       "      <td>0.561290</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.659600</td>\n",
-       "      <td>-0.702090</td>\n",
-       "      <td>-0.011905</td>\n",
-       "      <td>-0.000570</td>\n",
-       "      <td>0.066570</td>\n",
-       "      <td>0.015971</td>\n",
-       "      <td>0.257335</td>\n",
-       "      <td>0.140519</td>\n",
-       "      <td>0.471360</td>\n",
-       "      <td>-0.283820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>BRD-A82035391-001-02-7</td>\n",
-       "      <td>[AVPR1A, AVPR2]</td>\n",
-       "      <td>-0.300770</td>\n",
-       "      <td>0.132704</td>\n",
-       "      <td>-0.619200</td>\n",
-       "      <td>0.053390</td>\n",
-       "      <td>-0.329085</td>\n",
-       "      <td>0.107972</td>\n",
-       "      <td>0.157675</td>\n",
-       "      <td>0.834860</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-1.682600</td>\n",
-       "      <td>0.840775</td>\n",
-       "      <td>0.287105</td>\n",
-       "      <td>-0.617285</td>\n",
-       "      <td>-0.596950</td>\n",
-       "      <td>0.194805</td>\n",
-       "      <td>-1.037480</td>\n",
-       "      <td>-0.871205</td>\n",
-       "      <td>-0.846345</td>\n",
-       "      <td>0.533585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>BRD-A82156122-001-01-9</td>\n",
-       "      <td>[DPP4]</td>\n",
-       "      <td>-0.229737</td>\n",
-       "      <td>-0.045755</td>\n",
-       "      <td>-1.031130</td>\n",
-       "      <td>0.078795</td>\n",
-       "      <td>-0.541225</td>\n",
-       "      <td>0.393505</td>\n",
-       "      <td>-0.494920</td>\n",
-       "      <td>0.976335</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.154289</td>\n",
-       "      <td>0.216080</td>\n",
-       "      <td>0.636485</td>\n",
-       "      <td>0.088675</td>\n",
-       "      <td>-0.438930</td>\n",
-       "      <td>-0.251265</td>\n",
-       "      <td>-0.333184</td>\n",
-       "      <td>-0.601420</td>\n",
-       "      <td>-0.130211</td>\n",
-       "      <td>0.183651</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>BRD-K50691590-001-02-2</td>\n",
-       "      <td>[PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSM...</td>\n",
-       "      <td>7.777100</td>\n",
-       "      <td>-2.558850</td>\n",
-       "      <td>-0.169902</td>\n",
-       "      <td>-0.768970</td>\n",
-       "      <td>6.395400</td>\n",
-       "      <td>-7.443450</td>\n",
-       "      <td>-7.568300</td>\n",
-       "      <td>-21.230000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-5.809700</td>\n",
-       "      <td>0.732385</td>\n",
-       "      <td>-7.893800</td>\n",
-       "      <td>-6.190550</td>\n",
-       "      <td>-1.162450</td>\n",
-       "      <td>2.364450</td>\n",
-       "      <td>1.785100</td>\n",
-       "      <td>1.329300</td>\n",
-       "      <td>-1.494850</td>\n",
-       "      <td>1.846500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>BRD-K60230970-001-10-0</td>\n",
-       "      <td>[PSMB1]</td>\n",
-       "      <td>-4.508950</td>\n",
-       "      <td>10.006400</td>\n",
-       "      <td>2.551850</td>\n",
-       "      <td>-0.706140</td>\n",
-       "      <td>13.484500</td>\n",
-       "      <td>6.264200</td>\n",
-       "      <td>-5.839750</td>\n",
-       "      <td>-11.688000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-5.048700</td>\n",
-       "      <td>0.381675</td>\n",
-       "      <td>-3.348650</td>\n",
-       "      <td>-6.486350</td>\n",
-       "      <td>-0.308715</td>\n",
-       "      <td>3.929950</td>\n",
-       "      <td>1.430250</td>\n",
-       "      <td>3.869900</td>\n",
-       "      <td>-5.724050</td>\n",
-       "      <td>6.475100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>BRD-K67789209-001-01-0</td>\n",
-       "      <td>[HTR1A, HTR2A]</td>\n",
-       "      <td>0.052475</td>\n",
-       "      <td>-0.040268</td>\n",
-       "      <td>0.666435</td>\n",
-       "      <td>0.548785</td>\n",
-       "      <td>0.178140</td>\n",
-       "      <td>0.230900</td>\n",
-       "      <td>-0.015085</td>\n",
-       "      <td>-0.344318</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.077800</td>\n",
-       "      <td>-0.781525</td>\n",
-       "      <td>0.235365</td>\n",
-       "      <td>-0.045767</td>\n",
-       "      <td>0.177765</td>\n",
-       "      <td>0.637345</td>\n",
-       "      <td>0.130025</td>\n",
-       "      <td>-0.011241</td>\n",
-       "      <td>0.533865</td>\n",
-       "      <td>0.416050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>BRD-K67844266-003-01-9</td>\n",
-       "      <td>[NAE1, UBA3]</td>\n",
-       "      <td>0.077450</td>\n",
-       "      <td>3.715650</td>\n",
-       "      <td>-1.087885</td>\n",
-       "      <td>0.005565</td>\n",
-       "      <td>4.808450</td>\n",
-       "      <td>0.042300</td>\n",
-       "      <td>-0.246730</td>\n",
-       "      <td>-1.929100</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.102012</td>\n",
-       "      <td>1.663355</td>\n",
-       "      <td>-0.582000</td>\n",
-       "      <td>-0.398745</td>\n",
-       "      <td>1.070995</td>\n",
-       "      <td>1.386330</td>\n",
-       "      <td>1.302950</td>\n",
-       "      <td>2.515300</td>\n",
-       "      <td>0.468750</td>\n",
-       "      <td>-0.502195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>BRD-K68103045-001-02-9</td>\n",
-       "      <td>[GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6]</td>\n",
-       "      <td>0.245735</td>\n",
-       "      <td>-0.142770</td>\n",
-       "      <td>0.757980</td>\n",
-       "      <td>0.636800</td>\n",
-       "      <td>-0.116950</td>\n",
-       "      <td>-0.329765</td>\n",
-       "      <td>-0.183954</td>\n",
-       "      <td>-0.042453</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.590850</td>\n",
-       "      <td>-0.164918</td>\n",
-       "      <td>0.120890</td>\n",
-       "      <td>-0.352405</td>\n",
-       "      <td>-0.109732</td>\n",
-       "      <td>0.364360</td>\n",
-       "      <td>0.170652</td>\n",
-       "      <td>-0.019672</td>\n",
-       "      <td>0.283860</td>\n",
-       "      <td>0.404025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>BRD-K68132782-003-13-8</td>\n",
-       "      <td>[SQLE]</td>\n",
-       "      <td>-0.115830</td>\n",
-       "      <td>-0.127212</td>\n",
-       "      <td>0.552555</td>\n",
-       "      <td>0.065825</td>\n",
-       "      <td>-0.123745</td>\n",
-       "      <td>-0.011705</td>\n",
-       "      <td>0.427760</td>\n",
-       "      <td>-0.108485</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.463495</td>\n",
-       "      <td>0.093568</td>\n",
-       "      <td>0.442330</td>\n",
-       "      <td>0.244282</td>\n",
-       "      <td>0.630595</td>\n",
-       "      <td>0.216720</td>\n",
-       "      <td>-0.148982</td>\n",
-       "      <td>-0.025293</td>\n",
-       "      <td>0.054689</td>\n",
-       "      <td>0.263120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>BRD-K68164687-001-01-6</td>\n",
-       "      <td>[TUBB]</td>\n",
-       "      <td>1.410420</td>\n",
-       "      <td>2.547000</td>\n",
-       "      <td>-2.946600</td>\n",
-       "      <td>-0.618420</td>\n",
-       "      <td>3.138550</td>\n",
-       "      <td>-0.396105</td>\n",
-       "      <td>1.149465</td>\n",
-       "      <td>-6.490200</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-3.897050</td>\n",
-       "      <td>6.267000</td>\n",
-       "      <td>-6.635950</td>\n",
-       "      <td>-8.808600</td>\n",
-       "      <td>-8.821800</td>\n",
-       "      <td>-1.763250</td>\n",
-       "      <td>1.752600</td>\n",
-       "      <td>3.754700</td>\n",
-       "      <td>-1.052100</td>\n",
-       "      <td>-9.717150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>BRD-K68232413-001-01-2</td>\n",
-       "      <td>[GABRA1]</td>\n",
-       "      <td>0.126708</td>\n",
-       "      <td>0.166565</td>\n",
-       "      <td>0.583315</td>\n",
-       "      <td>-0.532045</td>\n",
-       "      <td>0.047595</td>\n",
-       "      <td>0.174960</td>\n",
-       "      <td>0.359635</td>\n",
-       "      <td>0.103767</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.428960</td>\n",
-       "      <td>-0.174345</td>\n",
-       "      <td>0.131420</td>\n",
-       "      <td>1.046350</td>\n",
-       "      <td>1.698700</td>\n",
-       "      <td>-0.022657</td>\n",
-       "      <td>-0.170655</td>\n",
-       "      <td>-0.407506</td>\n",
-       "      <td>0.049480</td>\n",
-       "      <td>0.468805</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>BRD-K68488863-001-04-9</td>\n",
-       "      <td>[AURKA, FLT3, KDR, PDGFRA, PTK2, SRC]</td>\n",
-       "      <td>1.437900</td>\n",
-       "      <td>-0.565585</td>\n",
-       "      <td>-0.035150</td>\n",
-       "      <td>0.857750</td>\n",
-       "      <td>-0.824100</td>\n",
-       "      <td>-1.062155</td>\n",
-       "      <td>0.435550</td>\n",
-       "      <td>-2.471550</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.792025</td>\n",
-       "      <td>0.219445</td>\n",
-       "      <td>-0.027473</td>\n",
-       "      <td>-1.568100</td>\n",
-       "      <td>-1.196800</td>\n",
-       "      <td>1.167565</td>\n",
-       "      <td>1.191870</td>\n",
-       "      <td>1.725600</td>\n",
-       "      <td>0.708345</td>\n",
-       "      <td>0.004007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>BRD-K68532323-003-02-8</td>\n",
-       "      <td>[KIF11]</td>\n",
-       "      <td>-2.707550</td>\n",
-       "      <td>2.707100</td>\n",
-       "      <td>-8.441350</td>\n",
-       "      <td>0.735130</td>\n",
-       "      <td>2.538900</td>\n",
-       "      <td>2.048850</td>\n",
-       "      <td>1.948550</td>\n",
-       "      <td>-2.051800</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.277210</td>\n",
-       "      <td>6.326200</td>\n",
-       "      <td>1.095750</td>\n",
-       "      <td>-0.069796</td>\n",
-       "      <td>0.806905</td>\n",
-       "      <td>-0.931870</td>\n",
-       "      <td>-0.132730</td>\n",
-       "      <td>1.866050</td>\n",
-       "      <td>-1.291700</td>\n",
-       "      <td>-1.867900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>BRD-K68552125-001-05-3</td>\n",
-       "      <td>[KCNT2, TRPV4]</td>\n",
-       "      <td>-5.459300</td>\n",
-       "      <td>5.695200</td>\n",
-       "      <td>2.569400</td>\n",
-       "      <td>-1.958950</td>\n",
-       "      <td>7.423450</td>\n",
-       "      <td>3.947450</td>\n",
-       "      <td>0.014595</td>\n",
-       "      <td>-8.178600</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.330120</td>\n",
-       "      <td>-2.802300</td>\n",
-       "      <td>-2.065600</td>\n",
-       "      <td>-7.149400</td>\n",
-       "      <td>-3.275900</td>\n",
-       "      <td>6.976850</td>\n",
-       "      <td>4.534550</td>\n",
-       "      <td>4.842250</td>\n",
-       "      <td>0.854165</td>\n",
-       "      <td>-0.969015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>BRD-K68747584-001-02-0</td>\n",
-       "      <td>[AURKA, AURKB]</td>\n",
-       "      <td>1.629300</td>\n",
-       "      <td>-1.494500</td>\n",
-       "      <td>-7.914850</td>\n",
-       "      <td>-0.827525</td>\n",
-       "      <td>-3.036550</td>\n",
-       "      <td>-3.044650</td>\n",
-       "      <td>0.093925</td>\n",
-       "      <td>-0.221685</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.207195</td>\n",
-       "      <td>8.463450</td>\n",
-       "      <td>-0.705630</td>\n",
-       "      <td>-0.403320</td>\n",
-       "      <td>-0.767400</td>\n",
-       "      <td>-1.273770</td>\n",
-       "      <td>-0.658235</td>\n",
-       "      <td>0.702570</td>\n",
-       "      <td>-1.869850</td>\n",
-       "      <td>-3.957450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>BRD-K68938568-001-01-7</td>\n",
-       "      <td>[BIRC2, BIRC3, BIRC7, XIAP]</td>\n",
-       "      <td>1.021975</td>\n",
-       "      <td>-1.233680</td>\n",
-       "      <td>-0.055290</td>\n",
-       "      <td>0.094870</td>\n",
-       "      <td>-1.508100</td>\n",
-       "      <td>-1.625400</td>\n",
-       "      <td>-0.267168</td>\n",
-       "      <td>0.259418</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.692215</td>\n",
-       "      <td>0.584290</td>\n",
-       "      <td>1.226250</td>\n",
-       "      <td>0.588680</td>\n",
-       "      <td>1.099500</td>\n",
-       "      <td>0.114581</td>\n",
-       "      <td>0.032505</td>\n",
-       "      <td>0.292280</td>\n",
-       "      <td>0.046877</td>\n",
-       "      <td>0.720565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>BRD-K69236721-001-02-7</td>\n",
-       "      <td>[CFTR]</td>\n",
-       "      <td>-0.241255</td>\n",
-       "      <td>-0.167479</td>\n",
-       "      <td>0.922020</td>\n",
-       "      <td>-0.299425</td>\n",
-       "      <td>-0.087035</td>\n",
-       "      <td>0.070245</td>\n",
-       "      <td>-0.097815</td>\n",
-       "      <td>0.433940</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.522105</td>\n",
-       "      <td>-0.051832</td>\n",
-       "      <td>-0.408905</td>\n",
-       "      <td>0.229405</td>\n",
-       "      <td>0.558905</td>\n",
-       "      <td>-0.410785</td>\n",
-       "      <td>0.138148</td>\n",
-       "      <td>0.103984</td>\n",
-       "      <td>0.247400</td>\n",
-       "      <td>0.328564</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>BRD-K70330367-003-07-9</td>\n",
-       "      <td>[DRD2, GRIN2A, GRIN2B, GRIN2C, GRIN2D, GRIN3A]</td>\n",
-       "      <td>0.128625</td>\n",
-       "      <td>0.322145</td>\n",
-       "      <td>0.462110</td>\n",
-       "      <td>0.237160</td>\n",
-       "      <td>0.002715</td>\n",
-       "      <td>0.374645</td>\n",
-       "      <td>0.824375</td>\n",
-       "      <td>-0.490540</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.438780</td>\n",
-       "      <td>-0.304260</td>\n",
-       "      <td>-0.066394</td>\n",
-       "      <td>-0.653895</td>\n",
-       "      <td>-0.862500</td>\n",
-       "      <td>0.080967</td>\n",
-       "      <td>-0.281715</td>\n",
-       "      <td>-0.356915</td>\n",
-       "      <td>0.010415</td>\n",
-       "      <td>0.452110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>BRD-K70358946-001-15-7</td>\n",
-       "      <td>[ADRA1A, ADRA1B, ADRA2A, ADRA2B, ADRA2C, CHRM1...</td>\n",
-       "      <td>0.020480</td>\n",
-       "      <td>0.280960</td>\n",
-       "      <td>0.201028</td>\n",
-       "      <td>-0.177224</td>\n",
-       "      <td>0.141425</td>\n",
-       "      <td>0.452695</td>\n",
-       "      <td>-0.296370</td>\n",
-       "      <td>0.669775</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.226835</td>\n",
-       "      <td>-0.296180</td>\n",
-       "      <td>-1.044930</td>\n",
-       "      <td>-1.262620</td>\n",
-       "      <td>-1.232685</td>\n",
-       "      <td>-0.900705</td>\n",
-       "      <td>-0.230250</td>\n",
-       "      <td>-0.480575</td>\n",
-       "      <td>0.164065</td>\n",
-       "      <td>-0.074795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>BRD-K70401845-003-09-6</td>\n",
-       "      <td>[EGFR, NR1I2]</td>\n",
-       "      <td>2.164250</td>\n",
-       "      <td>-1.853250</td>\n",
-       "      <td>-1.967100</td>\n",
-       "      <td>0.307925</td>\n",
-       "      <td>-1.830400</td>\n",
-       "      <td>-2.712300</td>\n",
-       "      <td>-0.008760</td>\n",
-       "      <td>-0.084900</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.208145</td>\n",
-       "      <td>3.868550</td>\n",
-       "      <td>-0.753250</td>\n",
-       "      <td>0.855255</td>\n",
-       "      <td>-0.335050</td>\n",
-       "      <td>-1.362150</td>\n",
-       "      <td>-1.839250</td>\n",
-       "      <td>-1.343350</td>\n",
-       "      <td>-1.796950</td>\n",
-       "      <td>-4.749500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>BRD-K70463136-001-01-5</td>\n",
-       "      <td>[HIF1A]</td>\n",
-       "      <td>2.191800</td>\n",
-       "      <td>-1.505500</td>\n",
-       "      <td>-3.346450</td>\n",
-       "      <td>-0.483690</td>\n",
-       "      <td>-0.946445</td>\n",
-       "      <td>-2.778650</td>\n",
-       "      <td>0.129450</td>\n",
-       "      <td>-0.047165</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.533510</td>\n",
-       "      <td>2.235500</td>\n",
-       "      <td>-1.789000</td>\n",
-       "      <td>0.236270</td>\n",
-       "      <td>0.108268</td>\n",
-       "      <td>-0.478755</td>\n",
-       "      <td>-0.652825</td>\n",
-       "      <td>-0.025295</td>\n",
-       "      <td>-0.716160</td>\n",
-       "      <td>-0.445430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>BRD-K70557564-305-03-6</td>\n",
-       "      <td>[ABCB1, ABCB4]</td>\n",
-       "      <td>-0.103667</td>\n",
-       "      <td>0.079621</td>\n",
-       "      <td>0.342375</td>\n",
-       "      <td>0.161715</td>\n",
-       "      <td>0.010880</td>\n",
-       "      <td>0.318060</td>\n",
-       "      <td>0.459880</td>\n",
-       "      <td>0.287720</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.262635</td>\n",
-       "      <td>-0.128570</td>\n",
-       "      <td>0.339765</td>\n",
-       "      <td>-0.322085</td>\n",
-       "      <td>-0.470385</td>\n",
-       "      <td>0.113840</td>\n",
-       "      <td>-0.029797</td>\n",
-       "      <td>-0.129276</td>\n",
-       "      <td>0.260420</td>\n",
-       "      <td>0.474815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>BRD-K70778732-003-26-7</td>\n",
-       "      <td>[ADRA1A, ADRA2A, HRH1, HTR1A, HTR2A, HTR2B, HT...</td>\n",
-       "      <td>0.144625</td>\n",
-       "      <td>-0.062230</td>\n",
-       "      <td>0.664970</td>\n",
-       "      <td>-0.153800</td>\n",
-       "      <td>0.008159</td>\n",
-       "      <td>-0.008455</td>\n",
-       "      <td>0.596140</td>\n",
-       "      <td>-0.245270</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.053541</td>\n",
-       "      <td>0.160210</td>\n",
-       "      <td>-0.145157</td>\n",
-       "      <td>0.324945</td>\n",
-       "      <td>-0.047550</td>\n",
-       "      <td>-0.766600</td>\n",
-       "      <td>-0.688035</td>\n",
-       "      <td>-0.784095</td>\n",
-       "      <td>-0.291670</td>\n",
-       "      <td>-0.017363</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>BRD-K70912147-001-01-8</td>\n",
-       "      <td>[SCN4A, SCN9A]</td>\n",
-       "      <td>0.235497</td>\n",
-       "      <td>0.034777</td>\n",
-       "      <td>0.591370</td>\n",
-       "      <td>-0.716795</td>\n",
-       "      <td>-0.023120</td>\n",
-       "      <td>-0.202931</td>\n",
-       "      <td>-0.252567</td>\n",
-       "      <td>0.457522</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.663715</td>\n",
-       "      <td>0.060585</td>\n",
-       "      <td>-0.345720</td>\n",
-       "      <td>0.625860</td>\n",
-       "      <td>0.620355</td>\n",
-       "      <td>-0.573280</td>\n",
-       "      <td>-0.720540</td>\n",
-       "      <td>-0.722265</td>\n",
-       "      <td>-0.257816</td>\n",
-       "      <td>-0.163612</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>BRD-K70914287-300-02-8</td>\n",
-       "      <td>[EGFR, ERBB2]</td>\n",
-       "      <td>0.354524</td>\n",
-       "      <td>-0.275470</td>\n",
-       "      <td>0.235450</td>\n",
-       "      <td>0.692250</td>\n",
-       "      <td>-0.373965</td>\n",
-       "      <td>-0.206835</td>\n",
-       "      <td>-0.821455</td>\n",
-       "      <td>0.339605</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.547135</td>\n",
-       "      <td>0.158863</td>\n",
-       "      <td>-0.795380</td>\n",
-       "      <td>0.593255</td>\n",
-       "      <td>-0.090712</td>\n",
-       "      <td>-0.940430</td>\n",
-       "      <td>-0.449660</td>\n",
-       "      <td>-0.446850</td>\n",
-       "      <td>-0.153649</td>\n",
-       "      <td>-1.377700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>BRD-K71035033-001-07-1</td>\n",
-       "      <td>[FGFR3, KIT, PDGFRA, PDGFRB]</td>\n",
-       "      <td>-0.556740</td>\n",
-       "      <td>0.524400</td>\n",
-       "      <td>0.112045</td>\n",
-       "      <td>-0.086755</td>\n",
-       "      <td>0.361720</td>\n",
-       "      <td>0.433180</td>\n",
-       "      <td>-0.361091</td>\n",
-       "      <td>0.877305</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.455890</td>\n",
-       "      <td>-0.498130</td>\n",
-       "      <td>0.415775</td>\n",
-       "      <td>0.342680</td>\n",
-       "      <td>0.834700</td>\n",
-       "      <td>-0.012443</td>\n",
-       "      <td>0.260045</td>\n",
-       "      <td>0.278225</td>\n",
-       "      <td>0.434905</td>\n",
-       "      <td>0.356610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>BRD-K71221037-001-01-6</td>\n",
-       "      <td>[BIRC2, XIAP]</td>\n",
-       "      <td>1.180020</td>\n",
-       "      <td>-1.258350</td>\n",
-       "      <td>-0.187847</td>\n",
-       "      <td>0.321595</td>\n",
-       "      <td>-1.716150</td>\n",
-       "      <td>-1.286565</td>\n",
-       "      <td>-0.291015</td>\n",
-       "      <td>-0.103765</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.078725</td>\n",
-       "      <td>0.587655</td>\n",
-       "      <td>0.357165</td>\n",
-       "      <td>1.731700</td>\n",
-       "      <td>1.300700</td>\n",
-       "      <td>-1.007465</td>\n",
-       "      <td>-0.671785</td>\n",
-       "      <td>-0.601432</td>\n",
-       "      <td>-0.283855</td>\n",
-       "      <td>-0.337245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>BRD-K71289571-001-11-4</td>\n",
-       "      <td>[CYSLTR1, CYSLTR2]</td>\n",
-       "      <td>-0.080632</td>\n",
-       "      <td>0.217810</td>\n",
-       "      <td>0.450755</td>\n",
-       "      <td>0.038573</td>\n",
-       "      <td>-0.005439</td>\n",
-       "      <td>0.291390</td>\n",
-       "      <td>-0.121175</td>\n",
-       "      <td>0.377335</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.136862</td>\n",
-       "      <td>-0.512265</td>\n",
-       "      <td>0.135083</td>\n",
-       "      <td>0.877580</td>\n",
-       "      <td>1.453550</td>\n",
-       "      <td>-0.203165</td>\n",
-       "      <td>0.493000</td>\n",
-       "      <td>0.407505</td>\n",
-       "      <td>0.513030</td>\n",
-       "      <td>0.204351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>BRD-K71480163-001-01-4</td>\n",
-       "      <td>[AKT1, AKT2, AKT3, PRKG1]</td>\n",
-       "      <td>-0.145263</td>\n",
-       "      <td>0.535385</td>\n",
-       "      <td>-0.187114</td>\n",
-       "      <td>-0.339755</td>\n",
-       "      <td>0.180860</td>\n",
-       "      <td>0.541150</td>\n",
-       "      <td>0.811725</td>\n",
-       "      <td>0.410355</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.005545</td>\n",
-       "      <td>0.634770</td>\n",
-       "      <td>0.648390</td>\n",
-       "      <td>0.172200</td>\n",
-       "      <td>-0.256040</td>\n",
-       "      <td>0.029160</td>\n",
-       "      <td>-1.086205</td>\n",
-       "      <td>-0.910565</td>\n",
-       "      <td>-0.752630</td>\n",
-       "      <td>-0.632420</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>BRD-K72215350-001-06-5</td>\n",
-       "      <td>[GAST]</td>\n",
-       "      <td>0.471630</td>\n",
-       "      <td>-1.353545</td>\n",
-       "      <td>-0.619568</td>\n",
-       "      <td>0.232770</td>\n",
-       "      <td>-2.020750</td>\n",
-       "      <td>-1.169450</td>\n",
-       "      <td>-0.395645</td>\n",
-       "      <td>-0.174520</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.353880</td>\n",
-       "      <td>-0.383021</td>\n",
-       "      <td>-1.919050</td>\n",
-       "      <td>0.422770</td>\n",
-       "      <td>-0.120706</td>\n",
-       "      <td>1.258530</td>\n",
-       "      <td>0.674490</td>\n",
-       "      <td>1.292800</td>\n",
-       "      <td>0.653655</td>\n",
-       "      <td>-2.583750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>BRD-K72222507-003-16-8</td>\n",
-       "      <td>[ACE]</td>\n",
-       "      <td>-0.090870</td>\n",
-       "      <td>-0.014643</td>\n",
-       "      <td>0.221170</td>\n",
-       "      <td>0.346739</td>\n",
-       "      <td>-0.024475</td>\n",
-       "      <td>0.139190</td>\n",
-       "      <td>-0.014600</td>\n",
-       "      <td>0.358470</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.542065</td>\n",
-       "      <td>-0.469180</td>\n",
-       "      <td>1.024750</td>\n",
-       "      <td>-0.181349</td>\n",
-       "      <td>0.000732</td>\n",
-       "      <td>0.653135</td>\n",
-       "      <td>0.029800</td>\n",
-       "      <td>-0.025294</td>\n",
-       "      <td>0.304690</td>\n",
-       "      <td>0.790020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>BRD-K72414522-001-06-7</td>\n",
-       "      <td>[KCNH2]</td>\n",
-       "      <td>-1.042440</td>\n",
-       "      <td>0.744960</td>\n",
-       "      <td>-0.714040</td>\n",
-       "      <td>-0.294695</td>\n",
-       "      <td>1.052550</td>\n",
-       "      <td>-0.049433</td>\n",
-       "      <td>1.171350</td>\n",
-       "      <td>0.047168</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.640910</td>\n",
-       "      <td>4.936850</td>\n",
-       "      <td>0.643810</td>\n",
-       "      <td>-1.311220</td>\n",
-       "      <td>-0.839090</td>\n",
-       "      <td>0.104182</td>\n",
-       "      <td>0.262755</td>\n",
-       "      <td>0.829060</td>\n",
-       "      <td>-0.838548</td>\n",
-       "      <td>-1.612110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>BRD-K73196317-003-14-8</td>\n",
-       "      <td>[HTR1A]</td>\n",
-       "      <td>-0.287335</td>\n",
-       "      <td>0.225135</td>\n",
-       "      <td>0.270234</td>\n",
-       "      <td>-0.457900</td>\n",
-       "      <td>0.082952</td>\n",
-       "      <td>0.383750</td>\n",
-       "      <td>0.416569</td>\n",
-       "      <td>0.216970</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.075717</td>\n",
-       "      <td>0.096258</td>\n",
-       "      <td>0.076012</td>\n",
-       "      <td>0.053776</td>\n",
-       "      <td>0.081934</td>\n",
-       "      <td>-0.248475</td>\n",
-       "      <td>-0.178780</td>\n",
-       "      <td>-0.258555</td>\n",
-       "      <td>0.101564</td>\n",
-       "      <td>0.207023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>BRD-K73237276-001-01-0</td>\n",
-       "      <td>[VDR]</td>\n",
-       "      <td>0.167024</td>\n",
-       "      <td>-0.848375</td>\n",
-       "      <td>0.039545</td>\n",
-       "      <td>0.095122</td>\n",
-       "      <td>-0.920620</td>\n",
-       "      <td>-0.673840</td>\n",
-       "      <td>0.304640</td>\n",
-       "      <td>0.306585</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.102961</td>\n",
-       "      <td>0.581595</td>\n",
-       "      <td>0.404330</td>\n",
-       "      <td>0.219112</td>\n",
-       "      <td>0.647420</td>\n",
-       "      <td>0.442170</td>\n",
-       "      <td>-0.411735</td>\n",
-       "      <td>-0.132086</td>\n",
-       "      <td>-0.138025</td>\n",
-       "      <td>0.498860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>BRD-K73319509-001-08-0</td>\n",
-       "      <td>[MET]</td>\n",
-       "      <td>-0.041595</td>\n",
-       "      <td>0.140935</td>\n",
-       "      <td>0.413410</td>\n",
-       "      <td>-0.599740</td>\n",
-       "      <td>0.054390</td>\n",
-       "      <td>0.263425</td>\n",
-       "      <td>-0.398075</td>\n",
-       "      <td>-0.150935</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.871865</td>\n",
-       "      <td>-0.438220</td>\n",
-       "      <td>0.197356</td>\n",
-       "      <td>0.149885</td>\n",
-       "      <td>-0.220930</td>\n",
-       "      <td>-0.269090</td>\n",
-       "      <td>-0.219415</td>\n",
-       "      <td>-0.250125</td>\n",
-       "      <td>0.174480</td>\n",
-       "      <td>0.223719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>BRD-K74363950-004-01-0</td>\n",
-       "      <td>[CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]</td>\n",
-       "      <td>0.026877</td>\n",
-       "      <td>-0.165650</td>\n",
-       "      <td>-0.181990</td>\n",
-       "      <td>-0.143430</td>\n",
-       "      <td>0.013598</td>\n",
-       "      <td>-0.078052</td>\n",
-       "      <td>-0.373260</td>\n",
-       "      <td>0.311305</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.518620</td>\n",
-       "      <td>-0.562750</td>\n",
-       "      <td>0.657090</td>\n",
-       "      <td>0.748865</td>\n",
-       "      <td>0.076081</td>\n",
-       "      <td>0.037513</td>\n",
-       "      <td>0.509250</td>\n",
-       "      <td>0.393450</td>\n",
-       "      <td>0.653660</td>\n",
-       "      <td>-0.168288</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>BRD-K75958547-238-01-0</td>\n",
-       "      <td>[HMGCR]</td>\n",
-       "      <td>1.245290</td>\n",
-       "      <td>0.258995</td>\n",
-       "      <td>-0.000370</td>\n",
-       "      <td>-0.254469</td>\n",
-       "      <td>0.756080</td>\n",
-       "      <td>-0.427330</td>\n",
-       "      <td>-0.093925</td>\n",
-       "      <td>-0.094336</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.705540</td>\n",
-       "      <td>-0.976735</td>\n",
-       "      <td>-0.286188</td>\n",
-       "      <td>-0.855840</td>\n",
-       "      <td>-1.180700</td>\n",
-       "      <td>0.574950</td>\n",
-       "      <td>0.999570</td>\n",
-       "      <td>0.727890</td>\n",
-       "      <td>0.763035</td>\n",
-       "      <td>-0.487505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>BRD-K76908866-001-07-6</td>\n",
-       "      <td>[ERBB2]</td>\n",
-       "      <td>0.557385</td>\n",
-       "      <td>-0.543620</td>\n",
-       "      <td>-0.594665</td>\n",
-       "      <td>0.712370</td>\n",
-       "      <td>-1.068870</td>\n",
-       "      <td>-0.406515</td>\n",
-       "      <td>-0.352330</td>\n",
-       "      <td>-0.108482</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.502780</td>\n",
-       "      <td>0.071355</td>\n",
-       "      <td>0.265125</td>\n",
-       "      <td>0.139015</td>\n",
-       "      <td>-0.204103</td>\n",
-       "      <td>-0.033055</td>\n",
-       "      <td>-0.398195</td>\n",
-       "      <td>-0.199536</td>\n",
-       "      <td>-0.174484</td>\n",
-       "      <td>-0.556290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>BRD-K77908580-001-09-6</td>\n",
-       "      <td>[HDAC1, HDAC2, HDAC3, HDAC9]</td>\n",
-       "      <td>1.027085</td>\n",
-       "      <td>-0.005490</td>\n",
-       "      <td>-3.732000</td>\n",
-       "      <td>-0.393795</td>\n",
-       "      <td>0.006799</td>\n",
-       "      <td>-0.592535</td>\n",
-       "      <td>-0.100737</td>\n",
-       "      <td>0.136787</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-1.920500</td>\n",
-       "      <td>-1.007045</td>\n",
-       "      <td>-1.197430</td>\n",
-       "      <td>-0.853550</td>\n",
-       "      <td>-1.547250</td>\n",
-       "      <td>-0.178835</td>\n",
-       "      <td>1.934100</td>\n",
-       "      <td>1.894200</td>\n",
-       "      <td>1.617250</td>\n",
-       "      <td>-1.901300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>BRD-K79254416-001-22-6</td>\n",
-       "      <td>[DNMT1, DNMT3A]</td>\n",
-       "      <td>-1.382900</td>\n",
-       "      <td>1.577800</td>\n",
-       "      <td>-3.091250</td>\n",
-       "      <td>-0.195245</td>\n",
-       "      <td>0.494990</td>\n",
-       "      <td>1.462800</td>\n",
-       "      <td>-0.750890</td>\n",
-       "      <td>1.943300</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.280694</td>\n",
-       "      <td>1.288375</td>\n",
-       "      <td>0.900240</td>\n",
-       "      <td>1.921100</td>\n",
-       "      <td>1.718450</td>\n",
-       "      <td>-1.650930</td>\n",
-       "      <td>-0.316930</td>\n",
-       "      <td>-0.297900</td>\n",
-       "      <td>-0.499990</td>\n",
-       "      <td>-3.373800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>BRD-K80700417-001-04-2</td>\n",
-       "      <td>[FLT3, PIM1, PIM2, PIM3]</td>\n",
-       "      <td>-0.637375</td>\n",
-       "      <td>0.498775</td>\n",
-       "      <td>-0.002929</td>\n",
-       "      <td>0.106054</td>\n",
-       "      <td>0.087033</td>\n",
-       "      <td>0.398060</td>\n",
-       "      <td>0.169837</td>\n",
-       "      <td>0.382055</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.628235</td>\n",
-       "      <td>-0.427445</td>\n",
-       "      <td>-0.059985</td>\n",
-       "      <td>0.288905</td>\n",
-       "      <td>0.085592</td>\n",
-       "      <td>-0.503085</td>\n",
-       "      <td>-0.243790</td>\n",
-       "      <td>-0.553645</td>\n",
-       "      <td>-0.088544</td>\n",
-       "      <td>0.063442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>BRD-K81258678-001-01-0</td>\n",
-       "      <td>[RELA]</td>\n",
-       "      <td>-0.290530</td>\n",
-       "      <td>2.601900</td>\n",
-       "      <td>0.852815</td>\n",
-       "      <td>0.241650</td>\n",
-       "      <td>5.584950</td>\n",
-       "      <td>2.324650</td>\n",
-       "      <td>2.234700</td>\n",
-       "      <td>-4.466700</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-5.285650</td>\n",
-       "      <td>13.551000</td>\n",
-       "      <td>-1.326100</td>\n",
-       "      <td>4.612750</td>\n",
-       "      <td>5.419350</td>\n",
-       "      <td>5.724250</td>\n",
-       "      <td>-8.781900</td>\n",
-       "      <td>-5.519550</td>\n",
-       "      <td>-9.482150</td>\n",
-       "      <td>-11.058500</td>\n",
+       "      <td>-1.495100</td>\n",
+       "      <td>-0.867225</td>\n",
+       "      <td>-0.066115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>BRD-K81957469-001-01-0</td>\n",
-       "      <td>[PSEN1]</td>\n",
-       "      <td>-1.073170</td>\n",
-       "      <td>1.045150</td>\n",
-       "      <td>0.311975</td>\n",
-       "      <td>-0.457700</td>\n",
-       "      <td>1.278300</td>\n",
-       "      <td>1.064075</td>\n",
-       "      <td>0.403425</td>\n",
-       "      <td>0.377335</td>\n",
+       "      <th>1</th>\n",
+       "      <td>BRD-A69815203-001-07-6</td>\n",
+       "      <td>[ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...</td>\n",
+       "      <td>2.487450</td>\n",
+       "      <td>-2.872750</td>\n",
+       "      <td>0.616635</td>\n",
+       "      <td>-0.451942</td>\n",
+       "      <td>-2.260100</td>\n",
+       "      <td>-3.300900</td>\n",
+       "      <td>0.316320</td>\n",
+       "      <td>-1.825400</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.010772</td>\n",
-       "      <td>-0.308305</td>\n",
-       "      <td>-0.007785</td>\n",
-       "      <td>-0.863870</td>\n",
-       "      <td>-0.750570</td>\n",
-       "      <td>-0.602995</td>\n",
-       "      <td>0.371105</td>\n",
-       "      <td>0.559265</td>\n",
-       "      <td>0.471365</td>\n",
-       "      <td>0.064110</td>\n",
+       "      <td>-2.681800</td>\n",
+       "      <td>-0.197230</td>\n",
+       "      <td>-4.717350</td>\n",
+       "      <td>0.644170</td>\n",
+       "      <td>1.324100</td>\n",
+       "      <td>0.103070</td>\n",
+       "      <td>0.986025</td>\n",
+       "      <td>1.346200</td>\n",
+       "      <td>0.773450</td>\n",
+       "      <td>-2.749350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>BRD-K82135108-001-04-3</td>\n",
-       "      <td>[HSPA1A]</td>\n",
-       "      <td>0.803115</td>\n",
-       "      <td>0.329465</td>\n",
-       "      <td>-3.880350</td>\n",
-       "      <td>-0.744140</td>\n",
-       "      <td>0.645912</td>\n",
-       "      <td>-1.757450</td>\n",
-       "      <td>-0.694445</td>\n",
-       "      <td>-4.429020</td>\n",
+       "      <th>2</th>\n",
+       "      <td>BRD-A70858459-001-01-7</td>\n",
+       "      <td>[ESR1, ESR2, MAP1A, MAP2]</td>\n",
+       "      <td>-0.920210</td>\n",
+       "      <td>1.461550</td>\n",
+       "      <td>0.445630</td>\n",
+       "      <td>-0.394235</td>\n",
+       "      <td>1.528450</td>\n",
+       "      <td>1.116100</td>\n",
+       "      <td>-0.054990</td>\n",
+       "      <td>1.061270</td>\n",
        "      <td>...</td>\n",
-       "      <td>-3.787800</td>\n",
-       "      <td>3.698950</td>\n",
-       "      <td>-4.905050</td>\n",
-       "      <td>0.290050</td>\n",
-       "      <td>0.425045</td>\n",
-       "      <td>0.344295</td>\n",
-       "      <td>-1.414005</td>\n",
-       "      <td>0.488985</td>\n",
-       "      <td>-3.562550</td>\n",
-       "      <td>-1.630150</td>\n",
+       "      <td>0.238875</td>\n",
+       "      <td>0.326475</td>\n",
+       "      <td>0.064563</td>\n",
+       "      <td>0.187646</td>\n",
+       "      <td>0.200447</td>\n",
+       "      <td>-0.695660</td>\n",
+       "      <td>0.100225</td>\n",
+       "      <td>0.401885</td>\n",
+       "      <td>0.114583</td>\n",
+       "      <td>-0.245753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>BRD-K82677201-001-01-6</td>\n",
-       "      <td>[ATP1A1]</td>\n",
-       "      <td>1.137800</td>\n",
-       "      <td>-0.619580</td>\n",
-       "      <td>-1.351550</td>\n",
-       "      <td>0.202985</td>\n",
-       "      <td>-0.658175</td>\n",
-       "      <td>-0.890450</td>\n",
-       "      <td>-0.177140</td>\n",
-       "      <td>0.646190</td>\n",
+       "      <th>3</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>[HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...</td>\n",
+       "      <td>0.045435</td>\n",
+       "      <td>0.099755</td>\n",
+       "      <td>0.103628</td>\n",
+       "      <td>0.592620</td>\n",
+       "      <td>-0.352200</td>\n",
+       "      <td>0.202930</td>\n",
+       "      <td>-0.059855</td>\n",
+       "      <td>-0.353755</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.345960</td>\n",
-       "      <td>0.582945</td>\n",
-       "      <td>-0.146985</td>\n",
-       "      <td>-0.677920</td>\n",
-       "      <td>-1.008060</td>\n",
-       "      <td>0.059615</td>\n",
-       "      <td>-0.268170</td>\n",
-       "      <td>-0.033725</td>\n",
-       "      <td>-0.158858</td>\n",
-       "      <td>-0.046745</td>\n",
+       "      <td>1.069575</td>\n",
+       "      <td>-0.475915</td>\n",
+       "      <td>-0.174002</td>\n",
+       "      <td>0.217965</td>\n",
+       "      <td>0.090715</td>\n",
+       "      <td>-0.154695</td>\n",
+       "      <td>0.165235</td>\n",
+       "      <td>-0.160191</td>\n",
+       "      <td>0.242195</td>\n",
+       "      <td>-0.126886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>BRD-K82746043-001-15-1</td>\n",
-       "      <td>[BCL2, BCL2L1, BCL2L2]</td>\n",
-       "      <td>0.154225</td>\n",
-       "      <td>-1.293200</td>\n",
-       "      <td>-2.563600</td>\n",
-       "      <td>-0.018505</td>\n",
-       "      <td>-1.728350</td>\n",
-       "      <td>-0.999075</td>\n",
-       "      <td>-0.328488</td>\n",
-       "      <td>0.127351</td>\n",
+       "      <th>4</th>\n",
+       "      <td>BRD-A73368467-003-17-6</td>\n",
+       "      <td>[HRH1]</td>\n",
+       "      <td>-0.062074</td>\n",
+       "      <td>-0.314820</td>\n",
+       "      <td>0.526190</td>\n",
+       "      <td>-0.502485</td>\n",
+       "      <td>-0.444675</td>\n",
+       "      <td>-0.191225</td>\n",
+       "      <td>0.145019</td>\n",
+       "      <td>0.018870</td>\n",
        "      <td>...</td>\n",
-       "      <td>-7.051600</td>\n",
-       "      <td>2.185700</td>\n",
-       "      <td>1.613200</td>\n",
-       "      <td>-1.670500</td>\n",
-       "      <td>-1.670150</td>\n",
-       "      <td>1.785400</td>\n",
-       "      <td>-3.656900</td>\n",
-       "      <td>-3.720950</td>\n",
-       "      <td>-3.130250</td>\n",
-       "      <td>1.099200</td>\n",
+       "      <td>0.527805</td>\n",
+       "      <td>-1.204250</td>\n",
+       "      <td>0.615420</td>\n",
+       "      <td>-0.187645</td>\n",
+       "      <td>0.321880</td>\n",
+       "      <td>1.013235</td>\n",
+       "      <td>0.793675</td>\n",
+       "      <td>0.682925</td>\n",
+       "      <td>1.075500</td>\n",
+       "      <td>0.844115</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>51 rows × 495 columns</p>\n",
+       "<p>5 rows × 495 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Metadata_broad_sample                                    Metadata_target  \\\n",
-       "1   BRD-A69636825-003-04-7  [CACNA1C, CACNA1S, CACNA2D1, CACNG1, HTR3A, KC...   \n",
-       "2   BRD-A69815203-001-07-6  [ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...   \n",
-       "3   BRD-A70858459-001-01-7                          [ESR1, ESR2, MAP1A, MAP2]   \n",
-       "4   BRD-A72309220-001-04-1  [HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...   \n",
-       "6   BRD-A73368467-003-17-6                                             [HRH1]   \n",
-       "8   BRD-A81233518-004-16-1                [CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]   \n",
-       "9   BRD-A82035391-001-02-7                                    [AVPR1A, AVPR2]   \n",
-       "10  BRD-A82156122-001-01-9                                             [DPP4]   \n",
-       "11  BRD-K50691590-001-02-2  [PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSM...   \n",
-       "12  BRD-K60230970-001-10-0                                            [PSMB1]   \n",
-       "13  BRD-K67789209-001-01-0                                     [HTR1A, HTR2A]   \n",
-       "14  BRD-K67844266-003-01-9                                       [NAE1, UBA3]   \n",
-       "15  BRD-K68103045-001-02-9   [GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6]   \n",
-       "16  BRD-K68132782-003-13-8                                             [SQLE]   \n",
-       "17  BRD-K68164687-001-01-6                                             [TUBB]   \n",
-       "18  BRD-K68232413-001-01-2                                           [GABRA1]   \n",
-       "19  BRD-K68488863-001-04-9              [AURKA, FLT3, KDR, PDGFRA, PTK2, SRC]   \n",
-       "20  BRD-K68532323-003-02-8                                            [KIF11]   \n",
-       "21  BRD-K68552125-001-05-3                                     [KCNT2, TRPV4]   \n",
-       "23  BRD-K68747584-001-02-0                                     [AURKA, AURKB]   \n",
-       "24  BRD-K68938568-001-01-7                        [BIRC2, BIRC3, BIRC7, XIAP]   \n",
-       "25  BRD-K69236721-001-02-7                                             [CFTR]   \n",
-       "27  BRD-K70330367-003-07-9     [DRD2, GRIN2A, GRIN2B, GRIN2C, GRIN2D, GRIN3A]   \n",
-       "28  BRD-K70358946-001-15-7  [ADRA1A, ADRA1B, ADRA2A, ADRA2B, ADRA2C, CHRM1...   \n",
-       "29  BRD-K70401845-003-09-6                                      [EGFR, NR1I2]   \n",
-       "30  BRD-K70463136-001-01-5                                            [HIF1A]   \n",
-       "31  BRD-K70557564-305-03-6                                     [ABCB1, ABCB4]   \n",
-       "32  BRD-K70778732-003-26-7  [ADRA1A, ADRA2A, HRH1, HTR1A, HTR2A, HTR2B, HT...   \n",
-       "33  BRD-K70912147-001-01-8                                     [SCN4A, SCN9A]   \n",
-       "34  BRD-K70914287-300-02-8                                      [EGFR, ERBB2]   \n",
-       "35  BRD-K71035033-001-07-1                       [FGFR3, KIT, PDGFRA, PDGFRB]   \n",
-       "36  BRD-K71221037-001-01-6                                      [BIRC2, XIAP]   \n",
-       "38  BRD-K71289571-001-11-4                                 [CYSLTR1, CYSLTR2]   \n",
-       "39  BRD-K71480163-001-01-4                          [AKT1, AKT2, AKT3, PRKG1]   \n",
-       "40  BRD-K72215350-001-06-5                                             [GAST]   \n",
-       "41  BRD-K72222507-003-16-8                                              [ACE]   \n",
-       "42  BRD-K72414522-001-06-7                                            [KCNH2]   \n",
-       "43  BRD-K73196317-003-14-8                                            [HTR1A]   \n",
-       "44  BRD-K73237276-001-01-0                                              [VDR]   \n",
-       "45  BRD-K73319509-001-08-0                                              [MET]   \n",
-       "46  BRD-K74363950-004-01-0                [CHRM1, CHRM2, CHRM3, CHRM4, CHRM5]   \n",
-       "47  BRD-K75958547-238-01-0                                            [HMGCR]   \n",
-       "48  BRD-K76908866-001-07-6                                            [ERBB2]   \n",
-       "49  BRD-K77908580-001-09-6                       [HDAC1, HDAC2, HDAC3, HDAC9]   \n",
-       "50  BRD-K79254416-001-22-6                                    [DNMT1, DNMT3A]   \n",
-       "51  BRD-K80700417-001-04-2                           [FLT3, PIM1, PIM2, PIM3]   \n",
-       "53  BRD-K81258678-001-01-0                                             [RELA]   \n",
-       "54  BRD-K81957469-001-01-0                                            [PSEN1]   \n",
-       "55  BRD-K82135108-001-04-3                                           [HSPA1A]   \n",
-       "56  BRD-K82677201-001-01-6                                           [ATP1A1]   \n",
-       "57  BRD-K82746043-001-15-1                             [BCL2, BCL2L1, BCL2L2]   \n",
+       "    Metadata_broad_sample                                    Metadata_target  \\\n",
+       "0  BRD-A69636825-003-04-7  [CACNA1C, CACNA1S, CACNA2D1, CACNG1, HTR3A, KC...   \n",
+       "1  BRD-A69815203-001-07-6  [ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...   \n",
+       "2  BRD-A70858459-001-01-7                          [ESR1, ESR2, MAP1A, MAP2]   \n",
+       "3  BRD-A72309220-001-04-1  [HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...   \n",
+       "4  BRD-A73368467-003-17-6                                             [HRH1]   \n",
        "\n",
-       "    Cells_AreaShape_Eccentricity  Cells_AreaShape_Extent  \\\n",
-       "1                      -0.326365                0.651610   \n",
-       "2                       2.487450               -2.872750   \n",
-       "3                      -0.920210                1.461550   \n",
-       "4                       0.045435                0.099755   \n",
-       "6                      -0.062074               -0.314820   \n",
-       "8                      -0.612415               -0.128128   \n",
-       "9                      -0.300770                0.132704   \n",
-       "10                     -0.229737               -0.045755   \n",
-       "11                      7.777100               -2.558850   \n",
-       "12                     -4.508950               10.006400   \n",
-       "13                      0.052475               -0.040268   \n",
-       "14                      0.077450                3.715650   \n",
-       "15                      0.245735               -0.142770   \n",
-       "16                     -0.115830               -0.127212   \n",
-       "17                      1.410420                2.547000   \n",
-       "18                      0.126708                0.166565   \n",
-       "19                      1.437900               -0.565585   \n",
-       "20                     -2.707550                2.707100   \n",
-       "21                     -5.459300                5.695200   \n",
-       "23                      1.629300               -1.494500   \n",
-       "24                      1.021975               -1.233680   \n",
-       "25                     -0.241255               -0.167479   \n",
-       "27                      0.128625                0.322145   \n",
-       "28                      0.020480                0.280960   \n",
-       "29                      2.164250               -1.853250   \n",
-       "30                      2.191800               -1.505500   \n",
-       "31                     -0.103667                0.079621   \n",
-       "32                      0.144625               -0.062230   \n",
-       "33                      0.235497                0.034777   \n",
-       "34                      0.354524               -0.275470   \n",
-       "35                     -0.556740                0.524400   \n",
-       "36                      1.180020               -1.258350   \n",
-       "38                     -0.080632                0.217810   \n",
-       "39                     -0.145263                0.535385   \n",
-       "40                      0.471630               -1.353545   \n",
-       "41                     -0.090870               -0.014643   \n",
-       "42                     -1.042440                0.744960   \n",
-       "43                     -0.287335                0.225135   \n",
-       "44                      0.167024               -0.848375   \n",
-       "45                     -0.041595                0.140935   \n",
-       "46                      0.026877               -0.165650   \n",
-       "47                      1.245290                0.258995   \n",
-       "48                      0.557385               -0.543620   \n",
-       "49                      1.027085               -0.005490   \n",
-       "50                     -1.382900                1.577800   \n",
-       "51                     -0.637375                0.498775   \n",
-       "53                     -0.290530                2.601900   \n",
-       "54                     -1.073170                1.045150   \n",
-       "55                      0.803115                0.329465   \n",
-       "56                      1.137800               -0.619580   \n",
-       "57                      0.154225               -1.293200   \n",
+       "   Cells_AreaShape_Eccentricity  Cells_AreaShape_Extent  \\\n",
+       "0                     -0.326365                0.651610   \n",
+       "1                      2.487450               -2.872750   \n",
+       "2                     -0.920210                1.461550   \n",
+       "3                      0.045435                0.099755   \n",
+       "4                     -0.062074               -0.314820   \n",
        "\n",
-       "    Cells_AreaShape_FormFactor  Cells_AreaShape_Orientation  \\\n",
-       "1                     0.211280                     0.092412   \n",
-       "2                     0.616635                    -0.451942   \n",
-       "3                     0.445630                    -0.394235   \n",
-       "4                     0.103628                     0.592620   \n",
-       "6                     0.526190                    -0.502485   \n",
-       "8                    -0.741865                     0.178005   \n",
-       "9                    -0.619200                     0.053390   \n",
-       "10                   -1.031130                     0.078795   \n",
-       "11                   -0.169902                    -0.768970   \n",
-       "12                    2.551850                    -0.706140   \n",
-       "13                    0.666435                     0.548785   \n",
-       "14                   -1.087885                     0.005565   \n",
-       "15                    0.757980                     0.636800   \n",
-       "16                    0.552555                     0.065825   \n",
-       "17                   -2.946600                    -0.618420   \n",
-       "18                    0.583315                    -0.532045   \n",
-       "19                   -0.035150                     0.857750   \n",
-       "20                   -8.441350                     0.735130   \n",
-       "21                    2.569400                    -1.958950   \n",
-       "23                   -7.914850                    -0.827525   \n",
-       "24                   -0.055290                     0.094870   \n",
-       "25                    0.922020                    -0.299425   \n",
-       "27                    0.462110                     0.237160   \n",
-       "28                    0.201028                    -0.177224   \n",
-       "29                   -1.967100                     0.307925   \n",
-       "30                   -3.346450                    -0.483690   \n",
-       "31                    0.342375                     0.161715   \n",
-       "32                    0.664970                    -0.153800   \n",
-       "33                    0.591370                    -0.716795   \n",
-       "34                    0.235450                     0.692250   \n",
-       "35                    0.112045                    -0.086755   \n",
-       "36                   -0.187847                     0.321595   \n",
-       "38                    0.450755                     0.038573   \n",
-       "39                   -0.187114                    -0.339755   \n",
-       "40                   -0.619568                     0.232770   \n",
-       "41                    0.221170                     0.346739   \n",
-       "42                   -0.714040                    -0.294695   \n",
-       "43                    0.270234                    -0.457900   \n",
-       "44                    0.039545                     0.095122   \n",
-       "45                    0.413410                    -0.599740   \n",
-       "46                   -0.181990                    -0.143430   \n",
-       "47                   -0.000370                    -0.254469   \n",
-       "48                   -0.594665                     0.712370   \n",
-       "49                   -3.732000                    -0.393795   \n",
-       "50                   -3.091250                    -0.195245   \n",
-       "51                   -0.002929                     0.106054   \n",
-       "53                    0.852815                     0.241650   \n",
-       "54                    0.311975                    -0.457700   \n",
-       "55                   -3.880350                    -0.744140   \n",
-       "56                   -1.351550                     0.202985   \n",
-       "57                   -2.563600                    -0.018505   \n",
+       "   Cells_AreaShape_FormFactor  Cells_AreaShape_Orientation  \\\n",
+       "0                    0.211280                     0.092412   \n",
+       "1                    0.616635                    -0.451942   \n",
+       "2                    0.445630                    -0.394235   \n",
+       "3                    0.103628                     0.592620   \n",
+       "4                    0.526190                    -0.502485   \n",
        "\n",
-       "    Cells_AreaShape_Solidity  Cells_AreaShape_Zernike_0_0  \\\n",
-       "1                   0.456915                     0.486515   \n",
-       "2                  -2.260100                    -3.300900   \n",
-       "3                   1.528450                     1.116100   \n",
-       "4                  -0.352200                     0.202930   \n",
-       "6                  -0.444675                    -0.191225   \n",
-       "8                   0.006800                     0.282935   \n",
-       "9                  -0.329085                     0.107972   \n",
-       "10                 -0.541225                     0.393505   \n",
-       "11                  6.395400                    -7.443450   \n",
-       "12                 13.484500                     6.264200   \n",
-       "13                  0.178140                     0.230900   \n",
-       "14                  4.808450                     0.042300   \n",
-       "15                 -0.116950                    -0.329765   \n",
-       "16                 -0.123745                    -0.011705   \n",
-       "17                  3.138550                    -0.396105   \n",
-       "18                  0.047595                     0.174960   \n",
-       "19                 -0.824100                    -1.062155   \n",
-       "20                  2.538900                     2.048850   \n",
-       "21                  7.423450                     3.947450   \n",
-       "23                 -3.036550                    -3.044650   \n",
-       "24                 -1.508100                    -1.625400   \n",
-       "25                 -0.087035                     0.070245   \n",
-       "27                  0.002715                     0.374645   \n",
-       "28                  0.141425                     0.452695   \n",
-       "29                 -1.830400                    -2.712300   \n",
-       "30                 -0.946445                    -2.778650   \n",
-       "31                  0.010880                     0.318060   \n",
-       "32                  0.008159                    -0.008455   \n",
-       "33                 -0.023120                    -0.202931   \n",
-       "34                 -0.373965                    -0.206835   \n",
-       "35                  0.361720                     0.433180   \n",
-       "36                 -1.716150                    -1.286565   \n",
-       "38                 -0.005439                     0.291390   \n",
-       "39                  0.180860                     0.541150   \n",
-       "40                 -2.020750                    -1.169450   \n",
-       "41                 -0.024475                     0.139190   \n",
-       "42                  1.052550                    -0.049433   \n",
-       "43                  0.082952                     0.383750   \n",
-       "44                 -0.920620                    -0.673840   \n",
-       "45                  0.054390                     0.263425   \n",
-       "46                  0.013598                    -0.078052   \n",
-       "47                  0.756080                    -0.427330   \n",
-       "48                 -1.068870                    -0.406515   \n",
-       "49                  0.006799                    -0.592535   \n",
-       "50                  0.494990                     1.462800   \n",
-       "51                  0.087033                     0.398060   \n",
-       "53                  5.584950                     2.324650   \n",
-       "54                  1.278300                     1.064075   \n",
-       "55                  0.645912                    -1.757450   \n",
-       "56                 -0.658175                    -0.890450   \n",
-       "57                 -1.728350                    -0.999075   \n",
+       "   Cells_AreaShape_Solidity  Cells_AreaShape_Zernike_0_0  \\\n",
+       "0                  0.456915                     0.486515   \n",
+       "1                 -2.260100                    -3.300900   \n",
+       "2                  1.528450                     1.116100   \n",
+       "3                 -0.352200                     0.202930   \n",
+       "4                 -0.444675                    -0.191225   \n",
        "\n",
-       "    Cells_AreaShape_Zernike_1_1  Cells_AreaShape_Zernike_2_0  ...  \\\n",
-       "1                      0.435545                     0.863160  ...   \n",
-       "2                      0.316320                    -1.825400  ...   \n",
-       "3                     -0.054990                     1.061270  ...   \n",
-       "4                     -0.059855                    -0.353755  ...   \n",
-       "6                      0.145019                     0.018870  ...   \n",
-       "8                      0.587865                     0.561290  ...   \n",
-       "9                      0.157675                     0.834860  ...   \n",
-       "10                    -0.494920                     0.976335  ...   \n",
-       "11                    -7.568300                   -21.230000  ...   \n",
-       "12                    -5.839750                   -11.688000  ...   \n",
-       "13                    -0.015085                    -0.344318  ...   \n",
-       "14                    -0.246730                    -1.929100  ...   \n",
-       "15                    -0.183954                    -0.042453  ...   \n",
-       "16                     0.427760                    -0.108485  ...   \n",
-       "17                     1.149465                    -6.490200  ...   \n",
-       "18                     0.359635                     0.103767  ...   \n",
-       "19                     0.435550                    -2.471550  ...   \n",
-       "20                     1.948550                    -2.051800  ...   \n",
-       "21                     0.014595                    -8.178600  ...   \n",
-       "23                     0.093925                    -0.221685  ...   \n",
-       "24                    -0.267168                     0.259418  ...   \n",
-       "25                    -0.097815                     0.433940  ...   \n",
-       "27                     0.824375                    -0.490540  ...   \n",
-       "28                    -0.296370                     0.669775  ...   \n",
-       "29                    -0.008760                    -0.084900  ...   \n",
-       "30                     0.129450                    -0.047165  ...   \n",
-       "31                     0.459880                     0.287720  ...   \n",
-       "32                     0.596140                    -0.245270  ...   \n",
-       "33                    -0.252567                     0.457522  ...   \n",
-       "34                    -0.821455                     0.339605  ...   \n",
-       "35                    -0.361091                     0.877305  ...   \n",
-       "36                    -0.291015                    -0.103765  ...   \n",
-       "38                    -0.121175                     0.377335  ...   \n",
-       "39                     0.811725                     0.410355  ...   \n",
-       "40                    -0.395645                    -0.174520  ...   \n",
-       "41                    -0.014600                     0.358470  ...   \n",
-       "42                     1.171350                     0.047168  ...   \n",
-       "43                     0.416569                     0.216970  ...   \n",
-       "44                     0.304640                     0.306585  ...   \n",
-       "45                    -0.398075                    -0.150935  ...   \n",
-       "46                    -0.373260                     0.311305  ...   \n",
-       "47                    -0.093925                    -0.094336  ...   \n",
-       "48                    -0.352330                    -0.108482  ...   \n",
-       "49                    -0.100737                     0.136787  ...   \n",
-       "50                    -0.750890                     1.943300  ...   \n",
-       "51                     0.169837                     0.382055  ...   \n",
-       "53                     2.234700                    -4.466700  ...   \n",
-       "54                     0.403425                     0.377335  ...   \n",
-       "55                    -0.694445                    -4.429020  ...   \n",
-       "56                    -0.177140                     0.646190  ...   \n",
-       "57                    -0.328488                     0.127351  ...   \n",
+       "   Cells_AreaShape_Zernike_1_1  Cells_AreaShape_Zernike_2_0  ...  \\\n",
+       "0                     0.435545                     0.863160  ...   \n",
+       "1                     0.316320                    -1.825400  ...   \n",
+       "2                    -0.054990                     1.061270  ...   \n",
+       "3                    -0.059855                    -0.353755  ...   \n",
+       "4                     0.145019                     0.018870  ...   \n",
        "\n",
-       "    Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
-       "1                                         0.175200   \n",
-       "2                                        -2.681800   \n",
-       "3                                         0.238875   \n",
-       "4                                         1.069575   \n",
-       "6                                         0.527805   \n",
-       "8                                         0.659600   \n",
-       "9                                        -1.682600   \n",
-       "10                                        0.154289   \n",
-       "11                                       -5.809700   \n",
-       "12                                       -5.048700   \n",
-       "13                                        1.077800   \n",
-       "14                                        0.102012   \n",
-       "15                                       -0.590850   \n",
-       "16                                       -0.463495   \n",
-       "17                                       -3.897050   \n",
-       "18                                        0.428960   \n",
-       "19                                        0.792025   \n",
-       "20                                       -0.277210   \n",
-       "21                                       -0.330120   \n",
-       "23                                        0.207195   \n",
-       "24                                       -0.692215   \n",
-       "25                                       -0.522105   \n",
-       "27                                        0.438780   \n",
-       "28                                        0.226835   \n",
-       "29                                       -0.208145   \n",
-       "30                                       -0.533510   \n",
-       "31                                       -0.262635   \n",
-       "32                                       -0.053541   \n",
-       "33                                        0.663715   \n",
-       "34                                        0.547135   \n",
-       "35                                        0.455890   \n",
-       "36                                        1.078725   \n",
-       "38                                        0.136862   \n",
-       "39                                        1.005545   \n",
-       "40                                        0.353880   \n",
-       "41                                        0.542065   \n",
-       "42                                        0.640910   \n",
-       "43                                       -0.075717   \n",
-       "44                                        0.102961   \n",
-       "45                                        0.871865   \n",
-       "46                                       -0.518620   \n",
-       "47                                       -0.705540   \n",
-       "48                                        0.502780   \n",
-       "49                                       -1.920500   \n",
-       "50                                        0.280694   \n",
-       "51                                        0.628235   \n",
-       "53                                       -5.285650   \n",
-       "54                                       -0.010772   \n",
-       "55                                       -3.787800   \n",
-       "56                                        0.345960   \n",
-       "57                                       -7.051600   \n",
+       "   Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "0                                        0.175200   \n",
+       "1                                       -2.681800   \n",
+       "2                                        0.238875   \n",
+       "3                                        1.069575   \n",
+       "4                                        0.527805   \n",
        "\n",
-       "    Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
-       "1                                          0.557360   \n",
-       "2                                         -0.197230   \n",
-       "3                                          0.326475   \n",
-       "4                                         -0.475915   \n",
-       "6                                         -1.204250   \n",
-       "8                                         -0.702090   \n",
-       "9                                          0.840775   \n",
-       "10                                         0.216080   \n",
-       "11                                         0.732385   \n",
-       "12                                         0.381675   \n",
-       "13                                        -0.781525   \n",
-       "14                                         1.663355   \n",
-       "15                                        -0.164918   \n",
-       "16                                         0.093568   \n",
-       "17                                         6.267000   \n",
-       "18                                        -0.174345   \n",
-       "19                                         0.219445   \n",
-       "20                                         6.326200   \n",
-       "21                                        -2.802300   \n",
-       "23                                         8.463450   \n",
-       "24                                         0.584290   \n",
-       "25                                        -0.051832   \n",
-       "27                                        -0.304260   \n",
-       "28                                        -0.296180   \n",
-       "29                                         3.868550   \n",
-       "30                                         2.235500   \n",
-       "31                                        -0.128570   \n",
-       "32                                         0.160210   \n",
-       "33                                         0.060585   \n",
-       "34                                         0.158863   \n",
-       "35                                        -0.498130   \n",
-       "36                                         0.587655   \n",
-       "38                                        -0.512265   \n",
-       "39                                         0.634770   \n",
-       "40                                        -0.383021   \n",
-       "41                                        -0.469180   \n",
-       "42                                         4.936850   \n",
-       "43                                         0.096258   \n",
-       "44                                         0.581595   \n",
-       "45                                        -0.438220   \n",
-       "46                                        -0.562750   \n",
-       "47                                        -0.976735   \n",
-       "48                                         0.071355   \n",
-       "49                                        -1.007045   \n",
-       "50                                         1.288375   \n",
-       "51                                        -0.427445   \n",
-       "53                                        13.551000   \n",
-       "54                                        -0.308305   \n",
-       "55                                         3.698950   \n",
-       "56                                         0.582945   \n",
-       "57                                         2.185700   \n",
+       "   Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "0                                         0.557360   \n",
+       "1                                        -0.197230   \n",
+       "2                                         0.326475   \n",
+       "3                                        -0.475915   \n",
+       "4                                        -1.204250   \n",
        "\n",
-       "    Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
-       "1                                       -0.859465   \n",
-       "2                                       -4.717350   \n",
-       "3                                        0.064563   \n",
-       "4                                       -0.174002   \n",
-       "6                                        0.615420   \n",
-       "8                                       -0.011905   \n",
-       "9                                        0.287105   \n",
-       "10                                       0.636485   \n",
-       "11                                      -7.893800   \n",
-       "12                                      -3.348650   \n",
-       "13                                       0.235365   \n",
-       "14                                      -0.582000   \n",
-       "15                                       0.120890   \n",
-       "16                                       0.442330   \n",
-       "17                                      -6.635950   \n",
-       "18                                       0.131420   \n",
-       "19                                      -0.027473   \n",
-       "20                                       1.095750   \n",
-       "21                                      -2.065600   \n",
-       "23                                      -0.705630   \n",
-       "24                                       1.226250   \n",
-       "25                                      -0.408905   \n",
-       "27                                      -0.066394   \n",
-       "28                                      -1.044930   \n",
-       "29                                      -0.753250   \n",
-       "30                                      -1.789000   \n",
-       "31                                       0.339765   \n",
-       "32                                      -0.145157   \n",
-       "33                                      -0.345720   \n",
-       "34                                      -0.795380   \n",
-       "35                                       0.415775   \n",
-       "36                                       0.357165   \n",
-       "38                                       0.135083   \n",
-       "39                                       0.648390   \n",
-       "40                                      -1.919050   \n",
-       "41                                       1.024750   \n",
-       "42                                       0.643810   \n",
-       "43                                       0.076012   \n",
-       "44                                       0.404330   \n",
-       "45                                       0.197356   \n",
-       "46                                       0.657090   \n",
-       "47                                      -0.286188   \n",
-       "48                                       0.265125   \n",
-       "49                                      -1.197430   \n",
-       "50                                       0.900240   \n",
-       "51                                      -0.059985   \n",
-       "53                                      -1.326100   \n",
-       "54                                      -0.007785   \n",
-       "55                                      -4.905050   \n",
-       "56                                      -0.146985   \n",
-       "57                                       1.613200   \n",
+       "   Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "0                                      -0.859465   \n",
+       "1                                      -4.717350   \n",
+       "2                                       0.064563   \n",
+       "3                                      -0.174002   \n",
+       "4                                       0.615420   \n",
        "\n",
-       "    Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
-       "1                                           0.409045   \n",
-       "2                                           0.644170   \n",
-       "3                                           0.187646   \n",
-       "4                                           0.217965   \n",
-       "6                                          -0.187645   \n",
-       "8                                          -0.000570   \n",
-       "9                                          -0.617285   \n",
-       "10                                          0.088675   \n",
-       "11                                         -6.190550   \n",
-       "12                                         -6.486350   \n",
-       "13                                         -0.045767   \n",
-       "14                                         -0.398745   \n",
-       "15                                         -0.352405   \n",
-       "16                                          0.244282   \n",
-       "17                                         -8.808600   \n",
-       "18                                          1.046350   \n",
-       "19                                         -1.568100   \n",
-       "20                                         -0.069796   \n",
-       "21                                         -7.149400   \n",
-       "23                                         -0.403320   \n",
-       "24                                          0.588680   \n",
-       "25                                          0.229405   \n",
-       "27                                         -0.653895   \n",
-       "28                                         -1.262620   \n",
-       "29                                          0.855255   \n",
-       "30                                          0.236270   \n",
-       "31                                         -0.322085   \n",
-       "32                                          0.324945   \n",
-       "33                                          0.625860   \n",
-       "34                                          0.593255   \n",
-       "35                                          0.342680   \n",
-       "36                                          1.731700   \n",
-       "38                                          0.877580   \n",
-       "39                                          0.172200   \n",
-       "40                                          0.422770   \n",
-       "41                                         -0.181349   \n",
-       "42                                         -1.311220   \n",
-       "43                                          0.053776   \n",
-       "44                                          0.219112   \n",
-       "45                                          0.149885   \n",
-       "46                                          0.748865   \n",
-       "47                                         -0.855840   \n",
-       "48                                          0.139015   \n",
-       "49                                         -0.853550   \n",
-       "50                                          1.921100   \n",
-       "51                                          0.288905   \n",
-       "53                                          4.612750   \n",
-       "54                                         -0.863870   \n",
-       "55                                          0.290050   \n",
-       "56                                         -0.677920   \n",
-       "57                                         -1.670500   \n",
+       "   Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "0                                          0.409045   \n",
+       "1                                          0.644170   \n",
+       "2                                          0.187646   \n",
+       "3                                          0.217965   \n",
+       "4                                         -0.187645   \n",
        "\n",
-       "    Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
-       "1                                          0.201909   \n",
-       "2                                          1.324100   \n",
-       "3                                          0.200447   \n",
-       "4                                          0.090715   \n",
-       "6                                          0.321880   \n",
-       "8                                          0.066570   \n",
-       "9                                         -0.596950   \n",
-       "10                                        -0.438930   \n",
-       "11                                        -1.162450   \n",
-       "12                                        -0.308715   \n",
-       "13                                         0.177765   \n",
-       "14                                         1.070995   \n",
-       "15                                        -0.109732   \n",
-       "16                                         0.630595   \n",
-       "17                                        -8.821800   \n",
-       "18                                         1.698700   \n",
-       "19                                        -1.196800   \n",
-       "20                                         0.806905   \n",
-       "21                                        -3.275900   \n",
-       "23                                        -0.767400   \n",
-       "24                                         1.099500   \n",
-       "25                                         0.558905   \n",
-       "27                                        -0.862500   \n",
-       "28                                        -1.232685   \n",
-       "29                                        -0.335050   \n",
-       "30                                         0.108268   \n",
-       "31                                        -0.470385   \n",
-       "32                                        -0.047550   \n",
-       "33                                         0.620355   \n",
-       "34                                        -0.090712   \n",
-       "35                                         0.834700   \n",
-       "36                                         1.300700   \n",
-       "38                                         1.453550   \n",
-       "39                                        -0.256040   \n",
-       "40                                        -0.120706   \n",
-       "41                                         0.000732   \n",
-       "42                                        -0.839090   \n",
-       "43                                         0.081934   \n",
-       "44                                         0.647420   \n",
-       "45                                        -0.220930   \n",
-       "46                                         0.076081   \n",
-       "47                                        -1.180700   \n",
-       "48                                        -0.204103   \n",
-       "49                                        -1.547250   \n",
-       "50                                         1.718450   \n",
-       "51                                         0.085592   \n",
-       "53                                         5.419350   \n",
-       "54                                        -0.750570   \n",
-       "55                                         0.425045   \n",
-       "56                                        -1.008060   \n",
-       "57                                        -1.670150   \n",
+       "   Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "0                                         0.201909   \n",
+       "1                                         1.324100   \n",
+       "2                                         0.200447   \n",
+       "3                                         0.090715   \n",
+       "4                                         0.321880   \n",
        "\n",
-       "    Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
-       "1                           -1.003185                           -1.405850   \n",
-       "2                            0.103070                            0.986025   \n",
-       "3                           -0.695660                            0.100225   \n",
-       "4                           -0.154695                            0.165235   \n",
-       "6                            1.013235                            0.793675   \n",
-       "8                            0.015971                            0.257335   \n",
-       "9                            0.194805                           -1.037480   \n",
-       "10                          -0.251265                           -0.333184   \n",
-       "11                           2.364450                            1.785100   \n",
-       "12                           3.929950                            1.430250   \n",
-       "13                           0.637345                            0.130025   \n",
-       "14                           1.386330                            1.302950   \n",
-       "15                           0.364360                            0.170652   \n",
-       "16                           0.216720                           -0.148982   \n",
-       "17                          -1.763250                            1.752600   \n",
-       "18                          -0.022657                           -0.170655   \n",
-       "19                           1.167565                            1.191870   \n",
-       "20                          -0.931870                           -0.132730   \n",
-       "21                           6.976850                            4.534550   \n",
-       "23                          -1.273770                           -0.658235   \n",
-       "24                           0.114581                            0.032505   \n",
-       "25                          -0.410785                            0.138148   \n",
-       "27                           0.080967                           -0.281715   \n",
-       "28                          -0.900705                           -0.230250   \n",
-       "29                          -1.362150                           -1.839250   \n",
-       "30                          -0.478755                           -0.652825   \n",
-       "31                           0.113840                           -0.029797   \n",
-       "32                          -0.766600                           -0.688035   \n",
-       "33                          -0.573280                           -0.720540   \n",
-       "34                          -0.940430                           -0.449660   \n",
-       "35                          -0.012443                            0.260045   \n",
-       "36                          -1.007465                           -0.671785   \n",
-       "38                          -0.203165                            0.493000   \n",
-       "39                           0.029160                           -1.086205   \n",
-       "40                           1.258530                            0.674490   \n",
-       "41                           0.653135                            0.029800   \n",
-       "42                           0.104182                            0.262755   \n",
-       "43                          -0.248475                           -0.178780   \n",
-       "44                           0.442170                           -0.411735   \n",
-       "45                          -0.269090                           -0.219415   \n",
-       "46                           0.037513                            0.509250   \n",
-       "47                           0.574950                            0.999570   \n",
-       "48                          -0.033055                           -0.398195   \n",
-       "49                          -0.178835                            1.934100   \n",
-       "50                          -1.650930                           -0.316930   \n",
-       "51                          -0.503085                           -0.243790   \n",
-       "53                           5.724250                           -8.781900   \n",
-       "54                          -0.602995                            0.371105   \n",
-       "55                           0.344295                           -1.414005   \n",
-       "56                           0.059615                           -0.268170   \n",
-       "57                           1.785400                           -3.656900   \n",
+       "   Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "0                          -1.003185                           -1.405850   \n",
+       "1                           0.103070                            0.986025   \n",
+       "2                          -0.695660                            0.100225   \n",
+       "3                          -0.154695                            0.165235   \n",
+       "4                           1.013235                            0.793675   \n",
        "\n",
-       "    Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
-       "1                            -1.495100                          -0.867225   \n",
-       "2                             1.346200                           0.773450   \n",
-       "3                             0.401885                           0.114583   \n",
-       "4                            -0.160191                           0.242195   \n",
-       "6                             0.682925                           1.075500   \n",
-       "8                             0.140519                           0.471360   \n",
-       "9                            -0.871205                          -0.846345   \n",
-       "10                           -0.601420                          -0.130211   \n",
-       "11                            1.329300                          -1.494850   \n",
-       "12                            3.869900                          -5.724050   \n",
-       "13                           -0.011241                           0.533865   \n",
-       "14                            2.515300                           0.468750   \n",
-       "15                           -0.019672                           0.283860   \n",
-       "16                           -0.025293                           0.054689   \n",
-       "17                            3.754700                          -1.052100   \n",
-       "18                           -0.407506                           0.049480   \n",
-       "19                            1.725600                           0.708345   \n",
-       "20                            1.866050                          -1.291700   \n",
-       "21                            4.842250                           0.854165   \n",
-       "23                            0.702570                          -1.869850   \n",
-       "24                            0.292280                           0.046877   \n",
-       "25                            0.103984                           0.247400   \n",
-       "27                           -0.356915                           0.010415   \n",
-       "28                           -0.480575                           0.164065   \n",
-       "29                           -1.343350                          -1.796950   \n",
-       "30                           -0.025295                          -0.716160   \n",
-       "31                           -0.129276                           0.260420   \n",
-       "32                           -0.784095                          -0.291670   \n",
-       "33                           -0.722265                          -0.257816   \n",
-       "34                           -0.446850                          -0.153649   \n",
-       "35                            0.278225                           0.434905   \n",
-       "36                           -0.601432                          -0.283855   \n",
-       "38                            0.407505                           0.513030   \n",
-       "39                           -0.910565                          -0.752630   \n",
-       "40                            1.292800                           0.653655   \n",
-       "41                           -0.025294                           0.304690   \n",
-       "42                            0.829060                          -0.838548   \n",
-       "43                           -0.258555                           0.101564   \n",
-       "44                           -0.132086                          -0.138025   \n",
-       "45                           -0.250125                           0.174480   \n",
-       "46                            0.393450                           0.653660   \n",
-       "47                            0.727890                           0.763035   \n",
-       "48                           -0.199536                          -0.174484   \n",
-       "49                            1.894200                           1.617250   \n",
-       "50                           -0.297900                          -0.499990   \n",
-       "51                           -0.553645                          -0.088544   \n",
-       "53                           -5.519550                          -9.482150   \n",
-       "54                            0.559265                           0.471365   \n",
-       "55                            0.488985                          -3.562550   \n",
-       "56                           -0.033725                          -0.158858   \n",
-       "57                           -3.720950                          -3.130250   \n",
+       "   Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "0                           -1.495100                          -0.867225   \n",
+       "1                            1.346200                           0.773450   \n",
+       "2                            0.401885                           0.114583   \n",
+       "3                           -0.160191                           0.242195   \n",
+       "4                            0.682925                           1.075500   \n",
        "\n",
-       "    Nuclei_Texture_Variance_RNA_10_0  \n",
-       "1                          -0.066115  \n",
-       "2                          -2.749350  \n",
-       "3                          -0.245753  \n",
-       "4                          -0.126886  \n",
-       "6                           0.844115  \n",
-       "8                          -0.283820  \n",
-       "9                           0.533585  \n",
-       "10                          0.183651  \n",
-       "11                          1.846500  \n",
-       "12                          6.475100  \n",
-       "13                          0.416050  \n",
-       "14                         -0.502195  \n",
-       "15                          0.404025  \n",
-       "16                          0.263120  \n",
-       "17                         -9.717150  \n",
-       "18                          0.468805  \n",
-       "19                          0.004007  \n",
-       "20                         -1.867900  \n",
-       "21                         -0.969015  \n",
-       "23                         -3.957450  \n",
-       "24                          0.720565  \n",
-       "25                          0.328564  \n",
-       "27                          0.452110  \n",
-       "28                         -0.074795  \n",
-       "29                         -4.749500  \n",
-       "30                         -0.445430  \n",
-       "31                          0.474815  \n",
-       "32                         -0.017363  \n",
-       "33                         -0.163612  \n",
-       "34                         -1.377700  \n",
-       "35                          0.356610  \n",
-       "36                         -0.337245  \n",
-       "38                          0.204351  \n",
-       "39                         -0.632420  \n",
-       "40                         -2.583750  \n",
-       "41                          0.790020  \n",
-       "42                         -1.612110  \n",
-       "43                          0.207023  \n",
-       "44                          0.498860  \n",
-       "45                          0.223719  \n",
-       "46                         -0.168288  \n",
-       "47                         -0.487505  \n",
-       "48                         -0.556290  \n",
-       "49                         -1.901300  \n",
-       "50                         -3.373800  \n",
-       "51                          0.063442  \n",
-       "53                        -11.058500  \n",
-       "54                          0.064110  \n",
-       "55                         -1.630150  \n",
-       "56                         -0.046745  \n",
-       "57                          1.099200  \n",
+       "   Nuclei_Texture_Variance_RNA_10_0  \n",
+       "0                         -0.066115  \n",
+       "1                         -2.749350  \n",
+       "2                         -0.245753  \n",
+       "3                         -0.126886  \n",
+       "4                          0.844115  \n",
        "\n",
-       "[51 rows x 495 columns]"
+       "[5 rows x 495 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# aggregate replicates per perturbation using sample id and target\n",
-    "df_agg = aggregate(df, strata=[\"Metadata_broad_sample\", \"Metadata_target\"])\n",
-    "df_agg = df_agg[df_agg[\"Metadata_target\"].isna() == False]\n",
-    "df_agg['Metadata_target'] = df_agg['Metadata_target'].str.split('|')\n",
-    "df_agg.head(10)"
+    "# aggregate replicates by taking the median of each feature\n",
+    "feature_cols = [c for c in df_consistent.columns if not c.startswith(\"Metadata\")]\n",
+    "df_consistent = df_consistent.groupby([\"Metadata_broad_sample\", \"Metadata_target\"], as_index=False)[feature_cols].median()\n",
+    "df_consistent['Metadata_target'] = df_consistent['Metadata_target'].str.split('|')\n",
+    "df_consistent.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we again use metadata columns to define grouping of profiles. Here, we'd like to group those compounds that share a target and assess their similarity against compounds that do not have the same target:\n",
+    "\n",
+    "* Two compound profiles are a positive pair if they share the same target. To define that using metadata columns, positive pairs should share the same value in the metadata column that identifies targets (`Metadata_target`). We add this column to a list names `pos_sameby`.\n",
+    "\n",
+    "* In this case, profiles that form a positive pair do not need to be different in any of the metatada columns, so we keep `pos_diffby` empty. Although one could define them as being structurally different, for example.\n",
+    "\n",
+    "* Two profiles are a negative pair when do not share a common target. That means they should be different in the metadata column that identifies targets (`Metadata_target`).\n",
+    "\n",
+    "* Profiles that form a negative pair do not need to be same in any of the metatada columns, so we keep `neg_sameby` empty.\n",
+    "\n",
+    "We use `map.multilabel.average_precision` because each compound can have more than one target. If that's not the case, the standard `map.average_precision` should be used instead."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "641127b3e44e4bdd88162da28f03bade",
+       "model_id": "2978af4648c648fea1d376a113b5fe01",
        "version_major": 2,
        "version_minor": 0
       },
@@ -4351,7 +2476,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2af2dca7bb474bdeabfed5a4acda10d0",
+       "model_id": "978f42401bec43e2b54936e77e8bb113",
        "version_major": 2,
        "version_minor": 0
       },
@@ -4392,43 +2517,43 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>56</th>\n",
+       "      <th>54</th>\n",
        "      <td>BRD-A69636825-003-04-7</td>\n",
        "      <td>0.500000</td>\n",
        "      <td>1</td>\n",
-       "      <td>50</td>\n",
+       "      <td>46</td>\n",
        "      <td>HTR3A</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>36</th>\n",
+       "      <th>34</th>\n",
        "      <td>BRD-A72309220-001-04-1</td>\n",
-       "      <td>0.380556</td>\n",
+       "      <td>0.396412</td>\n",
        "      <td>4</td>\n",
-       "      <td>50</td>\n",
+       "      <td>46</td>\n",
        "      <td>HTR1A</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>41</th>\n",
+       "      <th>39</th>\n",
        "      <td>BRD-A72309220-001-04-1</td>\n",
-       "      <td>0.125000</td>\n",
+       "      <td>0.142857</td>\n",
        "      <td>1</td>\n",
-       "      <td>47</td>\n",
+       "      <td>43</td>\n",
        "      <td>HTR1B</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>43</th>\n",
+       "      <th>41</th>\n",
        "      <td>BRD-A72309220-001-04-1</td>\n",
-       "      <td>0.125000</td>\n",
+       "      <td>0.142857</td>\n",
        "      <td>1</td>\n",
-       "      <td>47</td>\n",
+       "      <td>43</td>\n",
        "      <td>HTR1D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>45</th>\n",
+       "      <th>43</th>\n",
        "      <td>BRD-A72309220-001-04-1</td>\n",
-       "      <td>0.125000</td>\n",
+       "      <td>0.142857</td>\n",
        "      <td>1</td>\n",
-       "      <td>47</td>\n",
+       "      <td>43</td>\n",
        "      <td>HTR1E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -4440,19 +2565,27 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.094538</td>\n",
+       "      <td>2</td>\n",
+       "      <td>46</td>\n",
+       "      <td>CHRM3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>BRD-K74363950-004-01-0</td>\n",
-       "      <td>0.083882</td>\n",
+       "      <td>0.094538</td>\n",
        "      <td>2</td>\n",
-       "      <td>50</td>\n",
+       "      <td>46</td>\n",
        "      <td>CHRM4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>BRD-K74363950-004-01-0</td>\n",
-       "      <td>0.083882</td>\n",
+       "      <td>0.094538</td>\n",
        "      <td>2</td>\n",
-       "      <td>50</td>\n",
+       "      <td>46</td>\n",
        "      <td>CHRM5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -4460,61 +2593,53 @@
        "      <td>BRD-K76908866-001-07-6</td>\n",
        "      <td>0.500000</td>\n",
        "      <td>1</td>\n",
-       "      <td>50</td>\n",
+       "      <td>46</td>\n",
        "      <td>ERBB2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>BRD-K80700417-001-04-2</td>\n",
-       "      <td>0.020833</td>\n",
-       "      <td>1</td>\n",
-       "      <td>50</td>\n",
-       "      <td>FLT3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67</th>\n",
+       "      <th>63</th>\n",
        "      <td>BRD-K81258678-001-01-0</td>\n",
        "      <td>0.100000</td>\n",
        "      <td>1</td>\n",
-       "      <td>50</td>\n",
+       "      <td>46</td>\n",
        "      <td>RELA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>70 rows × 5 columns</p>\n",
+       "<p>66 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "     Metadata_broad_sample  average_precision  n_pos_pairs  n_total_pairs  \\\n",
-       "56  BRD-A69636825-003-04-7           0.500000            1             50   \n",
-       "36  BRD-A72309220-001-04-1           0.380556            4             50   \n",
-       "41  BRD-A72309220-001-04-1           0.125000            1             47   \n",
-       "43  BRD-A72309220-001-04-1           0.125000            1             47   \n",
-       "45  BRD-A72309220-001-04-1           0.125000            1             47   \n",
+       "54  BRD-A69636825-003-04-7           0.500000            1             46   \n",
+       "34  BRD-A72309220-001-04-1           0.396412            4             46   \n",
+       "39  BRD-A72309220-001-04-1           0.142857            1             43   \n",
+       "41  BRD-A72309220-001-04-1           0.142857            1             43   \n",
+       "43  BRD-A72309220-001-04-1           0.142857            1             43   \n",
        "..                     ...                ...          ...            ...   \n",
-       "19  BRD-K74363950-004-01-0           0.083882            2             50   \n",
-       "22  BRD-K74363950-004-01-0           0.083882            2             50   \n",
-       "28  BRD-K76908866-001-07-6           0.500000            1             50   \n",
-       "30  BRD-K80700417-001-04-2           0.020833            1             50   \n",
-       "67  BRD-K81258678-001-01-0           0.100000            1             50   \n",
+       "16  BRD-K74363950-004-01-0           0.094538            2             46   \n",
+       "19  BRD-K74363950-004-01-0           0.094538            2             46   \n",
+       "22  BRD-K74363950-004-01-0           0.094538            2             46   \n",
+       "28  BRD-K76908866-001-07-6           0.500000            1             46   \n",
+       "63  BRD-K81258678-001-01-0           0.100000            1             46   \n",
        "\n",
        "   Metadata_target  \n",
-       "56           HTR3A  \n",
-       "36           HTR1A  \n",
-       "41           HTR1B  \n",
-       "43           HTR1D  \n",
-       "45           HTR1E  \n",
+       "54           HTR3A  \n",
+       "34           HTR1A  \n",
+       "39           HTR1B  \n",
+       "41           HTR1D  \n",
+       "43           HTR1E  \n",
        "..             ...  \n",
+       "16           CHRM3  \n",
        "19           CHRM4  \n",
        "22           CHRM5  \n",
        "28           ERBB2  \n",
-       "30            FLT3  \n",
-       "67            RELA  \n",
+       "63            RELA  \n",
        "\n",
-       "[70 rows x 5 columns]"
+       "[66 rows x 5 columns]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4528,10 +2653,10 @@
     "# negative pairs are compounds that do not share a target\n",
     "neg_diffby = [\"Metadata_target\"]\n",
     "\n",
-    "metadata = df_agg.filter(regex=\"^Metadata\")\n",
-    "profiles = df_agg.filter(regex=\"^(?!Metadata)\").values\n",
+    "metadata = df_consistent.filter(regex=\"^Metadata\")\n",
+    "profiles = df_consistent.filter(regex=\"^(?!Metadata)\").values\n",
     "\n",
-    "ap_scores = map.multilabel.average_precision(\n",
+    "target_aps = map.multilabel.average_precision(\n",
     "        metadata,\n",
     "        profiles,\n",
     "        pos_sameby=pos_sameby,\n",
@@ -4539,23 +2664,30 @@
     "        neg_sameby=neg_sameby,\n",
     "        neg_diffby=neg_diffby,\n",
     "        multilabel_col='Metadata_target')\n",
-    "ap_scores"
+    "target_aps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can compute mAP scores and p-values for each target group."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80e8224e98444cce976ad30694362c83",
+       "model_id": "096da8b2793b43c5b5a384b08e5e44c3",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/16 [00:00<?, ?it/s]"
+       "  0%|          | 0/15 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -4564,12 +2696,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45defb90f221478a9c3a268e331157f0",
+       "model_id": "3fed0e42fb09484bbef6662e3a6d981f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/29 [00:00<?, ?it/s]"
+       "  0%|          | 0/27 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -4610,379 +2742,158 @@
        "      <th>0</th>\n",
        "      <td>ADRA1A</td>\n",
        "      <td>0.238095</td>\n",
-       "      <td>0.092591</td>\n",
-       "      <td>0.170059</td>\n",
+       "      <td>0.104890</td>\n",
+       "      <td>0.167542</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
+       "      <td>0.775876</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>ADRA2A</td>\n",
        "      <td>0.238095</td>\n",
-       "      <td>0.092591</td>\n",
-       "      <td>0.170059</td>\n",
+       "      <td>0.104890</td>\n",
+       "      <td>0.167542</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
+       "      <td>0.775876</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AURKA</td>\n",
        "      <td>0.625000</td>\n",
-       "      <td>0.001900</td>\n",
-       "      <td>0.011019</td>\n",
-       "      <td>True</td>\n",
+       "      <td>0.022298</td>\n",
+       "      <td>0.100340</td>\n",
        "      <td>True</td>\n",
-       "      <td>1.957862</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.998526</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>BIRC2</td>\n",
-       "      <td>0.047727</td>\n",
-       "      <td>0.405359</td>\n",
-       "      <td>0.534337</td>\n",
+       "      <td>0.051316</td>\n",
+       "      <td>0.413459</td>\n",
+       "      <td>0.483152</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.272184</td>\n",
+       "      <td>0.315917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>CHRM1</td>\n",
-       "      <td>0.081192</td>\n",
-       "      <td>0.504350</td>\n",
-       "      <td>0.541709</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.266234</td>\n",
+       "      <td>0.315917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>CHRM2</td>\n",
-       "      <td>0.081192</td>\n",
-       "      <td>0.504350</td>\n",
-       "      <td>0.541709</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.266234</td>\n",
+       "      <td>0.315917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>CHRM3</td>\n",
-       "      <td>0.081192</td>\n",
-       "      <td>0.504350</td>\n",
-       "      <td>0.541709</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.266234</td>\n",
+       "      <td>0.315917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>CHRM4</td>\n",
-       "      <td>0.081192</td>\n",
-       "      <td>0.504350</td>\n",
-       "      <td>0.541709</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.266234</td>\n",
+       "      <td>0.315917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>CHRM5</td>\n",
-       "      <td>0.081192</td>\n",
-       "      <td>0.504350</td>\n",
-       "      <td>0.541709</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.266234</td>\n",
+       "      <td>0.315917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>DRD2</td>\n",
        "      <td>0.750000</td>\n",
-       "      <td>0.000800</td>\n",
-       "      <td>0.005799</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.236615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>EGFR</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.000700</td>\n",
-       "      <td>0.005799</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.236615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>ERBB2</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.037996</td>\n",
-       "      <td>0.157413</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.802960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>FLT3</td>\n",
-       "      <td>0.021528</td>\n",
-       "      <td>0.992301</td>\n",
-       "      <td>0.992301</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.003357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>GABRA1</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.078092</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>HRH1</td>\n",
-       "      <td>0.117663</td>\n",
-       "      <td>0.338666</td>\n",
-       "      <td>0.516911</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.286584</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>HTR1A</td>\n",
-       "      <td>0.262612</td>\n",
-       "      <td>0.066193</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>HTR1B</td>\n",
-       "      <td>0.229167</td>\n",
-       "      <td>0.094991</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>HTR1D</td>\n",
-       "      <td>0.229167</td>\n",
-       "      <td>0.094991</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>HTR1E</td>\n",
-       "      <td>0.229167</td>\n",
-       "      <td>0.094991</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>HTR2A</td>\n",
-       "      <td>0.340241</td>\n",
-       "      <td>0.011099</td>\n",
-       "      <td>0.053645</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>1.270474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>HTR2B</td>\n",
-       "      <td>0.066964</td>\n",
-       "      <td>0.384462</td>\n",
-       "      <td>0.534337</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.272184</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>HTR2C</td>\n",
-       "      <td>0.225362</td>\n",
-       "      <td>0.099690</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>HTR3A</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.000800</td>\n",
-       "      <td>0.005799</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.236615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>HTR6</td>\n",
-       "      <td>0.229167</td>\n",
-       "      <td>0.094991</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>HTR7</td>\n",
-       "      <td>0.229167</td>\n",
-       "      <td>0.094991</td>\n",
-       "      <td>0.170059</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.769399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>PDGFRA</td>\n",
-       "      <td>0.028571</td>\n",
-       "      <td>0.856014</td>\n",
-       "      <td>0.886586</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.052279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>PSMB1</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.002900</td>\n",
+       "      <td>0.000900</td>\n",
+       "      <td>0.006074</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>2.537645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>RELA</td>\n",
-       "      <td>0.150000</td>\n",
-       "      <td>0.133387</td>\n",
-       "      <td>0.214901</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.667762</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>XIAP</td>\n",
-       "      <td>0.047727</td>\n",
-       "      <td>0.405359</td>\n",
-       "      <td>0.534337</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.272184</td>\n",
+       "      <td>2.216497</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Metadata_target  mean_average_precision   p_value  corrected_p_value  \\\n",
-       "0           ADRA1A                0.238095  0.092591           0.170059   \n",
-       "1           ADRA2A                0.238095  0.092591           0.170059   \n",
-       "2            AURKA                0.625000  0.001900           0.011019   \n",
-       "3            BIRC2                0.047727  0.405359           0.534337   \n",
-       "4            CHRM1                0.081192  0.504350           0.541709   \n",
-       "5            CHRM2                0.081192  0.504350           0.541709   \n",
-       "6            CHRM3                0.081192  0.504350           0.541709   \n",
-       "7            CHRM4                0.081192  0.504350           0.541709   \n",
-       "8            CHRM5                0.081192  0.504350           0.541709   \n",
-       "9             DRD2                0.750000  0.000800           0.005799   \n",
-       "10            EGFR                0.750000  0.000700           0.005799   \n",
-       "11           ERBB2                0.500000  0.037996           0.157413   \n",
-       "12            FLT3                0.021528  0.992301           0.992301   \n",
-       "13          GABRA1                0.238095  0.078092           0.170059   \n",
-       "14            HRH1                0.117663  0.338666           0.516911   \n",
-       "15           HTR1A                0.262612  0.066193           0.170059   \n",
-       "16           HTR1B                0.229167  0.094991           0.170059   \n",
-       "17           HTR1D                0.229167  0.094991           0.170059   \n",
-       "18           HTR1E                0.229167  0.094991           0.170059   \n",
-       "19           HTR2A                0.340241  0.011099           0.053645   \n",
-       "20           HTR2B                0.066964  0.384462           0.534337   \n",
-       "21           HTR2C                0.225362  0.099690           0.170059   \n",
-       "22           HTR3A                0.750000  0.000800           0.005799   \n",
-       "23            HTR6                0.229167  0.094991           0.170059   \n",
-       "24            HTR7                0.229167  0.094991           0.170059   \n",
-       "25          PDGFRA                0.028571  0.856014           0.886586   \n",
-       "26           PSMB1                1.000000  0.000100           0.002900   \n",
-       "27            RELA                0.150000  0.133387           0.214901   \n",
-       "28            XIAP                0.047727  0.405359           0.534337   \n",
+       "  Metadata_target  mean_average_precision   p_value  corrected_p_value  \\\n",
+       "0          ADRA1A                0.238095  0.104890           0.167542   \n",
+       "1          ADRA2A                0.238095  0.104890           0.167542   \n",
+       "2           AURKA                0.625000  0.022298           0.100340   \n",
+       "3           BIRC2                0.051316  0.413459           0.483152   \n",
+       "4           CHRM1                0.091024  0.483152           0.483152   \n",
+       "5           CHRM2                0.091024  0.483152           0.483152   \n",
+       "6           CHRM3                0.091024  0.483152           0.483152   \n",
+       "7           CHRM4                0.091024  0.483152           0.483152   \n",
+       "8           CHRM5                0.091024  0.483152           0.483152   \n",
+       "9            DRD2                0.750000  0.000900           0.006074   \n",
        "\n",
-       "    below_p  below_corrected_p  -log10(p-value)  \n",
-       "0     False              False         0.769399  \n",
-       "1     False              False         0.769399  \n",
-       "2      True               True         1.957862  \n",
-       "3     False              False         0.272184  \n",
-       "4     False              False         0.266234  \n",
-       "5     False              False         0.266234  \n",
-       "6     False              False         0.266234  \n",
-       "7     False              False         0.266234  \n",
-       "8     False              False         0.266234  \n",
-       "9      True               True         2.236615  \n",
-       "10     True               True         2.236615  \n",
-       "11     True              False         0.802960  \n",
-       "12    False              False         0.003357  \n",
-       "13    False              False         0.769399  \n",
-       "14    False              False         0.286584  \n",
-       "15    False              False         0.769399  \n",
-       "16    False              False         0.769399  \n",
-       "17    False              False         0.769399  \n",
-       "18    False              False         0.769399  \n",
-       "19     True              False         1.270474  \n",
-       "20    False              False         0.272184  \n",
-       "21    False              False         0.769399  \n",
-       "22     True               True         2.236615  \n",
-       "23    False              False         0.769399  \n",
-       "24    False              False         0.769399  \n",
-       "25    False              False         0.052279  \n",
-       "26     True               True         2.537645  \n",
-       "27    False              False         0.667762  \n",
-       "28    False              False         0.272184  "
+       "   below_p  below_corrected_p  -log10(p-value)  \n",
+       "0    False              False         0.775876  \n",
+       "1    False              False         0.775876  \n",
+       "2     True              False         0.998526  \n",
+       "3    False              False         0.315917  \n",
+       "4    False              False         0.315917  \n",
+       "5    False              False         0.315917  \n",
+       "6    False              False         0.315917  \n",
+       "7    False              False         0.315917  \n",
+       "8    False              False         0.315917  \n",
+       "9     True               True         2.216497  "
       ]
      },
-     "execution_count": 39,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "map_scores = map.mean_average_precision(ap_scores, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
-    "map_scores[\"-log10(p-value)\"] = -map_scores[\"corrected_p_value\"].apply(np.log10)\n",
-    "map_scores.head(10)"
+    "target_maps = map.mean_average_precision(target_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
+    "target_maps[\"-log10(p-value)\"] = -target_maps[\"corrected_p_value\"].apply(np.log10)\n",
+    "target_maps.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, we can plot the results, where groups of compounds targeting the same gene are called consistent if their corrected p-value < 0.05."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -4992,14 +2903,48 @@
     }
    ],
    "source": [
-    "plt.scatter(data=map_scores, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
+    "consistent_ratio = target_maps.below_corrected_p.mean()\n",
+    "\n",
+    "plt.scatter(data=target_maps, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
     "plt.xlabel(\"mAP\")\n",
     "plt.ylabel(\"-log10(p-value)\")\n",
     "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
-    "plt.text(0.5, 1.5, f\"Phenotypically consistent = {100*map_scores.below_corrected_p.mean():.2f}%\", va=\"center\", ha=\"left\")\n",
+    "plt.text(0.5, 1.5, f\"Phenotypically consistent = {100*consistent_ratio:.2f}%\", va=\"center\", ha=\"left\")\n",
+    "\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can list compounds that are phenotypically active and consistent.\n",
+    "\n",
+    "Note that in multi-label scenario, when each compound can have multiple targets, the same compound can have \"consistent\" response in respect to one target, but not another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Phenotypically consistent targets: DRD2, EGFR, HTR3A, PSMB1\n",
+      "Phenotypically consistent compounds: BRD-A69636825-003-04-7, BRD-K50691590-001-02-2, BRD-K60230970-001-10-0, BRD-K70330367-003-07-9, BRD-K70358946-001-15-7, BRD-K70401845-003-09-6, BRD-K70914287-300-02-8\n"
+     ]
+    }
+   ],
+   "source": [
+    "consistent_targets = target_maps.query(\"below_corrected_p\")[\"Metadata_target\"]\n",
+    "consistent_compounds = df_consistent[df_consistent[\"Metadata_target\"].apply(lambda x: any(t in x for t in consistent_targets))][\"Metadata_broad_sample\"]\n",
+    "\n",
+    "print(f\"Phenotypically consistent targets: {consistent_targets.str.cat(sep=', ')}\")\n",
+    "print(f\"Phenotypically consistent compounds: {consistent_compounds.str.cat(sep=', ')}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 69ca58492360d4108aaa23d988951ad0b0406d2b Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 3 Jul 2024 11:17:03 -0400
Subject: [PATCH 19/30] add citation, sys recs & dependencies to readme

---
 README.md | 51 ++++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 48 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index ae7592e..36501b6 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,39 @@
  # copairs
 
-Find pairs and compute metrics between them.
+`copairs` is a Python package for finding groups of profiles based on metadata and calculate mean Average Precision to assess intra- vs inter-group similarities.
 
-## Installation
+## Getting started
 
+### System requirements
+copairs supports Python 3.8+ and should work with all modern operating systems (tested with MacOS 13.5, Ubuntu 18.04, Windows 10).
+
+### Dependencies
+copairs depends on widely used Python packages:
+* numpy
+* pandas
+* tqdm
+* statsmodels
+* [optional] plotly
+
+### Installation
+
+To install copairs and dependencies, run:
+```bash
+pip install git+https://github.com/cytomining/copairs.git@v0.4.2
+```
+
+To also install dependencies for running examples, run:
+```bash
+pip install copairs[demo] @ git+https://github.com/cytomining/copairs.git@v0.4.2
+```
+
+### Testing
+
+To run tests, run:
 ```bash
-pip install git+https://github.com/cytomining/copairs.git@v0.4.1
+pip install pytest scikit-learn
+cd copairs
+pytest
 ```
 
 ## Usage
@@ -117,3 +145,20 @@ structure discussed before:
  't1': [(2, 7), (2, 3), (2, 4), (2, 5)],
  't3': [(5, 2), (5, 8), (8, 2), (8, 7), (2, 7)]}
 ```
+
+## Citation
+If you find this work useful for your research, please cite our [pre-print](https://doi.org/10.1101/2024.04.01.587631):
+
+Kalinin, A.A., Arevalo, J., Vulliard, L., Serrano, E., Tsang, H., Bornholdt, M., Rajwa, B., Carpenter, A.E., Way, G.P. and Singh, S., 2024. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, pp.2024-04. doi:10.1101/2024.04.01.587631
+
+BibTeX:
+```
+@article{kalinin2024versatile,
+  title={A versatile information retrieval framework for evaluating profile strength and similarity},
+  author={Kalinin, Alexandr A and Arevalo, John and Vulliard, Loan and Serrano, Erik and Tsang, Hillary and Bornholdt, Michael and Rajwa, Bartek and Carpenter, Anne E and Way, Gregory P and Singh, Shantanu},
+  journal={bioRxiv},
+  pages={2024--04},
+  year={2024},
+  doi={10.1101/2024.04.01.587631}
+}
+```
\ No newline at end of file

From b5c53082ce4bedfd4f1e3b57959169449c8cac0b Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 10 Jul 2024 12:23:18 -0400
Subject: [PATCH 20/30] fix typos

---
 examples/demo.ipynb | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/examples/demo.ipynb b/examples/demo.ipynb
index ee76743..9208df4 100644
--- a/examples/demo.ipynb
+++ b/examples/demo.ipynb
@@ -33,7 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# thase imports are only needed for showing Figure 1 from the paper\n",
+    "# these imports are only needed for showing Figure 1 from the paper\n",
     "import requests\n",
     "from io import BytesIO\n",
     "from pathlib import Path\n",
@@ -612,9 +612,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Calculate mAP for assessing phenotypic activity of compounds\n",
+    "## Assessing phenotypic activity of compounds with mAP\n",
     "\n",
-    "Phenotypic activity of a perturbation reflects the average extent to which its replicate profiles are more similar to each other compared to control profiles (see Figure 1E)."
+    "Phenotypic activity of a perturbation reflects the average extent to which its replicate profiles are more similar to each other compared to control profiles (Figure 1E)."
    ]
   },
   {
@@ -1155,7 +1155,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aebfcde75ae74aee8390c1f731c392ae",
+       "model_id": "5d583875de81417fa8f66f87e2bfb80b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1169,7 +1169,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0c13ce3b5c0f4c0fab21d5cd94780bee",
+       "model_id": "e7687333b8544cc6a2e377ac848e3457",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1571,7 +1571,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d3c57cc5258f4dcb86670efd06eab762",
+       "model_id": "7b70f4682ff947ca875777958c499f94",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1585,7 +1585,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ad8f608cec534e7a97a5c30c4257a74f",
+       "model_id": "eed952af896e48ffaa790e3c997714fc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1807,7 +1807,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Calculate mAP for assessing phenotypic consistency of compounds grouped by targets\n",
+    "## Assessing phenotypic consistency of compounds grouped by targets\n",
     "\n",
     "Phenotypic consitency of a group of perturbations reflects the average extent to which members of this group are more similar to each other compared to other groups (see Figure 1F).\n",
     "\n",
@@ -2462,7 +2462,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2978af4648c648fea1d376a113b5fe01",
+       "model_id": "1dfaedfbdea44ec4ba89eb7d49a4d9ea",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2476,7 +2476,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "978f42401bec43e2b54936e77e8bb113",
+       "model_id": "b7e2c7e2ab214da09395dab1d71b9221",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2682,7 +2682,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "096da8b2793b43c5b5a384b08e5e44c3",
+       "model_id": "e4cf611dd48f421a92039c551475d6e8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2696,7 +2696,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fed0e42fb09484bbef6662e3a6d981f",
+       "model_id": "36db5ef9e7e24a55899d35ab9b7d746a",
        "version_major": 2,
        "version_minor": 0
       },

From 2673e55e5a4b69e5cdce64ec9bf2a96ef03007e9 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Thu, 5 Sep 2024 22:42:29 -0400
Subject: [PATCH 21/30] bump version to 0.4.2

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7ba9d08..ca6f34a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "copairs"
-version = "0.4.1"
+version = "0.4.2"
 description = "Find pairs and compute metrics between them"
 readme = "README.md"
 requires-python = ">=3.8"

From fac5985e6766d4689221908f2504f70561c03ab8 Mon Sep 17 00:00:00 2001
From: alxndrkalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Tue, 17 Sep 2024 14:09:40 -0400
Subject: [PATCH 22/30] allow distance fn selection; add euclidean, abs_cosine

---
 src/copairs/compute.py               | 29 ++++++++++++++---
 src/copairs/map/average_precision.py |  7 +++--
 src/copairs/map/multilabel.py        |  6 ++--
 src/copairs/replicating.py           |  8 +++--
 tests/test_compute.py                | 47 ++++++++++++++++++++++++++--
 5 files changed, 83 insertions(+), 14 deletions(-)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index 8bc7dce..7e977f4 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -41,7 +41,6 @@ def par_func(i):
     return batched_fn
 
 
-@batch_processing
 def pairwise_corr(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     """
     Compute pearson correlation between two matrices in a paired row-wise
@@ -62,7 +61,6 @@ def pairwise_corr(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     return corrs
 
 
-@batch_processing
 def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     x_norm = x_sample / np.linalg.norm(x_sample, axis=1)[:, np.newaxis]
     y_norm = y_sample / np.linalg.norm(y_sample, axis=1)[:, np.newaxis]
@@ -70,10 +68,33 @@ def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     return c_sim
 
 
-@batch_processing
+def pairwise_abs_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    return np.abs(pairwise_cosine(x_sample, y_sample))
+
+
 def pairwise_euclidean(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     e_dist = np.sqrt(np.sum((x_sample - y_sample) ** 2, axis=1))
-    return 1 - e_dist
+    return 1 / (1 + e_dist)
+
+
+def get_distance_fn(distance):
+    distance_metrics = {
+        "abs_cosine": pairwise_abs_cosine,
+        "cosine": pairwise_cosine,
+        "correlation": pairwise_corr,
+        "euclidean": pairwise_euclidean,
+    }
+
+    if isinstance(distance, str):
+        if distance not in distance_metrics:
+            raise ValueError(f"Unsupported distance metric: {distance}. Supported metrics are: {list(distance_metrics.keys())}")
+        distance_fn = distance_metrics[distance]
+    elif callable(distance):
+        distance_fn = distance
+    else:
+        raise ValueError("Distance must be either a string or a callable object.")
+    
+    return batch_processing(distance_fn)
 
 
 def random_binary_matrix(n, m, k, rng):
diff --git a/src/copairs/map/average_precision.py b/src/copairs/map/average_precision.py
index fc886e8..7084134 100644
--- a/src/copairs/map/average_precision.py
+++ b/src/copairs/map/average_precision.py
@@ -28,11 +28,12 @@ def build_rank_lists(pos_pairs, neg_pairs, pos_sims, neg_sims):
 
 
 def average_precision(
-    meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby, batch_size=20000
+    meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby, batch_size=20000, distance="cosine"
 ) -> pd.DataFrame:
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
     meta, columns = evaluate_and_filter(meta, columns)
     validate_pipeline_input(meta, feats, columns)
+    distance_fn = compute.get_distance_fn(distance)
 
     # Critical!, otherwise the indexing wont work
     meta = meta.reset_index(drop=True).copy()
@@ -62,10 +63,10 @@ def average_precision(
     )
 
     logger.info("Computing positive similarities...")
-    pos_sims = compute.pairwise_cosine(feats, pos_pairs, batch_size)
+    pos_sims = distance_fn(feats, pos_pairs, batch_size)
 
     logger.info("Computing negative similarities...")
-    neg_sims = compute.pairwise_cosine(feats, neg_pairs, batch_size)
+    neg_sims = distance_fn(feats, neg_pairs, batch_size)
 
     logger.info("Building rank lists...")
     paired_ix, rel_k_list, counts = build_rank_lists(
diff --git a/src/copairs/map/multilabel.py b/src/copairs/map/multilabel.py
index 25e5b9a..da8efde 100644
--- a/src/copairs/map/multilabel.py
+++ b/src/copairs/map/multilabel.py
@@ -74,10 +74,12 @@ def average_precision(
     neg_diffby,
     multilabel_col,
     batch_size=20000,
+    distance="cosine",
 ) -> pd.DataFrame:
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
     meta, columns = evaluate_and_filter(meta, columns)
     validate_pipeline_input(meta, feats, columns)
+    distance_fn = compute.get_distance_fn(distance)
     # Critical!, otherwise the indexing wont work
     meta = meta.reset_index(drop=True).copy()
 
@@ -114,10 +116,10 @@ def average_precision(
     neg_pairs = np.unique(neg_pairs, axis=0)
 
     logger.info("Computing positive similarities...")
-    pos_sims = compute.pairwise_cosine(feats, pos_pairs, batch_size)
+    pos_sims = distance_fn(feats, pos_pairs, batch_size)
 
     logger.info("Computing negative similarities...")
-    neg_sims = compute.pairwise_cosine(feats, neg_pairs, batch_size)
+    neg_sims = distance_fn(feats, neg_pairs, batch_size)
 
     logger.info("Computing AP per label...")
     negs_for = create_neg_query_solver(neg_pairs, neg_sims)
diff --git a/src/copairs/replicating.py b/src/copairs/replicating.py
index c1311e0..1957b40 100644
--- a/src/copairs/replicating.py
+++ b/src/copairs/replicating.py
@@ -4,14 +4,15 @@
 import numpy as np
 import pandas as pd
 
-from copairs.compute import pairwise_corr
+from copairs.compute import get_distance_fn
 from .matching import Matcher
 
 
 def corr_from_null_pairs(X: np.ndarray, null_pairs, n_replicates):
     """Correlation from a given list of unnamed pairs."""
     null_pairs = np.asarray(null_pairs, int)
-    corrs = pairwise_corr(X, null_pairs, batch_size=20000)
+    corr_fn = get_distance_fn("correlation")
+    corrs = corr_fn(X, null_pairs, batch_size=20000)
     corrs = corrs.reshape(-1, n_replicates)
     null_dist = np.nanmedian(corrs, axis=1)
     return pd.Series(null_dist)
@@ -56,7 +57,8 @@ def corr_from_pairs(X: np.ndarray, pairs: dict, sameby: List[str]):
     list-like of correlation values and median of number of replicates
     """
     pair_ix = np.vstack(list(pairs.values()))
-    corrs = pairwise_corr(X, pair_ix, batch_size=20000)
+    corr_fn = get_distance_fn("correlation")
+    corrs = corr_fn(X, pair_ix, batch_size=20000)
     counts = [len(v) for v in pairs.values()]
 
     if len(sameby) == 1:
diff --git a/tests/test_compute.py b/tests/test_compute.py
index 696d965..ca18cf1 100644
--- a/tests/test_compute.py
+++ b/tests/test_compute.py
@@ -24,6 +24,18 @@ def cosine_naive(feats, pairs):
     return cosine
 
 
+def euclidean_naive(feats, pairs):
+    euclidean_sim = np.empty((len(pairs),))
+    for pos, (i, j) in enumerate(pairs):
+        dist = np.linalg.norm(feats[i] - feats[j])
+        euclidean_sim[pos] = 1 / (1 + dist)
+    return euclidean_sim
+
+
+def abs_cosine_naive(feats, pairs):
+    return np.abs(cosine_naive(feats, pairs))
+
+
 def test_corrcoef():
     n_samples = 10
     n_pairs = 20
@@ -33,7 +45,8 @@ def test_corrcoef():
     pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
 
     corr_gt = corrcoef_naive(feats, pairs)
-    corr = compute.pairwise_corr(feats, pairs, batch_size)
+    corr_fn = compute.get_distance_fn("correlation")
+    corr = corr_fn(feats, pairs, batch_size)
     assert np.allclose(corr_gt, corr)
 
 
@@ -46,5 +59,35 @@ def test_cosine():
     pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
 
     cosine_gt = cosine_naive(feats, pairs)
-    cosine = compute.pairwise_cosine(feats, pairs, batch_size)
+    cosine_fn = compute.get_distance_fn("cosine")
+    cosine = cosine_fn(feats, pairs, batch_size)
     assert np.allclose(cosine_gt, cosine)
+
+
+def test_euclidean():
+    n_samples = 10
+    n_pairs = 20
+    n_feats = 5
+    batch_size = 4
+    feats = rng.uniform(0, 1, [n_samples, n_feats])
+    pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
+
+    euclidean_gt = euclidean_naive(feats, pairs)
+    euclidean_fn = compute.get_distance_fn("euclidean")
+    euclidean = euclidean_fn(feats, pairs, batch_size)
+    assert np.allclose(euclidean_gt, euclidean)
+
+
+def test_abs_cosine():
+    n_samples = 10
+    n_pairs = 20
+    n_feats = 5
+    batch_size = 4
+    feats = rng.uniform(0, 1, [n_samples, n_feats])
+    pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
+
+    abs_cosine_gt = abs_cosine_naive(feats, pairs)
+    abs_cosine_fn = compute.get_distance_fn("abs_cosine")
+    abs_cosine = abs_cosine_fn(feats, pairs, batch_size)
+    assert np.allclose(abs_cosine_gt, abs_cosine)
+

From 4a07fac6bb4510ec3a43cfb35af866fb7f07a5b3 Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 9 Oct 2024 13:02:22 -0400
Subject: [PATCH 23/30] chore: raise min python version to 3.9; update author
 list

---
 pyproject.toml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index ca6f34a..d56815d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,11 +3,12 @@ name = "copairs"
 version = "0.4.2"
 description = "Find pairs and compute metrics between them"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 license = {file = "LICENSE.txt"}
 keywords = ["pairwise", "replication"]
 authors = [
-  {name = "John Arevalo", email = "johnarevalo@gmail.com" }
+  { name = "John Arevalo", email = "johnarevalo@gmail.com" },
+  { name = "Alexandr Kalinin", email = "akalinin@broadinstitute.org" }
 ]
 dependencies = [
   "pandas",

From 55a5fa4c890142b7bc250dd1fb4e239eeb6c3778 Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 9 Oct 2024 13:06:45 -0400
Subject: [PATCH 24/30] chore: remove python 3.8 from github actions

---
 .github/workflows/python-package.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 4597441..7f54d22 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3

From dd6ea96c9defb9744e1677ec81390ba53a17d378 Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 16 Oct 2024 14:41:19 -0400
Subject: [PATCH 25/30] add manhattan & chebyshev distances

---
 src/copairs/compute.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index 7e977f4..f1d29b5 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -77,12 +77,24 @@ def pairwise_euclidean(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray
     return 1 / (1 + e_dist)
 
 
+def pairwise_manhattan(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    m_dist = np.sum(np.abs(x_sample - y_sample), axis=1)
+    return 1 / (1 + m_dist)
+
+
+def pairwise_chebyshev(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    c_dist = np.max(np.abs(x_sample - y_sample), axis=1)
+    return 1 / (1 + c_dist)
+
+
 def get_distance_fn(distance):
     distance_metrics = {
         "abs_cosine": pairwise_abs_cosine,
         "cosine": pairwise_cosine,
         "correlation": pairwise_corr,
         "euclidean": pairwise_euclidean,
+        "manhattan": pairwise_manhattan,
+        "chebyshev": pairwise_chebyshev,
     }
 
     if isinstance(distance, str):

From c13e66e370cefe6c0c24fc616fc0ee0da0664495 Mon Sep 17 00:00:00 2001
From: Alexandr Kalinin <1107762+alxndrkalinin@users.noreply.github.com>
Date: Wed, 16 Oct 2024 14:56:14 -0400
Subject: [PATCH 26/30] move matching demo to example notebook, update readme

---
 README.md                               | 115 +--------
 examples/finding_pairs.ipynb            | 299 ++++++++++++++++++++++++
 examples/{demo.ipynb => mAP_demo.ipynb} |   0
 3 files changed, 305 insertions(+), 109 deletions(-)
 create mode 100644 examples/finding_pairs.ipynb
 rename examples/{demo.ipynb => mAP_demo.ipynb} (100%)

diff --git a/README.md b/README.md
index 36501b6..006c024 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 ## Getting started
 
 ### System requirements
-copairs supports Python 3.8+ and should work with all modern operating systems (tested with MacOS 13.5, Ubuntu 18.04, Windows 10).
+copairs supports Python 3.9+ and should work with all modern operating systems (tested with MacOS 13.5, Ubuntu 18.04, Windows 10).
 
 ### Dependencies
 copairs depends on widely used Python packages:
@@ -19,12 +19,12 @@ copairs depends on widely used Python packages:
 
 To install copairs and dependencies, run:
 ```bash
-pip install git+https://github.com/cytomining/copairs.git@v0.4.2
+pip install copairs
 ```
 
 To also install dependencies for running examples, run:
 ```bash
-pip install copairs[demo] @ git+https://github.com/cytomining/copairs.git@v0.4.2
+pip install copairs[demo]
 ```
 
 ### Testing
@@ -38,113 +38,10 @@ pytest
 
 ## Usage
 
-### Data
+We provide examples demonstrating how to use copairs for:
+- [grouping profiles based on their metadata](./examples/finding_pairs.ipynb)
+- [calculating mAP to assess phenotypic activity and consistnecy of perturbation using real data](./examples/mAP_demo.ipynb)
 
-Say you have a dataset with 20 samples taken in 3 plates `p1, p2, p3`,
-each plate is composed of 5 wells `w1, w2, w3, w4, w5`, and each well 
-has one or more labels (`t1, t2, t3, t4`) assigned.
-
-```python
-import pandas as pd
-import random
-
-random.seed(0)
-n_samples = 20
-dframe = pd.DataFrame({
-    'plate': [random.choice(['p1', 'p2', 'p3']) for _ in range(n_samples)],
-    'well': [random.choice(['w1', 'w2', 'w3', 'w4', 'w5']) for _ in range(n_samples)],
-    'label': [random.choice(['t1', 't2', 't3', 't4']) for _ in range(n_samples)]
-})
-dframe = dframe.drop_duplicates()
-dframe = dframe.sort_values(by=['plate', 'well', 'label'])
-dframe = dframe.reset_index(drop=True)
-```
-
-|    | plate   | well   | label   |
-|---:|:--------|:-------|:--------|
-|  0 | p1      | w2     | t4      |
-|  1 | p1      | w3     | t2      |
-|  2 | p1      | w3     | t4      |
-|  3 | p1      | w4     | t1      |
-|  4 | p1      | w4     | t3      |
-|  5 | p2      | w1     | t1      |
-|  6 | p2      | w2     | t1      |
-|  7 | p2      | w3     | t1      |
-|  8 | p2      | w3     | t2      |
-|  9 | p2      | w3     | t3      |
-| 10 | p2      | w4     | t2      |
-| 11 | p2      | w5     | t1      |
-| 12 | p2      | w5     | t3      |
-| 13 | p3      | w1     | t3      |
-| 14 | p3      | w1     | t4      |
-| 15 | p3      | w4     | t2      |
-| 16 | p3      | w5     | t2      |
-| 17 | p3      | w5     | t4      |
-
-### Getting valid pairs
-
-To get pairs of samples that share the same `label` but comes from different
-`plate`s at different `well` positions: 
-
-```python
-from copairs import Matcher
-matcher = Matcher(dframe, ['plate', 'well', 'label'], seed=0)
-pairs_dict = matcher.get_all_pairs(sameby=['label'], diffby=['plate', 'well'])
-```
-
-`pairs_dict` is a `label_id: pairs` dictionary containing the list of valid
-pairs for every unique value of `labels`
-
-```
-{'t4': [(0, 17), (0, 14), (17, 2), (2, 14)],
- 't2': [(1, 16), (1, 10), (1, 15), (8, 16), (8, 15), (10, 16)],
- 't1': [(3, 11), (3, 5), (3, 6), (3, 7)],
- 't3': [(9, 4), (9, 13), (13, 4), (13, 12), (4, 12)]}
-```
-
-### Getting valid pairs from a multilabel column
-
-For eficiency reasons, you may not want to have duplicated rows. You can
-group all the labels in a single row and use `MatcherMultilabel` to find the
-corresponding pairs:
-
-```python
-dframe_multi = dframe.groupby(['plate', 'well'])['label'].unique().reset_index()
-```
-
-|    | plate   | well   | label              |
-|---:|:--------|:-------|:-------------------|
-|  0 | p1      | w2     | ['t4']             |
-|  1 | p1      | w3     | ['t2', 't4']       |
-|  2 | p1      | w4     | ['t1', 't3']       |
-|  3 | p2      | w1     | ['t1']             |
-|  4 | p2      | w2     | ['t1']             |
-|  5 | p2      | w3     | ['t1', 't2', 't3'] |
-|  6 | p2      | w4     | ['t2']             |
-|  7 | p2      | w5     | ['t1', 't3']       |
-|  8 | p3      | w1     | ['t3', 't4']       |
-|  9 | p3      | w4     | ['t2']             |
-| 10 | p3      | w5     | ['t2', 't4']       |
-
-```python
-from copairs import MatcherMultilabel
-matcher_multi = MatcherMultilabel(dframe_multi,
-                                  columns=['plate', 'well', 'label'],
-                                  multilabel_col='label',
-                                  seed=0)
-pairs_multi = matcher_multi.get_all_pairs(sameby=['label'],
-                                          diffby=['plate', 'well'])
-```
-
-`pairs_multi` is also a `label_id: pairs` dictionary with the same
-structure discussed before:
-
-```
-{'t4': [(0, 10), (0, 8), (10, 1), (1, 8)],
- 't2': [(1, 10), (1, 6), (1, 9), (5, 10), (5, 9), (6, 10)],
- 't1': [(2, 7), (2, 3), (2, 4), (2, 5)],
- 't3': [(5, 2), (5, 8), (8, 2), (8, 7), (2, 7)]}
-```
 
 ## Citation
 If you find this work useful for your research, please cite our [pre-print](https://doi.org/10.1101/2024.04.01.587631):
diff --git a/examples/finding_pairs.ipynb b/examples/finding_pairs.ipynb
new file mode 100644
index 0000000..6006f25
--- /dev/null
+++ b/examples/finding_pairs.ipynb
@@ -0,0 +1,299 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching profiles based on metadata columns\n",
+    "\n",
+    "This example demostrates how to use `copairs` to group profiles based on their metadata properties.\n",
+    "\n",
+    "Specifically, this is used in calculation of mAP for profile strength and similarity assesement.\n",
+    "\n",
+    "Citation:\n",
+    "> Kalinin, A. A. et al. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, 2024-04, (2024)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import pandas as pd\n",
+    "\n",
+    "from copairs import Matcher, MatcherMultilabel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data\n",
+    "\n",
+    "Let's assume you have a dataset with 20 samples taken in 3 plates `p1, p2, p3`,\n",
+    "each plate is composed of 5 wells `w1, w2, w3, w4, w5`, and each well \n",
+    "has one or more labels (`t1, t2, t3, t4`) assigned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.seed(0)\n",
+    "n_samples = 20\n",
+    "dframe = pd.DataFrame({\n",
+    "    'plate': [random.choice(['p1', 'p2', 'p3']) for _ in range(n_samples)],\n",
+    "    'well': [random.choice(['w1', 'w2', 'w3', 'w4', 'w5']) for _ in range(n_samples)],\n",
+    "    'label': [random.choice(['t1', 't2', 't3', 't4']) for _ in range(n_samples)]\n",
+    "})\n",
+    "dframe = dframe.drop_duplicates()\n",
+    "dframe = dframe.sort_values(by=['plate', 'well', 'label'])\n",
+    "dframe = dframe.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting valid pairs\n",
+    "\n",
+    "To get pairs of samples that share the same `label` but comes from different\n",
+    "`plate`s at different `well` positions: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'t1': [(3, 11), (3, 5), (3, 6), (3, 7)],\n",
+       " 't2': [(1, 16), (1, 10), (1, 15), (8, 16), (8, 15), (10, 16)],\n",
+       " 't3': [(9, 4), (9, 13), (13, 4), (13, 12), (4, 12)],\n",
+       " 't4': [(0, 17), (0, 14), (17, 2), (2, 14)]}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "matcher = Matcher(dframe, ['plate', 'well', 'label'], seed=0)\n",
+    "pairs_dict = matcher.get_all_pairs(sameby=['label'], diffby=['plate', 'well'])\n",
+    "pairs_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting valid pairs from a multilabel column\n",
+    "\n",
+    "For eficiency reasons, you may not want to have duplicated rows. You can\n",
+    "group all the labels in a single row and use `MatcherMultilabel` to find the\n",
+    "corresponding pairs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>plate</th>\n",
+       "      <th>well</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>p1</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>[t4]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>p1</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>[t2, t4]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>p1</td>\n",
+       "      <td>w4</td>\n",
+       "      <td>[t1, t3]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>[t1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>[t1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>[t1, t2, t3]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w4</td>\n",
+       "      <td>[t2]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w5</td>\n",
+       "      <td>[t1, t3]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p3</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>[t3, t4]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>p3</td>\n",
+       "      <td>w4</td>\n",
+       "      <td>[t2]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>p3</td>\n",
+       "      <td>w5</td>\n",
+       "      <td>[t2, t4]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   plate well         label\n",
+       "0     p1   w2          [t4]\n",
+       "1     p1   w3      [t2, t4]\n",
+       "2     p1   w4      [t1, t3]\n",
+       "3     p2   w1          [t1]\n",
+       "4     p2   w2          [t1]\n",
+       "5     p2   w3  [t1, t2, t3]\n",
+       "6     p2   w4          [t2]\n",
+       "7     p2   w5      [t1, t3]\n",
+       "8     p3   w1      [t3, t4]\n",
+       "9     p3   w4          [t2]\n",
+       "10    p3   w5      [t2, t4]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dframe_multi = dframe.groupby(['plate', 'well'])['label'].unique().reset_index()\n",
+    "dframe_multi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matcher_multi = MatcherMultilabel(dframe_multi,\n",
+    "                                  columns=['plate', 'well', 'label'],\n",
+    "                                  multilabel_col='label',\n",
+    "                                  seed=0)\n",
+    "pairs_multi = matcher_multi.get_all_pairs(sameby=['label'],\n",
+    "                                          diffby=['plate', 'well'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`pairs_multi` is also a `label_id: pairs` dictionary with the same\n",
+    "structure discussed before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'t1': [(2, 7), (2, 3), (2, 4), (2, 5)],\n",
+       " 't2': [(1, 10), (1, 6), (1, 9), (5, 10), (5, 9), (6, 10)],\n",
+       " 't3': [(5, 2), (5, 8), (8, 2), (8, 7), (2, 7)],\n",
+       " 't4': [(0, 10), (0, 8), (10, 1), (1, 8)]}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pairs_multi"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "map_benchmark",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/demo.ipynb b/examples/mAP_demo.ipynb
similarity index 100%
rename from examples/demo.ipynb
rename to examples/mAP_demo.ipynb

From 0eb53b3688cf3d2452e4c7ee7b96759780417c35 Mon Sep 17 00:00:00 2001
From: John Arevalo <johnarevalo@gmail.com>
Date: Tue, 22 Oct 2024 10:23:17 -0400
Subject: [PATCH 27/30] Support python 3.8

---
 .github/workflows/python-package.yml | 7 +++----
 pyproject.toml                       | 3 ++-
 src/copairs/map/filter.py            | 5 +++--
 3 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 7f54d22..522bba5 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3
@@ -27,9 +27,9 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip build
-        python -m pip install flake8 pytest
+        python -m pip install flake8
         python -m build
-        pip install -e .
+        pip install -e .[test]
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -38,5 +38,4 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        python -m pip install scikit-learn
         pytest
diff --git a/pyproject.toml b/pyproject.toml
index d56815d..2133055 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "copairs"
 version = "0.4.2"
 description = "Find pairs and compute metrics between them"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.8"
 license = {file = "LICENSE.txt"}
 keywords = ["pairwise", "replication"]
 authors = [
@@ -18,6 +18,7 @@ dependencies = [
 
 [project.optional-dependencies]
 plot = ["plotly"]
+test = ["scikit-learn", "pytest"]
 demo = ["notebook", "matplotlib"]
 
 [project.urls]
diff --git a/src/copairs/map/filter.py b/src/copairs/map/filter.py
index e89579e..c1a0f76 100644
--- a/src/copairs/map/filter.py
+++ b/src/copairs/map/filter.py
@@ -1,3 +1,4 @@
+from typing import Tuple, List
 import itertools
 import re
 
@@ -28,14 +29,14 @@ def flatten_str_list(*args):
     return columns
 
 
-def evaluate_and_filter(df, columns) -> tuple[pd.DataFrame, list[str]]:
+def evaluate_and_filter(df, columns) -> Tuple[pd.DataFrame, List[str]]:
     """Evaluate queries and filter the dataframe"""
     query_list, columns = extract_filters(columns, df.columns)
     df = apply_filters(df, query_list)
     return df, columns
 
 
-def extract_filters(columns, df_columns) -> tuple[list[str], list[str]]:
+def extract_filters(columns, df_columns) -> Tuple[List[str], List[str]]:
     """Extract and validate filters from columns"""
     parsed_cols = []
     queries_to_eval = []

From c3fd0c627213119b918211d2394af207145517a6 Mon Sep 17 00:00:00 2001
From: John Arevalo <johnarevalo@gmail.com>
Date: Tue, 22 Oct 2024 10:56:53 -0400
Subject: [PATCH 28/30] Format using ruff

---
 src/copairs/__init__.py              |  3 +++
 src/copairs/compute.py               |  6 ++++--
 src/copairs/map/__init__.py          |  4 +++-
 src/copairs/map/average_precision.py |  9 ++++++++-
 src/copairs/map/filter.py            | 12 +++++++-----
 src/copairs/map/multilabel.py        |  4 +---
 src/copairs/matching.py              | 10 +++++++---
 src/copairs/plot.py                  |  1 +
 src/copairs/replicating.py           |  2 ++
 tests/helpers.py                     |  2 +-
 tests/test_compute.py                |  2 --
 tests/test_map.py                    | 26 ++++++++++++++++----------
 tests/test_map_filter.py             | 10 +++-------
 tests/test_matching.py               |  1 +
 tests/test_matching_multilabel.py    |  1 +
 tests/test_replicating.py            |  3 +--
 16 files changed, 59 insertions(+), 37 deletions(-)

diff --git a/src/copairs/__init__.py b/src/copairs/__init__.py
index d07a328..ee93afd 100644
--- a/src/copairs/__init__.py
+++ b/src/copairs/__init__.py
@@ -1,4 +1,7 @@
 """
 Package to create pairwise lists based on sameby and diffby criteria
 """
+
 from .matching import Matcher, MatcherMultilabel
+
+__all__ = ["Matcher", "MatcherMultilabel"]
diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index f1d29b5..954bf01 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -99,13 +99,15 @@ def get_distance_fn(distance):
 
     if isinstance(distance, str):
         if distance not in distance_metrics:
-            raise ValueError(f"Unsupported distance metric: {distance}. Supported metrics are: {list(distance_metrics.keys())}")
+            raise ValueError(
+                f"Unsupported distance metric: {distance}. Supported metrics are: {list(distance_metrics.keys())}"
+            )
         distance_fn = distance_metrics[distance]
     elif callable(distance):
         distance_fn = distance
     else:
         raise ValueError("Distance must be either a string or a callable object.")
-    
+
     return batch_processing(distance_fn)
 
 
diff --git a/src/copairs/map/__init__.py b/src/copairs/map/__init__.py
index c646fd6..0e1998c 100644
--- a/src/copairs/map/__init__.py
+++ b/src/copairs/map/__init__.py
@@ -1,3 +1,5 @@
-from .map import mean_average_precision
 from . import multilabel
 from .average_precision import average_precision
+from .map import mean_average_precision
+
+__all__ = ["mean_average_precision", "multilabel", "average_precision"]
diff --git a/src/copairs/map/average_precision.py b/src/copairs/map/average_precision.py
index 7084134..10b481a 100644
--- a/src/copairs/map/average_precision.py
+++ b/src/copairs/map/average_precision.py
@@ -28,7 +28,14 @@ def build_rank_lists(pos_pairs, neg_pairs, pos_sims, neg_sims):
 
 
 def average_precision(
-    meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby, batch_size=20000, distance="cosine"
+    meta,
+    feats,
+    pos_sameby,
+    pos_diffby,
+    neg_sameby,
+    neg_diffby,
+    batch_size=20000,
+    distance="cosine",
 ) -> pd.DataFrame:
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
     meta, columns = evaluate_and_filter(meta, columns)
diff --git a/src/copairs/map/filter.py b/src/copairs/map/filter.py
index c1a0f76..c2956da 100644
--- a/src/copairs/map/filter.py
+++ b/src/copairs/map/filter.py
@@ -1,9 +1,9 @@
-from typing import Tuple, List
 import itertools
 import re
+from typing import List, Tuple
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 
 def validate_pipeline_input(meta, feats, columns):
@@ -45,12 +45,12 @@ def extract_filters(columns, df_columns) -> Tuple[List[str], List[str]]:
         if col in df_columns:
             parsed_cols.append(col)
             continue
-        column_names = re.findall(r'(\w+)\s*[=<>!]+', col)
+        column_names = re.findall(r"(\w+)\s*[=<>!]+", col)
 
         valid_column_names = [col for col in column_names if col in df_columns]
         if not valid_column_names:
             raise ValueError(f"Invalid query or column name: {col}")
-        
+
         queries_to_eval.append(col)
         parsed_cols.extend(valid_column_names)
 
@@ -71,6 +71,8 @@ def apply_filters(df, query_list):
         if df_filtered.empty:
             raise ValueError(f"No data matched the query: {combined_query}")
     except Exception as e:
-        raise ValueError(f"Invalid combined query expression: {combined_query}. Error: {e}")
+        raise ValueError(
+            f"Invalid combined query expression: {combined_query}. Error: {e}"
+        )
 
     return df_filtered
diff --git a/src/copairs/map/multilabel.py b/src/copairs/map/multilabel.py
index da8efde..ff124a3 100644
--- a/src/copairs/map/multilabel.py
+++ b/src/copairs/map/multilabel.py
@@ -84,9 +84,7 @@ def average_precision(
     meta = meta.reset_index(drop=True).copy()
 
     logger.info("Indexing metadata...")
-    matcher = MatcherMultilabel(
-        meta, columns, multilabel_col=multilabel_col, seed=0
-    )
+    matcher = MatcherMultilabel(meta, columns, multilabel_col=multilabel_col, seed=0)
 
     logger.info("Finding positive pairs...")
     pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
diff --git a/src/copairs/matching.py b/src/copairs/matching.py
index f7ebd68..f840b1c 100644
--- a/src/copairs/matching.py
+++ b/src/copairs/matching.py
@@ -1,11 +1,12 @@
 """
 Sample pairs with given column restrictions
 """
-from collections import namedtuple
+
 import itertools
 import logging
-from math import comb
 import re
+from collections import namedtuple
+from math import comb
 from typing import Dict, Sequence, Set, Union
 
 import numpy as np
@@ -442,5 +443,8 @@ def _only_diffby_multi(self):
         pairs = itertools.chain.from_iterable(pairs.values())
         pairs = set(map(frozenset, pairs))
         all_pairs = itertools.combinations(range(self.size), 2)
-        filter_fn = lambda x: set(x) not in pairs
+
+        def filter_fn(x):
+            return set(x) not in pairs
+
         return {None: list(filter(filter_fn, all_pairs))}
diff --git a/src/copairs/plot.py b/src/copairs/plot.py
index cbe7704..eea7010 100644
--- a/src/copairs/plot.py
+++ b/src/copairs/plot.py
@@ -1,4 +1,5 @@
 from typing import Optional
+
 from plotly import graph_objects as go
 from plotly.subplots import make_subplots
 
diff --git a/src/copairs/replicating.py b/src/copairs/replicating.py
index 1957b40..3674f42 100644
--- a/src/copairs/replicating.py
+++ b/src/copairs/replicating.py
@@ -1,10 +1,12 @@
 """Class for getting Percent replicating metric"""
+
 from typing import List, Literal
 
 import numpy as np
 import pandas as pd
 
 from copairs.compute import get_distance_fn
+
 from .matching import Matcher
 
 
diff --git a/tests/helpers.py b/tests/helpers.py
index 49a11e3..a7a4c25 100644
--- a/tests/helpers.py
+++ b/tests/helpers.py
@@ -1,8 +1,8 @@
 from itertools import product
 from typing import Dict
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 from copairs.matching import ColumnList
 
diff --git a/tests/test_compute.py b/tests/test_compute.py
index ca18cf1..63444c7 100644
--- a/tests/test_compute.py
+++ b/tests/test_compute.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 
 from copairs import compute
@@ -90,4 +89,3 @@ def test_abs_cosine():
     abs_cosine_fn = compute.get_distance_fn("abs_cosine")
     abs_cosine = abs_cosine_fn(feats, pairs, batch_size)
     assert np.allclose(abs_cosine_gt, abs_cosine)
-
diff --git a/tests/test_map.py b/tests/test_map.py
index 907ba31..816d7d8 100644
--- a/tests/test_map.py
+++ b/tests/test_map.py
@@ -1,7 +1,7 @@
+import numpy as np
 import pandas as pd
 import pytest
 from sklearn.metrics import average_precision_score
-import numpy as np
 
 from copairs import compute
 from copairs.map import average_precision
@@ -140,8 +140,8 @@ def test_raise_no_pairs():
         average_precision(meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
     with pytest.raises(UnpairedException, match="Unable to find negative pairs."):
         average_precision(meta, feats, pos_diffby, [], pos_sameby, [])
-        
-        
+
+
 def test_raise_nan_error():
     length = 10
     vocab_size = {"p": 5, "w": 3, "l": 4}
@@ -154,14 +154,20 @@ def test_raise_nan_error():
     meta = simulate_random_dframe(length, vocab_size, pos_sameby, pos_diffby, rng)
     length = len(meta)
     feats = rng.uniform(size=(length, n_feats))
-    
+
     # add null values
     feats_nan = feats.copy()
-    feats_nan[2,2] = None
+    feats_nan[2, 2] = None
     meta_nan = meta.copy()
-    meta_nan.loc[1,"p"] = None
-    
+    meta_nan.loc[1, "p"] = None
+
     with pytest.raises(ValueError, match="features should not have null values."):
-        average_precision(meta, feats_nan, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
-    with pytest.raises(ValueError, match="metadata columns should not have null values."):
-        average_precision(meta_nan, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+        average_precision(
+            meta, feats_nan, pos_sameby, pos_diffby, neg_sameby, neg_diffby
+        )
+    with pytest.raises(
+        ValueError, match="metadata columns should not have null values."
+    ):
+        average_precision(
+            meta_nan, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby
+        )
diff --git a/tests/test_map_filter.py b/tests/test_map_filter.py
index 59df5bc..9b1b311 100644
--- a/tests/test_map_filter.py
+++ b/tests/test_map_filter.py
@@ -1,6 +1,5 @@
-import pytest
-import pandas as pd
 import numpy as np
+import pytest
 
 from copairs.map.filter import evaluate_and_filter
 from tests.helpers import simulate_random_dframe
@@ -23,8 +22,8 @@ def mock_dataframe():
 def test_correct(mock_dataframe):
     df, parsed_cols = evaluate_and_filter(mock_dataframe, ["p == 'p1'", "w > 'w2'"])
     assert not df.empty
-    assert 'p' in parsed_cols and 'w' in parsed_cols
-    assert all(df['w'].str.extract(r'(\d+)')[0].astype(int) > 2)
+    assert "p" in parsed_cols and "w" in parsed_cols
+    assert all(df["w"].str.extract(r"(\d+)")[0].astype(int) > 2)
 
 
 def test_invalid_query(mock_dataframe):
@@ -44,6 +43,3 @@ def test_empty_result_from_valid_query(mock_dataframe):
     with pytest.raises(ValueError) as excinfo:
         evaluate_and_filter(mock_dataframe, ['p == "p4"'])
     assert "No data matched the query" in str(excinfo.value)
-
-
-
diff --git a/tests/test_matching.py b/tests/test_matching.py
index ad95625..5bc1132 100644
--- a/tests/test_matching.py
+++ b/tests/test_matching.py
@@ -1,4 +1,5 @@
 """Test functions for Matcher"""
+
 from string import ascii_letters
 
 import numpy as np
diff --git a/tests/test_matching_multilabel.py b/tests/test_matching_multilabel.py
index 856d29f..50f978e 100644
--- a/tests/test_matching_multilabel.py
+++ b/tests/test_matching_multilabel.py
@@ -1,4 +1,5 @@
 import pandas as pd
+
 from copairs.matching import MatcherMultilabel
 from tests.helpers import simulate_random_plates
 
diff --git a/tests/test_replicating.py b/tests/test_replicating.py
index 2e1b016..a273bbe 100644
--- a/tests/test_replicating.py
+++ b/tests/test_replicating.py
@@ -3,10 +3,9 @@
 from copairs import Matcher
 from copairs.replicating import (
     corr_between_replicates,
-    correlation_test,
     corr_from_pairs,
+    correlation_test,
 )
-
 from tests.helpers import create_dframe
 
 SEED = 0

From 4206e579f58abac1f9482e6efd355e46cd773fe7 Mon Sep 17 00:00:00 2001
From: John Arevalo <johnarevalo@gmail.com>
Date: Tue, 22 Oct 2024 11:00:08 -0400
Subject: [PATCH 29/30] Add ruff workflow. update README

---
 .github/workflows/ruff.yml | 8 ++++++++
 README.md                  | 7 +++----
 2 files changed, 11 insertions(+), 4 deletions(-)
 create mode 100644 .github/workflows/ruff.yml

diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
new file mode 100644
index 0000000..d367fa2
--- /dev/null
+++ b/.github/workflows/ruff.yml
@@ -0,0 +1,8 @@
+name: Ruff
+on: [push, pull_request]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v1
diff --git a/README.md b/README.md
index 006c024..dc8d731 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 ## Getting started
 
 ### System requirements
-copairs supports Python 3.9+ and should work with all modern operating systems (tested with MacOS 13.5, Ubuntu 18.04, Windows 10).
+copairs supports Python 3.8+ and should work with all modern operating systems (tested with MacOS 13.5, Ubuntu 18.04, Windows 10).
 
 ### Dependencies
 copairs depends on widely used Python packages:
@@ -31,8 +31,7 @@ pip install copairs[demo]
 
 To run tests, run:
 ```bash
-pip install pytest scikit-learn
-cd copairs
+pip install -e .[test]
 pytest
 ```
 
@@ -58,4 +57,4 @@ BibTeX:
   year={2024},
   doi={10.1101/2024.04.01.587631}
 }
-```
\ No newline at end of file
+```

From 7d47818d25901454f1c7b98c1b13b77d49ba9577 Mon Sep 17 00:00:00 2001
From: John Arevalo <johnarevalo@gmail.com>
Date: Tue, 22 Oct 2024 11:08:22 -0400
Subject: [PATCH 30/30] Remove flake. Add ruff format check. Format nb

---
 .github/workflows/python-package.yml | 10 +---
 .github/workflows/ruff.yml           |  3 +
 examples/finding_pairs.ipynb         | 33 +++++------
 examples/mAP_demo.ipynb              | 83 ++++++++++++++++++++--------
 4 files changed, 81 insertions(+), 48 deletions(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 522bba5..7c95642 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -26,16 +26,8 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip build
-        python -m pip install flake8
-        python -m build
+        python -m pip install --upgrade pip
         pip install -e .[test]
-    - name: Lint with flake8
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
         pytest
diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
index d367fa2..c97fd13 100644
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -6,3 +6,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - uses: astral-sh/ruff-action@v1
+      - uses: astral-sh/ruff-action@v1
+        with:
+          args: "format --check"
diff --git a/examples/finding_pairs.ipynb b/examples/finding_pairs.ipynb
index 6006f25..d8fa818 100644
--- a/examples/finding_pairs.ipynb
+++ b/examples/finding_pairs.ipynb
@@ -45,13 +45,17 @@
    "source": [
     "random.seed(0)\n",
     "n_samples = 20\n",
-    "dframe = pd.DataFrame({\n",
-    "    'plate': [random.choice(['p1', 'p2', 'p3']) for _ in range(n_samples)],\n",
-    "    'well': [random.choice(['w1', 'w2', 'w3', 'w4', 'w5']) for _ in range(n_samples)],\n",
-    "    'label': [random.choice(['t1', 't2', 't3', 't4']) for _ in range(n_samples)]\n",
-    "})\n",
+    "dframe = pd.DataFrame(\n",
+    "    {\n",
+    "        \"plate\": [random.choice([\"p1\", \"p2\", \"p3\"]) for _ in range(n_samples)],\n",
+    "        \"well\": [\n",
+    "            random.choice([\"w1\", \"w2\", \"w3\", \"w4\", \"w5\"]) for _ in range(n_samples)\n",
+    "        ],\n",
+    "        \"label\": [random.choice([\"t1\", \"t2\", \"t3\", \"t4\"]) for _ in range(n_samples)],\n",
+    "    }\n",
+    ")\n",
     "dframe = dframe.drop_duplicates()\n",
-    "dframe = dframe.sort_values(by=['plate', 'well', 'label'])\n",
+    "dframe = dframe.sort_values(by=[\"plate\", \"well\", \"label\"])\n",
     "dframe = dframe.reset_index(drop=True)"
    ]
   },
@@ -85,9 +89,8 @@
     }
    ],
    "source": [
-    "\n",
-    "matcher = Matcher(dframe, ['plate', 'well', 'label'], seed=0)\n",
-    "pairs_dict = matcher.get_all_pairs(sameby=['label'], diffby=['plate', 'well'])\n",
+    "matcher = Matcher(dframe, [\"plate\", \"well\", \"label\"], seed=0)\n",
+    "pairs_dict = matcher.get_all_pairs(sameby=[\"label\"], diffby=[\"plate\", \"well\"])\n",
     "pairs_dict"
    ]
   },
@@ -225,7 +228,7 @@
     }
    ],
    "source": [
-    "dframe_multi = dframe.groupby(['plate', 'well'])['label'].unique().reset_index()\n",
+    "dframe_multi = dframe.groupby([\"plate\", \"well\"])[\"label\"].unique().reset_index()\n",
     "dframe_multi"
    ]
   },
@@ -235,12 +238,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "matcher_multi = MatcherMultilabel(dframe_multi,\n",
-    "                                  columns=['plate', 'well', 'label'],\n",
-    "                                  multilabel_col='label',\n",
-    "                                  seed=0)\n",
-    "pairs_multi = matcher_multi.get_all_pairs(sameby=['label'],\n",
-    "                                          diffby=['plate', 'well'])"
+    "matcher_multi = MatcherMultilabel(\n",
+    "    dframe_multi, columns=[\"plate\", \"well\", \"label\"], multilabel_col=\"label\", seed=0\n",
+    ")\n",
+    "pairs_multi = matcher_multi.get_all_pairs(sameby=[\"label\"], diffby=[\"plate\", \"well\"])"
    ]
   },
   {
diff --git a/examples/mAP_demo.ipynb b/examples/mAP_demo.ipynb
index 9208df4..23a8478 100644
--- a/examples/mAP_demo.ipynb
+++ b/examples/mAP_demo.ipynb
@@ -10,7 +10,7 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from copairs import map\n"
+    "from copairs import map"
    ]
   },
   {
@@ -559,7 +559,7 @@
     "url = f\"https://media.githubusercontent.com/media/broadinstitute/lincs-cell-painting/{commit}/profiles/2016_04_01_a549_48hr_batch1/{plate}/{plate}_normalized_feature_select.csv.gz\"\n",
     "\n",
     "df = pd.read_csv(url)\n",
-    "df = df.loc[:, df.nunique() > 1] # remove constant columns\n",
+    "df = df.loc[:, df.nunique() > 1]  # remove constant columns\n",
     "df"
    ]
   },
@@ -1104,7 +1104,9 @@
     "# make index equal to -1 for all DMSO treatment replicates\n",
     "df_activity.loc[df[\"Metadata_broad_sample\"] == \"DMSO\", \"Metadata_treatment_index\"] = -1\n",
     "# now all treatment replicates differ in the index column, except for DMSO replicates\n",
-    "df_activity.insert(0, \"Metadata_treatment_index\", df_activity.pop(\"Metadata_treatment_index\"))\n",
+    "df_activity.insert(\n",
+    "    0, \"Metadata_treatment_index\", df_activity.pop(\"Metadata_treatment_index\")\n",
+    ")\n",
     "df_activity"
    ]
   },
@@ -1134,7 +1136,7 @@
     "pos_diffby = []\n",
     "\n",
     "neg_sameby = []\n",
-    "# negative pairs are replicates of different treatments \n",
+    "# negative pairs are replicates of different treatments\n",
     "neg_diffby = [\"Metadata_broad_sample\", \"Metadata_treatment_index\"]"
    ]
   },
@@ -1549,8 +1551,10 @@
     "metadata = df_activity.filter(regex=\"^Metadata\")\n",
     "profiles = df_activity.filter(regex=\"^(?!Metadata)\").values\n",
     "\n",
-    "replicate_aps = map.average_precision(metadata, profiles, pos_sameby, pos_diffby, neg_sameby, neg_diffby)\n",
-    "replicate_aps = replicate_aps.query(\"Metadata_broad_sample != 'DMSO'\") # remove DMSO\n",
+    "replicate_aps = map.average_precision(\n",
+    "    metadata, profiles, pos_sameby, pos_diffby, neg_sameby, neg_diffby\n",
+    ")\n",
+    "replicate_aps = replicate_aps.query(\"Metadata_broad_sample != 'DMSO'\")  # remove DMSO\n",
     "replicate_aps"
    ]
   },
@@ -1763,7 +1767,9 @@
     }
    ],
    "source": [
-    "replicate_maps = map.mean_average_precision(replicate_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
+    "replicate_maps = map.mean_average_precision(\n",
+    "    replicate_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0\n",
+    ")\n",
     "replicate_maps[\"-log10(p-value)\"] = -replicate_maps[\"corrected_p_value\"].apply(np.log10)\n",
     "replicate_maps.head(10)"
    ]
@@ -1794,12 +1800,21 @@
    "source": [
     "active_ratio = replicate_maps.below_corrected_p.mean()\n",
     "\n",
-    "plt.scatter(data=replicate_maps, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
-    "# 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', \n",
+    "plt.scatter(\n",
+    "    data=replicate_maps,\n",
+    "    x=\"mean_average_precision\",\n",
+    "    y=\"-log10(p-value)\",\n",
+    "    c=\"below_corrected_p\",\n",
+    "    cmap=\"tab10\",\n",
+    "    s=10,\n",
+    ")\n",
+    "# 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r',\n",
     "plt.xlabel(\"mAP\")\n",
     "plt.ylabel(\"-log10(p-value)\")\n",
     "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
-    "plt.text(0.5, 1.5, f\"Phenotypically active = {100*active_ratio:.2f}%\", va=\"center\", ha=\"left\")\n",
+    "plt.text(\n",
+    "    0.5, 1.5, f\"Phenotypically active = {100*active_ratio:.2f}%\", va=\"center\", ha=\"left\"\n",
+    ")\n",
     "plt.show()"
    ]
   },
@@ -2432,8 +2447,10 @@
    "source": [
     "# aggregate replicates by taking the median of each feature\n",
     "feature_cols = [c for c in df_consistent.columns if not c.startswith(\"Metadata\")]\n",
-    "df_consistent = df_consistent.groupby([\"Metadata_broad_sample\", \"Metadata_target\"], as_index=False)[feature_cols].median()\n",
-    "df_consistent['Metadata_target'] = df_consistent['Metadata_target'].str.split('|')\n",
+    "df_consistent = df_consistent.groupby(\n",
+    "    [\"Metadata_broad_sample\", \"Metadata_target\"], as_index=False\n",
+    ")[feature_cols].median()\n",
+    "df_consistent[\"Metadata_target\"] = df_consistent[\"Metadata_target\"].str.split(\"|\")\n",
     "df_consistent.head()"
    ]
   },
@@ -2657,13 +2674,14 @@
     "profiles = df_consistent.filter(regex=\"^(?!Metadata)\").values\n",
     "\n",
     "target_aps = map.multilabel.average_precision(\n",
-    "        metadata,\n",
-    "        profiles,\n",
-    "        pos_sameby=pos_sameby,\n",
-    "        pos_diffby=pos_diffby,\n",
-    "        neg_sameby=neg_sameby,\n",
-    "        neg_diffby=neg_diffby,\n",
-    "        multilabel_col='Metadata_target')\n",
+    "    metadata,\n",
+    "    profiles,\n",
+    "    pos_sameby=pos_sameby,\n",
+    "    pos_diffby=pos_diffby,\n",
+    "    neg_sameby=neg_sameby,\n",
+    "    neg_diffby=neg_diffby,\n",
+    "    multilabel_col=\"Metadata_target\",\n",
+    ")\n",
     "target_aps"
    ]
   },
@@ -2874,7 +2892,9 @@
     }
    ],
    "source": [
-    "target_maps = map.mean_average_precision(target_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0)\n",
+    "target_maps = map.mean_average_precision(\n",
+    "    target_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0\n",
+    ")\n",
     "target_maps[\"-log10(p-value)\"] = -target_maps[\"corrected_p_value\"].apply(np.log10)\n",
     "target_maps.head(10)"
    ]
@@ -2905,11 +2925,24 @@
    "source": [
     "consistent_ratio = target_maps.below_corrected_p.mean()\n",
     "\n",
-    "plt.scatter(data=target_maps, x=\"mean_average_precision\", y=\"-log10(p-value)\", c=\"below_corrected_p\", cmap=\"tab10\", s=10)\n",
+    "plt.scatter(\n",
+    "    data=target_maps,\n",
+    "    x=\"mean_average_precision\",\n",
+    "    y=\"-log10(p-value)\",\n",
+    "    c=\"below_corrected_p\",\n",
+    "    cmap=\"tab10\",\n",
+    "    s=10,\n",
+    ")\n",
     "plt.xlabel(\"mAP\")\n",
     "plt.ylabel(\"-log10(p-value)\")\n",
     "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
-    "plt.text(0.5, 1.5, f\"Phenotypically consistent = {100*consistent_ratio:.2f}%\", va=\"center\", ha=\"left\")\n",
+    "plt.text(\n",
+    "    0.5,\n",
+    "    1.5,\n",
+    "    f\"Phenotypically consistent = {100*consistent_ratio:.2f}%\",\n",
+    "    va=\"center\",\n",
+    "    ha=\"left\",\n",
+    ")\n",
     "\n",
     "plt.show()"
    ]
@@ -2939,7 +2972,11 @@
    ],
    "source": [
     "consistent_targets = target_maps.query(\"below_corrected_p\")[\"Metadata_target\"]\n",
-    "consistent_compounds = df_consistent[df_consistent[\"Metadata_target\"].apply(lambda x: any(t in x for t in consistent_targets))][\"Metadata_broad_sample\"]\n",
+    "consistent_compounds = df_consistent[\n",
+    "    df_consistent[\"Metadata_target\"].apply(\n",
+    "        lambda x: any(t in x for t in consistent_targets)\n",
+    "    )\n",
+    "][\"Metadata_broad_sample\"]\n",
     "\n",
     "print(f\"Phenotypically consistent targets: {consistent_targets.str.cat(sep=', ')}\")\n",
     "print(f\"Phenotypically consistent compounds: {consistent_compounds.str.cat(sep=', ')}\")"