From 20701071330757b06334ffb0f99a8bcf86f2e218 Mon Sep 17 00:00:00 2001 From: npetrill Date: Mon, 26 Aug 2024 10:09:31 -0400 Subject: [PATCH] add sctools --- tools/scripts/sctools/Dockerfile | 36 + tools/scripts/sctools/LICENSE | 27 + tools/scripts/sctools/MANIFEST.in | 3 + tools/scripts/sctools/README.rst | 157 ++ .../sctools/build/lib/sctools/__init__.py | 19 + .../scripts/sctools/build/lib/sctools/bam.py | 728 +++++++++ .../sctools/build/lib/sctools/barcode.py | 379 +++++ .../sctools/build/lib/sctools/consts.py | 41 + .../sctools/build/lib/sctools/count.py | 400 +++++ .../sctools/build/lib/sctools/encodings.py | 296 ++++ .../sctools/build/lib/sctools/fastq.py | 404 +++++ .../sctools/build/lib/sctools/groups.py | 195 +++ .../scripts/sctools/build/lib/sctools/gtf.py | 446 ++++++ .../build/lib/sctools/metrics/README.md | 59 + .../build/lib/sctools/metrics/__init__.py | 4 + .../build/lib/sctools/metrics/aggregator.py | 595 ++++++++ .../build/lib/sctools/metrics/gatherer.py | 232 +++ 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tools/scripts/sctools/src/sctools/test/test_platform.py create mode 100644 tools/scripts/sctools/src/sctools/test/test_stats.py diff --git a/tools/scripts/sctools/Dockerfile b/tools/scripts/sctools/Dockerfile new file mode 100644 index 00000000..669341dd --- /dev/null +++ b/tools/scripts/sctools/Dockerfile @@ -0,0 +1,36 @@ +FROM python:3.7.7 + +LABEL maintainer="Farzaneh Khajouei " \ + software="sctools v.1.0.0" \ + description="A collection of tools for single cell data. Splitting fastq files based on cellbarcodes and other tools to compute metrics on single cell data using barcodes and UMIs." + + +RUN apt-get update && apt-get upgrade -y && apt-get install -y patch libhdf5-dev vim apt-utils +RUN mkdir /sctools/ + +COPY . /sctools + +ARG htslib_version="1.13" + +RUN cd /sctools/fastqpreprocessing &&\ + wget https://github.com/khajoue2/libStatGen/archive/refs/tags/v1.0.15.broad.tar.gz &&\ + wget https://github.com/samtools/htslib/releases/download/${htslib_version}/htslib-${htslib_version}.tar.bz2 &&\ + tar -zxvf v1.0.15.broad.tar.gz &&\ + tar -jxvf htslib-${htslib_version}.tar.bz2 &&\ + mv libStatGen-1.0.15.broad libStatGen + +RUN cd /sctools/fastqpreprocessing &&\ + wget http://www.cs.unc.edu/Research/compgeom/gzstream/gzstream.tgz &&\ + tar -xvf gzstream.tgz + +RUN cd /sctools/fastqpreprocessing &&\ + make -C libStatGen + +RUN cd /sctools/fastqpreprocessing && make -C htslib-${htslib_version}/ && make -C gzstream + +RUN cd /sctools/fastqpreprocessing && mkdir bin obj && make install + +RUN cp /sctools/fastqpreprocessing/bin/* /usr/local/bin/ + +WORKDIR usr/local/bin/sctools + diff --git a/tools/scripts/sctools/LICENSE b/tools/scripts/sctools/LICENSE new file mode 100644 index 00000000..45035a5b --- /dev/null +++ b/tools/scripts/sctools/LICENSE @@ -0,0 +1,27 @@ +Copyright (c) 2017 Human Cell Atlas Authors, https://humancellatlas.org +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name Broad Institute, Inc. nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE \ No newline at end of file diff --git a/tools/scripts/sctools/MANIFEST.in b/tools/scripts/sctools/MANIFEST.in new file mode 100644 index 00000000..a1762055 --- /dev/null +++ b/tools/scripts/sctools/MANIFEST.in @@ -0,0 +1,3 @@ +include src/sctools/test/data/* +include README.rst +include LICENSE \ No newline at end of file diff --git a/tools/scripts/sctools/README.rst b/tools/scripts/sctools/README.rst new file mode 100644 index 00000000..0fda46ea --- /dev/null +++ b/tools/scripts/sctools/README.rst @@ -0,0 +1,157 @@ +Single Cell Tools +################# + +.. image:: https://img.shields.io/circleci/project/github/HumanCellAtlas/sctools.svg?label=Unit%20Test%20on%20Circle%20CI%20&style=flat-square&logo=circleci + :target: https://circleci.com/gh/HumanCellAtlas/sctools/tree/master + :alt: Unit Test Status + +.. image:: https://img.shields.io/codecov/c/github/HumanCellAtlas/sctools/master.svg?label=Test%20Coverage&logo=codecov&style=flat-square + :target: https://codecov.io/gh/HumanCellAtlas/sctools + :alt: Test Coverage on Codecov + +.. image:: https://img.shields.io/readthedocs/sctools/latest.svg?label=ReadtheDocs%3A%20Latest&logo=Read%20the%20Docs&style=flat-square + :target: http://sctools.readthedocs.io/en/latest/?badge=latest + :alt: Documentation Status + +.. image:: https://img.shields.io/snyk/vulnerabilities/github/HumanCellAtlas/sctools/requirements.txt.svg?label=Snyk%20Vulnerabilities&logo=Snyk + :target: https://snyk.io/test/github/HumanCellAtlas/sctools/?targetFile=requirements.txt + :alt: Snyk Vulnerabilities for GitHub Repo (Specific Manifest) + +.. image:: https://img.shields.io/github/release/HumanCellAtlas/sctools.svg?label=Latest%20Release&style=flat-square&colorB=green + :target: https://github.com/HumanCellAtlas/sctools/releases + :alt: Latest Release + +.. image:: https://img.shields.io/github/license/HumanCellAtlas/sctools.svg?style=flat-square + :target: https://img.shields.io/github/license/HumanCellAtlas/sctools.svg?style=flat-square + :alt: License + +.. image:: https://img.shields.io/badge/python-3.6-green.svg?style=flat-square&logo=python&colorB=blue + :target: https://img.shields.io/badge/python-3.6-green.svg?style=flat-square&logo=python&colorB=blue + :alt: Language + +.. image:: https://img.shields.io/badge/Code%20Style-black-000000.svg?style=flat-square + :target: https://github.com/ambv/black + :alt: Code Style + +Single Cell Tools provides utilities for manipulating sequence data formats suitable for use in +distributed systems analyzing large biological datasets. + +Download and Installation +========================= + +.. code bash + git clone https://github.com/humancellatlas/sctools.git + cd sctools + pip3 install . + pytest # verify installation; run tests + +sctools Package +=============== + +The sctools package provides both command line utilities and classes designed for use in python +programs. + +Command Line Utilities +====================== + +1. Attach10XBarcodes: Attached barcodes stored in fastq files to reads in an unaligned bam file +2. SplitBam: Split a bam file into chunks, guaranteeing that cells are contained in 1 chunk +3. CalculateGeneMetrics: Calculate information about genes in an experiment or chunk +4. CalculateCellMetrics: Calculate information about cells in an experiment or chunk +5. MergeGeneMetrics: Merge gene metrics calculated from different chunks of an experiment +6. MergeCellMetrics Merge cell metrics calculated from different chunks of an experiment + +Main Package Classes +==================== + +1. **Platform**: an abstract class that defines a common data structure for different 3' sequencing + formats. All algorithms and methods in this package that are designed to work on 3' sequencing data + speak to this common data structure. Currently 10X_v2 is defined. + +2. **Reader**: a general iterator over arbitrarily zipped file(s) that is extended to work with common + sequence formats like fastq (fastq.Reader) and gtf (gtf.Reader). We recommend using the pysam + package for reading sam and bam files. + +3. **TwoBit & ThreeBit** DNA encoders that store DNA in 2- and 3-bit form. 2-bit is smaller but + randomizes "N" nucleotides. Both classes support fastq operations over common sequence tasks such + as the calculation of GC content. + +4. **ObservedBarcodeSet & PriorBarcodeSet**: classes for analysis and comparison of sets of barcodes + such as the cell barcodes used by 10X genomics. Supports operations like summarizing hamming + distances and comparing observed sequence diversity to expected (normally uniform) diversity. + +5. **gtf.Reader & gtf.Record** GTF iterator and GTF record class that exposes the gtf + fields as a lightweight, lazy-parsed python object. + +6. **fastq.Reader & fastq.Record** fastq reader and fastq record class that exposes the fastq fields + as a lightweight, lazy-parsed python object. + +7. **Metrics** calculate information about the genes and cells of an experiment + +8. **Bam** Split bam files into chunks and attach barcodes as tags + + +Viewing Test Results and Coverage +================================= + +To calculate and view test coverage cd to the ``sctools`` directory and +type the following two commands to generate the report and open it in your web browser: + +.. code:: bash + + pytest --cov-report html:cov_html --cov=sctools + open cov_html/index.html + +Definitions +=========== + +Several definitions are helpful to understand how sequence data is analyzed. + +1. **Cell**: an individual cell, the target of single-cell RNA-seq experiments and the entity that we +wish to characterize + +2. **Capture Primer**: A DNA oligonucleotide containing amplification machinery, a fixed cell barcode, +a random molecule barcode, and an oligo-dT tail to capture poly-adenylated RNA + +3. **Molecule**: A molecule refers to a single mRNA molecule that is captured by an oligo-dT capture +primer in a single-cell sequencing experiment + +4. **Molecule Barcode**: A molecule barcode (alias: UMI, RMT) is a short, random DNA barcode attached +to the capture primer that has adequate length to be probabilistically unique across the experiment. +Therefore, when multiple molecules of the same gene are captured in the same cell, they can be +differentiated through having different molecule barcodes. The proposed GA4GH standard tag for a +molecule barcode is UB and molecule barcode qualities is UY + +5. **Cell Barcode**: A short DNA barcode that is typically selected from a whitelist of barcodes that +will be used in an experiment. All capture primers for a given cell will contain the same cell +barcode. The proposed GA4GH standard tag for a cell barcode is CB and cell barcode qualities is CY + +6. **Fragment**: During library construction, mRNA molecules captured on capture primers are amplified, +and the resulting amplified oligonucleotides are fragmented. In 3' experiments, only the fragment +that contains the 3' end is retained, but the break point will be random, which means fragments +often have different lengths. Once sequenced, different fragments can be identified as unique +combinations of cell barcode, molecule barcode, the chromosome the sequence aligns to, and the +position it aligns to on that chromosome, after correcting for clipping that the aligner may add + +7. **Bam/Sam file**: The GA4GH standard file type for the storage of aligned sequencing reads. +Unless specified, our Single Cell Tools will operate over bam files containing either aligned or +unaligned reads + +Development +=========== + +Code Style +---------- +The sctools code base is complying with the PEP-8 and using `Black `_ to +format our code, in order to avoid "nitpicky" comments during the code review process so we spend more time discussing about the logic, +not code styles. + +In order to enable the auto-formatting in the development process, you have to spend a few seconds setting +up the ``pre-commit`` the first time you clone the repo: + +1. Install ``pre-commit`` by running: ``pip install pre-commit`` (or simply run ``pip install -r requirements.txt``). +2. Run `pre-commit install` to install the git hook. + +Once you successfully install the ``pre-commit`` hook to this repo, the Black linter/formatter will be automatically triggered and run on this repo. Please make sure you followed the above steps, otherwise your commits might fail at the linting test! + +If you really want to manually trigger the linters and formatters on your code, make sure ``Black`` and ``flake8`` are installed in your Python environment and run ``flake8 DIR1 DIR2`` and ``black DIR1 DIR2 --skip-string-normalization`` respectively. diff --git a/tools/scripts/sctools/build/lib/sctools/__init__.py b/tools/scripts/sctools/build/lib/sctools/__init__.py new file mode 100644 index 00000000..1fec1fb4 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/__init__.py @@ -0,0 +1,19 @@ +# flake8: noqa +from . import bam +from . import encodings +from . import barcode +from . import fastq +from . import gtf +from . import stats +from . import reader +from . import metrics +from . import platform +from . import consts +from . import groups +from pkg_resources import get_distribution, DistributionNotFound + + +try: + __version__ = get_distribution(__name__).version +except DistributionNotFound: + pass diff --git a/tools/scripts/sctools/build/lib/sctools/bam.py b/tools/scripts/sctools/build/lib/sctools/bam.py new file mode 100644 index 00000000..d8477386 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/bam.py @@ -0,0 +1,728 @@ +""" +Tools for Manipulating SAM/BAM format files +=========================================== + +.. currentmodule:: sctools + +This module provides functions and classes to subsample reads from bam files that correspond to +specific chromosomes, split bam files into chunks, assign tags to bam files from paired fastq +records, and iterate over sorted bam files by one or more tags + +This module makes heavy use of the pysam wrapper for HTSlib, a high-performance c-library designed +to manipulate sam files + +Methods +------- +iter_tag_groups function to iterate over reads by an arbitrary tag +iter_cell_barcodes wrapper for iter_tag_groups that iterates over cell barcode tags +iter_genes wrapper for iter_tag_groups that iterates over gene tags +iter_molecules wrapper for iter_tag_groups that iterates over molecule tags +sort_by_tags_and_queryname sort bam by given list of zero or more tags, followed by query name +verify_sort verifies whether bam is correctly sorted by given list of tags, then query name + +Classes +------- +SubsetAlignments class to extract reads specific to requested chromosome(s) +Tagger class to add tags to sam/bam records from paired fastq records +AlignmentSortOrder abstract class to represent alignment sort orders +QueryNameSortOrder alignment sort order by query name +TagSortableRecord class to facilitate sorting of pysam.AlignedSegments +SortError error raised when sorting is incorrect + +References +---------- +htslib : https://github.com/samtools/htslib + +""" + +import functools +from functools import partial, reduce +import math +import os +import warnings +from abc import abstractmethod +from typing import ( + Iterator, + Iterable, + Generator, + List, + Set, + Dict, + Union, + Tuple, + Callable, + Any, + Optional, +) + +import pysam +import shutil +import multiprocessing +import uuid + +from . import consts + +# File descriptor to write log messages to +STDERR = 2 + + +class SubsetAlignments: + """Wrapper for pysam/htslib that extracts reads corresponding to requested chromosome(s) + + Parameters + ---------- + alignment_file : str + sam or bam file + open_mode : {'r', 'rb', None}, optional + open mode for pysam.AlignmentFile. 'r' indicates a sam file, 'rb' indicates a bam file, + and None attempts to autodetect based on the file suffix (Default = None) + + Methods + ------- + indices_by_chromosome + returns indices to line numbers containing the requested number of reads for a specified + chromosome + + Notes + ----- + samtools is a good general-purpose tool for that is capable of most subsampling tasks. It is a + good idea to check the samtools documentation when approaching these types of tasks. + + References + ---------- + samtools documentation : http://www.htslib.org/doc/samtools.html + + """ + + def __init__(self, alignment_file: str, open_mode: str = None): + if open_mode is None: + if alignment_file.endswith(".bam"): + open_mode = "rb" + elif alignment_file.endswith(".sam"): + open_mode = "r" + else: + raise ValueError( + f"Could not autodetect file type for alignment_file {alignment_file} (detectable suffixes: " + f".sam, .bam)" + ) + self._file: str = alignment_file + self._open_mode: str = open_mode + + def indices_by_chromosome( + self, n_specific: int, chromosome: str, include_other: int = 0 + ) -> Union[List[int], Tuple[List[int], List[int]]]: + """Return the list of first `n_specific` indices of reads aligned to `chromosome`. + + Parameters + ---------- + n_specific : int + Number of aligned reads to return indices for + chromosome : str + Only reads from this chromosome are considered valid + include_other : int, optional + The number of reads to include that are NOT aligned to chromosome. These can be aligned + or unaligned reads (default = 0). + + Returns + ------- + chromosome_indices : List[int] + list of indices to reads aligning to `chromosome` + other_indices : List[int], optional + list of indices to reads NOT aligning to chromosome, only returned if include_other is + not 0. + + """ + + # acceptable chromosomes + valid_chromosomes = [str(i) for i in range(1, 23)] + ["M", "MT", "X", "Y"] + valid_chromosomes.extend(["chr" + v for v in valid_chromosomes]) + + # check chromosome + if isinstance(chromosome, int) and chromosome < 23: + chromosome = str(chromosome) # try to convert + if chromosome not in valid_chromosomes: + warnings.warn( + "chromsome %s not in list of expected chromosomes: %r" + % (chromosome, valid_chromosomes) + ) + + with pysam.AlignmentFile(self._file, self._open_mode) as fin: + chromosome = str(chromosome) + chromosome_indices = [] + other_indices = [] + + for i, record in enumerate(fin): + + if not record.is_unmapped: # record is mapped + if chromosome == record.reference_name: + if len(chromosome_indices) < n_specific: + chromosome_indices.append(i) + elif len(other_indices) < include_other: + other_indices.append(i) + elif len(other_indices) < include_other: # record is not mapped + other_indices.append(i) + + # check termination condition (we have the requisite number of reads + if ( + len(chromosome_indices) == n_specific + and len(other_indices) == include_other + ): + break + + if len(chromosome_indices) < n_specific or len(other_indices) < include_other: + warnings.warn( + "Only %d unaligned and %d reads aligned to chromosome %s were found in" + "%s" + % (len(other_indices), len(chromosome_indices), chromosome, self._file) + ) + + if include_other != 0: + return chromosome_indices, other_indices + else: + return chromosome_indices + + +class Tagger: + """Add tags to a bam file from tag generators. + + Parameters + ---------- + bam_file : str + Bam file that tags are to be added to. + + Methods + ------- + tag + tag bam records given tag_generators (often generated from paired bam or fastq files) + # todo this should probably be wrapped up in __init__ to make this more function-like + """ + + def __init__(self, bam_file: str) -> None: + if not isinstance(bam_file, str): + raise TypeError( + f'The argument "bam_file" must be of type str, not {type(bam_file)}' + ) + self.bam_file = bam_file + + # todo add type to tag_generators (make sure it doesn't introduce import issues + def tag(self, output_bam_name: str, tag_generators) -> None: + """Add tags to bam_file. + + Given a bam file and tag generators derived from files sharing the same sort order, + adds tags to the .bam file, and writes the resulting file to output_bam_name. + + Parameters + ---------- + output_bam_name : str + Name of output tagged bam. + tag_generators : List[fastq.TagGenerator] + list of generators that yield fastq.Tag objects + + """ + with pysam.AlignmentFile( + self.bam_file, "rb", check_sq=False + ) as inbam, pysam.AlignmentFile( + output_bam_name, "wb", template=inbam + ) as outbam: + + # zip up all the iterators + for *tag_sets, sam_record in zip(*tag_generators, inbam): + for tag_set in tag_sets: + for tag in tag_set: + sam_record.set_tag(*tag) + outbam.write(sam_record) + + +def get_barcodes_from_bam( + in_bam: str, tags: List[str], raise_missing: bool +) -> Set[str]: + """Get all the distinct barcodes from a bam + + :param in_bam: str + Input bam file. + :param tags: List[str] + Tags in the bam that might contain barcodes. + :param raise_missing: bool + Raise an error if no barcodes can be found. + :return: set + A set of barcodes found in the bam + This set will not contain a None value + """ + barcodes = set() + # Get all the Barcodes from the BAM + with pysam.AlignmentFile(in_bam, "rb", check_sq=False) as input_alignments: + for alignment in input_alignments: + barcode = get_barcode_for_alignment(alignment, tags, raise_missing) + # If no provided tag was found on the record that had a non-null value + if barcode is not None: + barcodes.add(barcode) + return barcodes + + +def get_barcode_for_alignment( + alignment: pysam.AlignedSegment, tags: List[str], raise_missing: bool +) -> str: + """ Get the barcode for an Alignment + + :param alignment: pysam.AlignedSegment + An Alignment from pysam. + :param tags: List[str] + Tags in the bam that might contain barcodes. If multiple Tags are passed, will + return the contents of the first tag that contains a barcode. + :param raise_missing: bool + Raise an error if no barcodes can be found. + :return: str + A barcode for the alignment, or None if one is not found and raise_missing is False. + """ + alignment_barcode = None + for tag in tags: + # The non-existent barcode should be the exceptional case, so try/except is faster than if/else + try: + alignment_barcode = alignment.get_tag(tag) + break # Got the key, don't bother getting the next tag + except KeyError: + continue # Try to get the next tag + + if raise_missing and alignment_barcode is None: + raise RuntimeError( + "Alignment encountered that is missing {} tag(s).".format(tags) + ) + + return alignment_barcode + + +def write_barcodes_to_bins( + in_bam: str, tags: List[str], barcodes_to_bins: Dict[str, int], raise_missing: bool +) -> List[str]: + """ Write barcodes to appropriate bins as defined by barcodes_to_bins + + :param in_bam: str + The bam file to read. + :param tags: List[str] + Tags in the bam that might contain barcodes. + :param barcodes_to_bins: Dict[str, int] + A Dict from barcode to bin. All barcodes of the same type need to be written to the same bin. + These numbered bins are merged after parallelization so that all alignments with the same + barcode are in the same bam. + :param raise_missing: bool + Raise an error if no barcodes can be found. + :return: A list of paths to the written bins. + """ + # Create all the output files + with pysam.AlignmentFile(in_bam, "rb", check_sq=False) as input_alignments: + + # We need a random int appended to the dirname to make sure input bams with the same name don't clash + dirname = ( + os.path.splitext(os.path.basename(in_bam))[0] + "_" + str(uuid.uuid4()) + ) + os.makedirs(dirname) + + files = [] + bins = list(set(barcodes_to_bins.values())) + filepaths = [] + # barcode_to_bins is a dict of barcodes to ints. The ints are contiguous and are used as indices + # in the files array. The files array is an array of open file handles to write to. + for i in range(len(bins)): + out_bam_name = os.path.join(f"{dirname}", f"{dirname}_{i}.bam") + filepaths.append(out_bam_name) + + open_bam = pysam.AlignmentFile(out_bam_name, "w", template=input_alignments) + files.append(open_bam) + + # Loop over input; check each tag in priority order and partition barcodes into files based + # on the highest priority tag that is identified + for alignment in input_alignments: + barcode = get_barcode_for_alignment(alignment, tags, raise_missing) + if barcode is not None: + # Find or set the file associated with the tag and write the record to the correct file + out_file = files[barcodes_to_bins[barcode]] + out_file.write(alignment) + + for file in files: + file.close() + + return filepaths + + +def merge_bams(bams: List[str]) -> str: + """ Merge input bams using samtools. + + This cannot be a local function within `split` because then Python "cannot pickle a local object". + :param bams: Name of the final bam + bams to merge. + Because of how its called using multiprocessing, the bam basename is the first element of the list. + :return: The output bam name. + """ + bam_name = os.path.realpath(bams[0] + ".bam") + bams_to_merge = bams[1:] + pysam.merge("-c", "-p", bam_name, *bams_to_merge) + return bam_name + + +def split( + in_bams: List[str], + out_prefix: str, + tags: List[str], + approx_mb_per_split: float = 1000, + raise_missing: bool = True, + num_processes: int = None, +) -> List[str]: + """split `in_bam` by tag into files of `approx_mb_per_split` + + Parameters + ---------- + in_bams : str + Input bam files. + out_prefix : str + Prefix for all output files; output will be named as prefix_n where n is an integer equal + to the chunk number. + tags : List[str] + The bam tags to split on. The tags are checked in order, and sorting is done based on the + first identified tag. Further tags are only checked if the first tag is missing. This is + useful in cases where sorting is executed over a corrected barcode, but some records only + have a raw barcode. + approx_mb_per_split : float + The target file size for each chunk in mb + raise_missing : bool, optional + if True, raise a RuntimeError if a record is encountered without a tag. Else silently + discard the record (default = True) + num_processes : int, optional + The number of processes to parallelize over. If not set, will use all available processes. + + Returns + ------- + output_filenames : List[str] + list of filenames of bam chunks + + Raises + ------ + ValueError + when `tags` is empty + RuntimeError + when `raise_missing` is true and any passed read contains no `tags` + + """ + + if len(tags) == 0: + raise ValueError("At least one tag must be passed") + + if num_processes is None: + num_processes = multiprocessing.cpu_count() + + # find correct number of subfiles to spawn + bam_mb = sum(os.path.getsize(b) * 1e-6 for b in in_bams) + n_subfiles = int(math.ceil(bam_mb / approx_mb_per_split)) + if n_subfiles > consts.MAX_BAM_SPLIT_SUBFILES_TO_WARN: + warnings.warn( + f"Number of requested subfiles ({n_subfiles}) exceeds " + f"{consts.MAX_BAM_SPLIT_SUBFILES_TO_WARN}; this may cause OS errors by exceeding fid limits" + ) + if n_subfiles > consts.MAX_BAM_SPLIT_SUBFILES_TO_RAISE: + raise ValueError( + f"Number of requested subfiles ({n_subfiles}) exceeds " + f"{consts.MAX_BAM_SPLIT_SUBFILES_TO_RAISE}; this will usually cause OS errors, " + f"think about increasing max_mb_per_split." + ) + + full_pool = multiprocessing.Pool(num_processes) + + # Get all the barcodes over all the bams + os.write(STDERR, b"Retrieving barcodes from bams\n") + result = full_pool.map( + partial(get_barcodes_from_bam, tags=tags, raise_missing=raise_missing), in_bams + ) + + barcodes_list = list(reduce(lambda set1, set2: set1.union(set2), result)) + os.write(STDERR, b"Retrieved barcodes from bams\n") + + # Create the barcodes to bin mapping + os.write(STDERR, b"Allocating bins\n") + barcodes_to_bins_dict = {} + + # barcodes_list will always contain non-None elements from get_barcodes_from_bam + if len(barcodes_list) <= n_subfiles: + for barcode_index in range(len(barcodes_list)): + barcodes_to_bins_dict[barcodes_list[barcode_index]] = barcode_index + else: + for barcode_index in range(len(barcodes_list)): + file_index = barcode_index % n_subfiles + barcodes_to_bins_dict[barcodes_list[barcode_index]] = file_index + + # Split the bams by barcode in parallel + os.write(STDERR, b"Splitting the bams by barcode\n") + # Samtools needs a thread for compression, so we leave half the given processes open. + write_pool_processes = math.ceil(num_processes / 2) if num_processes > 2 else 1 + write_pool = multiprocessing.Pool(write_pool_processes) + scattered_split_result = write_pool.map( + partial( + write_barcodes_to_bins, + tags=list(tags), + raise_missing=raise_missing, + barcodes_to_bins=barcodes_to_bins_dict, + ), + in_bams, + ) + + bin_indices = list(set(barcodes_to_bins_dict.values())) + # Create a list of lists, where the first element of every sub-list is the name of the final output bam + bins = list([f"{out_prefix}_{index}"] for index in bin_indices) + + # A shard is the computation of writing barcodes to bins + # Gather all the files for each bin into the same sub-list. + for shard_index in range(len(scattered_split_result)): + shard = scattered_split_result[shard_index] + for file_index in range(len(shard)): + bins[file_index].append(shard[file_index]) + + write_pool.close() + + # Recombine the binned bams + os.write(STDERR, b"Merging temporary bam files\n") + merged_bams = full_pool.map(partial(merge_bams), bins) + + os.write(STDERR, b"deleting temporary files\n") + for paths in scattered_split_result: + shutil.rmtree(os.path.dirname(paths[0])) + + full_pool.close() + + return merged_bams + + +# todo change this to throw away "None" reads instead of appending them if we are filtering them +def iter_tag_groups( + tag: str, bam_iterator: Iterator[pysam.AlignedSegment], filter_null: bool = False +) -> Generator: + """Iterates over reads and yields them grouped by the provided tag value + + Parameters + ---------- + tag : str + BAM tag to group over + bam_iterator : Iterator[pysam.AlignedSegment] + open bam file that can be iterated over + filter_null : bool, optional + If False, all reads that lack the requested tag are yielded together. Else, all reads + that lack the tag will be discarded (default = False). + + Yields + ------ + grouped_by_tag : Iterator[pysam.AlignedSegment] + reads sharing a unique value of tag + current_tag : str + the tag that reads in the group all share + + """ + + # get first read and tag set + reads = [next(bam_iterator)] + try: + current_tag = reads[0].get_tag(tag) + except KeyError: + current_tag = None # null tag is a category that gets emitted + + # now iterate over alignment sets + for alignment in bam_iterator: + try: + next_tag = alignment.get_tag(tag) + except KeyError: + next_tag = None # null tag is a category that we will emit + if next_tag == current_tag: + reads.append(alignment) + else: + # only yield if the tag is non-null or filter_null is false + if not filter_null or current_tag is not None: + yield iter(reads), current_tag + # reset to next group + reads = [alignment] + current_tag = next_tag + + if not filter_null or current_tag is not None: + yield iter(reads), current_tag + + +def iter_molecule_barcodes(bam_iterator: Iterator[pysam.AlignedSegment]) -> Generator: + """Iterate over all the molecules of a bam file sorted by molecule. + + Parameters + ---------- + bam_iterator : Iterator[pysam.AlignedSegment] + open bam file that can be iterated over + + Yields + ------ + grouped_by_tag : Iterator[pysam.AlignedSegment] + reads sharing a unique molecule barcode tag + current_tag : str + the molecule barcode that records in the group all share + + """ + return iter_tag_groups( + tag=consts.MOLECULE_BARCODE_TAG_KEY, bam_iterator=bam_iterator + ) + + +def iter_cell_barcodes(bam_iterator: Iterator[pysam.AlignedSegment]) -> Generator: + """Iterate over all the cells of a bam file sorted by cell. + + Parameters + ---------- + bam_iterator : Iterator[pysam.AlignedSegment] + open bam file that can be iterated over + + Yields + ------ + grouped_by_tag : Iterator[pysam.AlignedSegment] + reads sharing a unique cell barcode tag + current_tag : str + the cell barcode that reads in the group all share + + """ + return iter_tag_groups(tag=consts.CELL_BARCODE_TAG_KEY, bam_iterator=bam_iterator) + + +def iter_genes(bam_iterator: Iterator[pysam.AlignedSegment]) -> Generator: + """Iterate over all the cells of a bam file sorted by gene. + + Parameters + ---------- + bam_iterator : Iterator[pysam.AlignedSegment] + open bam file that can be iterated over + + Yields + ------ + grouped_by_tag : Iterator[pysam.AlignedSegment] + reads sharing a unique gene name tag + current_tag : str + the gene id that reads in the group all share + + """ + return iter_tag_groups(tag=consts.GENE_NAME_TAG_KEY, bam_iterator=bam_iterator) + + +def get_tag_or_default( + alignment: pysam.AlignedSegment, tag_key: str, default: Optional[str] = None +) -> Optional[str]: + """Extracts the value associated to `tag_key` from `alignment`, and returns a default value + if the tag is not present.""" + try: + return alignment.get_tag(tag_key) + except KeyError: + return default + + +class AlignmentSortOrder: + """The base class of alignment sort orders.""" + + @property + @abstractmethod + def key_generator(self) -> Callable[[pysam.AlignedSegment], Any]: + """Returns a callable function that calculates a sort key from given pysam.AlignedSegment.""" + raise NotImplementedError + + +class QueryNameSortOrder(AlignmentSortOrder): + """Alignment record sort order by query name.""" + + @staticmethod + def get_sort_key(alignment: pysam.AlignedSegment) -> str: + return alignment.query_name + + @property + def key_generator(self): + return QueryNameSortOrder.get_sort_key + + def __repr__(self) -> str: + return "query_name" + + +@functools.total_ordering +class TagSortableRecord(object): + """Wrapper for pysam.AlignedSegment that facilitates sorting by tags and query name.""" + + def __init__( + self, + tag_keys: Iterable[str], + tag_values: Iterable[str], + query_name: str, + record: pysam.AlignedSegment = None, + ) -> None: + self.tag_keys = tag_keys + self.tag_values = tag_values + self.query_name = query_name + self.record = record + + @classmethod + def from_aligned_segment( + cls, record: pysam.AlignedSegment, tag_keys: Iterable[str] + ) -> "TagSortableRecord": + """Create a TagSortableRecord from a pysam.AlignedSegment and list of tag keys""" + assert record is not None + tag_values = [get_tag_or_default(record, key, "") for key in tag_keys] + query_name = record.query_name + return cls(tag_keys, tag_values, query_name, record) + + def __lt__(self, other: object) -> bool: + if not isinstance(other, TagSortableRecord): + return NotImplemented + self.__verify_tag_keys_match(other) + for (self_tag_value, other_tag_value) in zip(self.tag_values, other.tag_values): + if self_tag_value < other_tag_value: + return True + elif self_tag_value > other_tag_value: + return False + return self.query_name < other.query_name + + def __eq__(self, other: object) -> bool: + # TODO: Add more error checking + if not isinstance(other, TagSortableRecord): + return NotImplemented + self.__verify_tag_keys_match(other) + for (self_tag_value, other_tag_value) in zip(self.tag_values, other.tag_values): + if self_tag_value != other_tag_value: + return False + return self.query_name == other.query_name + + def __verify_tag_keys_match(self, other) -> None: + if self.tag_keys != other.tag_keys: + format_str = "Cannot compare records using different tag lists: {0}, {1}" + raise ValueError(format_str.format(self.tag_keys, other.tag_keys)) + + def __str__(self) -> str: + return self.__repr__() + + def __repr__(self) -> str: + format_str = "TagSortableRecord(tags: {0}, tag_values: {1}, query_name: {2}" + return format_str.format(self.tag_keys, self.tag_values, self.query_name) + + +def sort_by_tags_and_queryname( + records: Iterable[pysam.AlignedSegment], tag_keys: Iterable[str] +) -> Iterable[pysam.AlignedSegment]: + """Sorts the given bam records by the given tags, followed by query name. + If no tags are given, just sorts by query name. + """ + tag_sortable_records = ( + TagSortableRecord.from_aligned_segment(r, tag_keys) for r in records + ) + sorted_records = sorted(tag_sortable_records) + aligned_segments = (r.record for r in sorted_records) + return aligned_segments + + +def verify_sort(records: Iterable[TagSortableRecord], tag_keys: Iterable[str]) -> None: + """Raise AssertionError if the given records are not correctly sorted by the given tags and query name""" + # Setting tag values and query name to empty string ensures first record will never be less than old_record + old_record = TagSortableRecord( + tag_keys=tag_keys, tag_values=["" for _ in tag_keys], query_name="", record=None + ) + i = 0 + for record in records: + i += 1 + if not record >= old_record: + msg = "Records {0} and {1} are not in correct order:\n{1}:{2} \nis less than \n{0}:{3}" + raise SortError(msg.format(i - 1, i, record, old_record)) + old_record = record + + +class SortError(Exception): + pass diff --git a/tools/scripts/sctools/build/lib/sctools/barcode.py b/tools/scripts/sctools/build/lib/sctools/barcode.py new file mode 100644 index 00000000..f26aac24 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/barcode.py @@ -0,0 +1,379 @@ +""" +Nucleotide Barcode Manipulation Tools +===================================== + +.. currentmodule:: sctools + +This module contains tools to characterize oligonucleotide barcodes and a simple hamming-base +error-correction approach which corrects barcodes within a specified distance of a "whitelist" of +expected barcodes. + +Classes +------- +Barcodes Class to characterize a set of barcodes +ErrorsToCorrectBarcodesMap Class to carry out error correction routines + +""" + +import itertools +from collections import Counter +from typing import Mapping, Iterator, List, Tuple, Iterable + +import numpy as np +import pysam + +from . import consts +from .encodings import TwoBit +from .stats import base4_entropy + + +class Barcodes: + """Container for a set of nucleotide barcodes. + + Contained barcodes are encoded in 2bit representation for fast operations. Instances of this + class can optionally be constructed from an iterable where barcodes can be present multiple + times. In these cases, barcodes are analyzed based on their observed frequencies. + + Parameters + ---------- + barcodes: Mapping[str, int] + dictionary-like mapping barcodes to the number of times they were observed + barcode_length: int + the length of all barcodes in the set. Different-length barcodes are not supported. + + See Also + -------- + sctools.encodings.TwoBit + + """ + + def __init__(self, barcodes: Mapping[str, int], barcode_length: int): + if not isinstance(barcodes, Mapping): + raise TypeError( + 'The argument "barcodes" must be a dict-like object mapping barcodes to counts' + ) + self._mapping: Mapping[str, int] = barcodes + + if not isinstance(barcode_length, int) and barcode_length > 0: + raise ValueError('The argument "barcode_length" must be a positive integer') + self._barcode_length: int = barcode_length + + def __contains__(self, item) -> bool: + return item in self._mapping + + def __iter__(self) -> Iterator[str]: + return iter(self._mapping) + + def __len__(self) -> int: + return len(self._mapping) + + def __getitem__(self, item) -> int: + return self._mapping[item] + + def summarize_hamming_distances(self) -> Mapping[str, float]: + """Returns descriptive statistics on hamming distances between pairs of barcodes. + + Returns + ------- + descriptive_statistics : Mapping[str, float] + minimum, 25th percentile, median, 75th percentile, maximum, and average hamming + distance between all pairs of barcodes + + References + ---------- + https://en.wikipedia.org/wiki/Hamming_distance + + """ + distances: List = [] + + for a, b in itertools.combinations(self, 2): + distances.append(TwoBit.hamming_distance(a, b)) + + keys: Tuple = ( + "minimum", + "25th percentile", + "median", + "75th percentile", + "maximum", + "average", + ) + values: List = list(np.percentile(distances, [0, 25, 50, 75, 100])) + values.append(np.mean(distances)) + + return dict(zip(keys, values)) + + def base_frequency(self, weighted=False) -> np.ndarray: + """return the frequency of each base at each position in the barcode set + + Notes + ----- + weighting is currently not supported, and must be set to False or base_frequency will raise + NotImplementedError # todo fix + + Parameters + ---------- + weighted: bool, optional + if True, each barcode is counted once for each time it was observed (default = False) + + Returns + ------- + frequencies : np.array + barcode_length x 4 2d numpy array + + Raises + ------ + NotImplementedError + if weighted is True + + """ + base_counts_by_position: np.ndarray = np.zeros( + (self._barcode_length, 4), dtype=np.uint64 + ) + + keys: np.ndarray = np.fromiter(self._mapping.keys(), dtype=np.uint64) + + for i in reversed(range(self._barcode_length)): + binary_base_representations, counts = np.unique( + keys & 3, return_counts=True + ) + if weighted: + raise NotImplementedError + else: + base_counts_by_position[i, binary_base_representations] = counts + + # finished with this nulceotide, move two bits forward to the next one + keys >>= 2 + + return base_counts_by_position + + def effective_diversity(self, weighted=False) -> np.ndarray: + """Returns the effective base diversity of the barcode set by position. + + maximum diversity for each position is 1, and represents a perfect split of 25% per base at + a given position. + + Parameters + ---------- + weighted : bool, optional + if True, each barcode is counted once for each time it was observed (default = False) + + Returns + ------- + effective_diversity : np.array[float] + 1-d array of size barcode_length containing floats in [0, 1] + + """ + return base4_entropy(self.base_frequency(weighted=weighted)) + + @classmethod + def from_whitelist(cls, file_: str, barcode_length: int): + """Creates a barcode set from a whitelist file. + + Parameters + ---------- + file_ : str + location of the whitelist file. Should be formatted one barcode per line. Barcodes + should be encoded in plain text (UTF-8, ASCII), not bit-encoded. Each barcode will be + assigned a count of 1. + barcode_length : int + Length of the barcodes in the file. + + Returns + ------- + barcodes : Barcodes + class object containing barcodes from a whitelist file + + """ + tbe = TwoBit(barcode_length) + with open(file_, "rb") as f: + return cls( + Counter(tbe.encode(barcode[:-1]) for barcode in f), barcode_length + ) + + @classmethod + def from_iterable_encoded(cls, iterable: Iterable[int], barcode_length: int): + """Construct an ObservedBarcodeSet from an iterable of encoded barcodes. + + Parameters + ---------- + iterable : Iterable[int] + iterable of barcodes encoded in TwoBit representation + barcode_length : int + the length of the barcodes in `iterable` + + Returns + ------- + barcodes : Barcodes + class object containing barcodes from a whitelist file + """ + return cls(Counter(iterable), barcode_length=barcode_length) + + @classmethod + def from_iterable_strings(cls, iterable: Iterable[str], barcode_length: int): + """Construct an ObservedBarcodeSet from an iterable of string barcodes. + + Parameters + ---------- + iterable : Iterable[str] + iterable of barcodes encoded in TwoBit representation + barcode_length : int + the length of the barcodes in `iterable` + + Returns + ------- + barcodes : Barcodes + class object containing barcodes from a whitelist file + """ + tbe: TwoBit = TwoBit(barcode_length) + return cls( + Counter(tbe.encode(b.encode()) for b in iterable), + barcode_length=barcode_length, + ) + + @classmethod + def from_iterable_bytes(cls, iterable: Iterable[bytes], barcode_length: int): + """Construct an ObservedBarcodeSet from an iterable of bytes barcodes. + + Parameters + ---------- + iterable : Iterable[bytes] + iterable of barcodes in bytes representation + barcode_length : int + the length of the barcodes in `iterable` + + Returns + ------- + barcodes : Barcodes + class object containing barcodes from a whitelist file + """ + tbe: TwoBit = TwoBit(barcode_length) + return cls( + Counter(tbe.encode(b) for b in iterable), barcode_length=barcode_length + ) + + +class ErrorsToCorrectBarcodesMap: + """Correct any barcode that is within one hamming distance of a whitelisted barcode + + Parameters + ---------- + errors_to_barcodes : Mapping[str, str] + dict-like mapping 1-base errors to the whitelist barcode that they could be generated from + + Methods + ------- + get_corrected_barcode(barcode: str) + Return a barcode if it is whitelist, or the corrected version if within edit distance 1 + correct_bam(bam_file: str, output_bam_file: str) + correct barcodes in a bam file, given a whitelist + + References + ---------- + https://en.wikipedia.org/wiki/Hamming_distance + + """ + + def __init__(self, errors_to_barcodes: Mapping[str, str]): + if not isinstance(errors_to_barcodes, Mapping): + raise TypeError( + f'The argument "errors_to_barcodes" must be a mapping of erroneous barcodes to correct ' + f"barcodes, not {type(errors_to_barcodes)}" + ) + self._map = errors_to_barcodes + + def get_corrected_barcode(self, barcode: str) -> str: + """Return a barcode if it is whitelist, or the corrected version if within edit distance 1 + + Parameters + ---------- + barcode : str + the barcode to return the corrected version of. If the barcode is in the whitelist, + the input barcode is returned unchanged. + + Returns + ------- + corrected_barcode : str + corrected version of the barcode + + Raises + ------ + KeyError + if the passed barcode is not within 1 hamming distance of any whitelist barcode + + References + ---------- + https://en.wikipedia.org/wiki/Hamming_distance + + """ + return self._map[barcode] + + @staticmethod + def _prepare_single_base_error_hash_table( + barcodes: Iterable[str], + ) -> Mapping[str, str]: + """Generate a map of correct barcodes and single base error codes to whitelist barcodes + + Parameters + ---------- + barcodes : Iterable[str] + :param Iterable barcodes: iterable of string barcodes + :return dict: mapping between erroneous barcodes with single-base mutations and the barcode + they were generated from + """ + error_map = {} + for barcode in barcodes: + + # include correct barcode + error_map[barcode] = barcode + + # include all single-base errors + for i, nucleotide in enumerate(barcode): + errors = set("ACGTN") + errors.discard(nucleotide) + for e in errors: + error_map[barcode[:i] + e + barcode[i + 1 :]] = barcode + return error_map + + @classmethod + def single_hamming_errors_from_whitelist(cls, whitelist_file: str): + """Factory method to generate instance of class from a file containing "correct" barcodes. + + Parameters + ---------- + whitelist_file : str + Text file containing barcode per line. + + Returns + ------- + errors_to_barcodes_map : ErrorsToCorrectBarcodesMap + instance of cls, built from whitelist + + """ + with open(whitelist_file, "r") as f: + return cls( + cls._prepare_single_base_error_hash_table((line[:-1] for line in f)) + ) + + def correct_bam(self, bam_file: str, output_bam_file: str) -> None: + """Correct barcodes in a (potentially unaligned) bamfile, given a whitelist. + + Parameters + ---------- + bam_file : str + BAM format file in same order as the fastq files + output_bam_file : str + BAM format file containing cell, umi, and sample tags. + + """ + with pysam.AlignmentFile(bam_file, "rb") as fin, pysam.AlignmentFile( + output_bam_file, "wb", template=fin + ) as fout: + for alignment in fin: + try: + tag = self.get_corrected_barcode(alignment.get_tag("CR")) + except KeyError: # pass through the uncorrected barcode. + tag = alignment.get_tag(consts.RAW_CELL_BARCODE_TAG_KEY) + alignment.set_tag( + tag=consts.CELL_BARCODE_TAG_KEY, value=tag, value_type="Z" + ) + fout.write(alignment) diff --git a/tools/scripts/sctools/build/lib/sctools/consts.py b/tools/scripts/sctools/build/lib/sctools/consts.py new file mode 100644 index 00000000..e07980cb --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/consts.py @@ -0,0 +1,41 @@ +""" +Global constants +================ + +.. currentmodule:: sctools + +This module contains global constants, such as various barcoded BAM tags, and sctools-specific +constants. +""" + +# BAM tag constants + +RAW_SAMPLE_BARCODE_TAG_KEY = "SR" +QUALITY_SAMPLE_BARCODE_TAG_KEY = "SY" + +MOLECULE_BARCODE_TAG_KEY = "UB" +RAW_MOLECULE_BARCODE_TAG_KEY = "UR" +QUALITY_MOLECULE_BARCODE_TAG_KEY = "UY" + +CELL_BARCODE_TAG_KEY = "CB" +RAW_CELL_BARCODE_TAG_KEY = "CR" +QUALITY_CELL_BARCODE_TAG_KEY = "CY" + +GENE_NAME_TAG_KEY = "GE" +NUMBER_OF_HITS_TAG_KEY = "NH" + +ALIGNMENT_LOCATION_TAG_KEY = "XF" +INTRONIC_ALIGNMENT_LOCATION_TAG_VALUE = "INTRONIC" +CODING_ALIGNMENT_LOCATION_TAG_VALUE = "CODING" +UTR_ALIGNMENT_LOCATION_TAG_VALUE = "UTR" +INTERGENIC_ALIGNMENT_LOCATION_TAG_VALUE = "INTERGENIC" + +# bam.py constants + +MAX_BAM_SPLIT_SUBFILES_TO_WARN = 500 +MAX_BAM_SPLIT_SUBFILES_TO_RAISE = 1000 + + +# modes of the count matrix runs +SINGLE_CELL_COUNT_MATRIX = 0 +SINGLE_NUCLEI_COUNT_MATRIX = 1 diff --git a/tools/scripts/sctools/build/lib/sctools/count.py b/tools/scripts/sctools/build/lib/sctools/count.py new file mode 100644 index 00000000..b8d2e740 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/count.py @@ -0,0 +1,400 @@ +""" +Construct Count Matrices +======================== + +This module defines methods that enable (optionally) distributed construction of count matrices. +This module outputs coordinate sparse matrices that are converted to CSR matrices prior to delivery +for compact storage, and helper functions to convert this format into other commonly used formats. + +Methods +------- +from_sorted_tagged_bam( + bam_file: str, annotation_file: str, cell_barcode_tag: str = consts.CELL_BARCODE_TAG_KEY, + molecule_barcode_tag: str=consts.MOLECULE_BARCODE_TAG_KEY, + gene_name_tag: str=consts.GENE_NAME_TAG_KEY, open_mode: str='rb') +from_mtx(matrix_mtx: str, row_index_file: str, col_index_file: str) + + +Notes +----- +Memory usage of this module can be roughly approximated by the chunk_size parameter in Optimus. +The memory usage is equal to approximately 6*8 bytes per molecules in the file. +""" + +import itertools +import operator +from typing import List, Dict, Tuple, Set, Optional, Generator + +import numpy as np +import pysam +import scipy.sparse as sp +from scipy.io import mmread + +from sctools import consts, bam + + +class CountMatrix: + def __init__( + self, matrix: sp.csr_matrix, row_index: np.ndarray, col_index: np.ndarray + ): + self._matrix = matrix + self._row_index = row_index + self._col_index = col_index + + @property + def matrix(self): + return self._matrix + + @property + def row_index(self): + return self._row_index + + @property + def col_index(self): + return self._col_index + + @staticmethod + def _get_alignments_grouped_by_query_name_generator( + bam_file: str, + cell_barcode_tag: str, + molecule_barcode_tag: str, + open_mode: str = "rb", + ) -> Generator[ + Tuple[str, Optional[str], Optional[str], List[pysam.AlignedSegment]], None, None + ]: + """Iterates through a query_name-sorted BAM file, groups all alignments with the same query name + + Parameters + ---------- + bam_file : str + input bam file marked by cell barcode, molecule barcode, and gene ID tags sorted in that + order + cell_barcode_tag : str + Tag that specifies the cell barcode for each read. + molecule_barcode_tag : str + Tag that specifies the molecule barcode for each read. + + Returns + ------- + a generator for tuples (query_name, cell_barcode, molecule_barcode, alignments) + """ + with pysam.AlignmentFile(bam_file, mode=open_mode) as bam_records: + for (query_name, grouper) in itertools.groupby( + bam_records, key=lambda record: record.query_name + ): + alignments: List[pysam.AlignedSegment] = list(grouper) + cell_barcode: Optional[str] = bam.get_tag_or_default( + alignments[0], cell_barcode_tag + ) + molecule_barcode: Optional[str] = bam.get_tag_or_default( + alignments[0], molecule_barcode_tag + ) + yield query_name, cell_barcode, molecule_barcode, alignments + + """Looks through a list of gene locations to find the one that the given read_start ovelaps + + Parameters + ---------- + _gene_locations: Array + array with gene start end locations and names + search_start: + index of gene to start searching from + search_end: + index of gene up to which to search to + read_start: + position at which the read starts at + + Returns + ------- + name of gene with overlap or None if no overlap is found + + """ + + @classmethod + def binary_overlap(cls, _gene_locations, search_start, search_end, read_start): + while search_start <= search_end: + current_gene_index = int((search_start + search_end) / 2) + if ( + _gene_locations[current_gene_index][0][0] + < read_start + < _gene_locations[current_gene_index][0][1] + ): + return _gene_locations[current_gene_index][1] + elif _gene_locations[current_gene_index][0][0] < read_start: + search_start = current_gene_index + 1 + else: + search_end = current_gene_index - 1 + return None + + # todo add support for generating a matrix of invalid barcodes + # todo add support for splitting spliced and unspliced reads + # todo add support for generating a map of cell barcodes + # todo add the option for stringent checks on the input (e.g. BAM sort order) + # todo once the stringent checks are in place, safely move on to the hashset-free implementation + @classmethod + def from_sorted_tagged_bam( + cls, + bam_file: str, + gene_name_to_index: Dict[str, int], + chromosomes_gene_locations_extended: Dict[str, List[tuple]] = None, + cell_barcode_tag: str = consts.CELL_BARCODE_TAG_KEY, + molecule_barcode_tag: str = consts.MOLECULE_BARCODE_TAG_KEY, + gene_name_tag: str = consts.GENE_NAME_TAG_KEY, + open_mode: str = "rb", + ) -> "CountMatrix": + """Generate a count matrix from a sorted, tagged bam file + + Notes + ----- + - Input bam file must be sorted by query name. + + - The sort order of the input BAM file is not strictly checked. If the input BAM file not sorted + by query_name, the output counts will be wrong without any warnings being issued. + + This method returns counts that correspond to both spliced and unspliced reads. + + Description of the algorithm + ---------------------------- + The implemented counting strategy is intended to closely match that of CellRanger 2.1.1 + (see the references). The following pseudo-code describes the counting algorithm: + + for each query_name (i.e. unique sequenced read): + - if only a single alignment exists, _consider_ the read + - if multiple alignments exist, + - if a unique gene name is associated to all alignments that have a gene name tag, + _consider_ the read; otherwise, the read is useless and neglect it + - if the read is to be _considered_, + - if the triple (cell barcode, molecule barcode, gene name) is not encountered before, + count it as evidence for a unique transcript; otherwise, consider the read as duplicate + and neglect it + + Parameters + ---------- + bam_file : str + input bam file marked by cell barcode, molecule barcode, and gene ID tags sorted in that + order + chromosomes_gene_locations_extended : dict + Location of genes by chromosome + (default = None) + cell_barcode_tag : str, optional + Tag that specifies the cell barcode for each read. Reads without this tag will be ignored + (default = consts.CELL_BARCODE_TAG_KEY) + molecule_barcode_tag : str, optional + Tag that specifies the molecule barcode for each read. Reads without this tag will be + ignored (default = consts.MOLECULE_BARCODE_TAG_KEY) + gene_name_tag + Tag that specifies the gene name for each read. Reads without this tag will be ignored + (default = consts.GENE_NAME_TAG_KEY) + gene_name_to_index : dict + A map from gene names to their counts matrix column index + open_mode : {'r', 'rb'}, optional + indicates that the passed file is a bam file ('rb') or sam file ('r') (default = 'rb'). + + Returns + ------- + count_matrix : CountMatrix + cells x genes sparse count matrix in compressed sparse row format (cells are compressed) + + Notes + ----- + All matrices produced by this function called on different BAM chunks that share the same annotation + file can be concatenated using the scipy sparse vstack function, since by definition, the cell barcodes + contained in different BAM chunks are mutually exclusive. for example: + + >>> import scipy.sparse as sp + >>> A = sp.coo_matrix([[1, 2], [3, 4]]).tocsr() + >>> B = sp.coo_matrix([[5, 6]]).tocsr() + >>> sp.vstack([A, B]).toarray() + array([[1, 2], + [3, 4], + [5, 6]]) + + See Also + -------- + samtools sort (-t parameter): + C library that can sort files as required. + http://www.htslib.org/doc/samtools.html#COMMANDS_AND_OPTIONS + + TagSortBam.CellSortBam: + WDL task that accomplishes the sorting necessary for this module. + https://github.com/HumanCellAtlas/skylab/blob/master/library/tasks/TagSortBam.wdl + + Relevant parmalinks to the counting algorithm in CellRanger: + [1] https://github.com/10XGenomics/cellranger/blob/aba5d379169ff0d4bee60e3d100df35752b90383/mro/stages/counter/ + attach_bcs_and_umis/__init__.py + [2] https://github.com/10XGenomics/cellranger/blob/aba5d379169ff0d4bee60e3d100df35752b90383/lib/rust/ + annotate_reads/src/main.rs + """ + # map the gene from reach record to an index in the sparse matrix + n_genes = len(gene_name_to_index) + + # track which tuples (cell_barcode, molecule_barcode, gene_name) we've encountered so far + observed_cell_molecule_gene_set: Set[Tuple[str, str, str]] = set() + + # COO sparse matrix entries + data: List[int] = [] + cell_indices: List[int] = [] + gene_indices: List[int] = [] + + # track which cells we've seen, and what the current cell number is + n_cells = 0 + cell_barcode_to_index: Dict[str, int] = {} + + grouped_records_generator = cls._get_alignments_grouped_by_query_name_generator( + bam_file, cell_barcode_tag, molecule_barcode_tag, open_mode=open_mode + ) + + for ( + query_name, + cell_barcode, + molecule_barcode, + input_alignments, + ) in grouped_records_generator: + + # modify alignments to include the gene name to the alignments to INTRONIC regions + alignments = input_alignments + + # only keep queries w/ well-formed UMIs + gene_name = None + if cell_barcode is None or molecule_barcode is None: + continue + + if len(alignments) == 1: + primary_alignment = alignments[0] + if ( + primary_alignment.has_tag(gene_name_tag) + and primary_alignment.has_tag("XF") + and primary_alignment.get_tag("XF") != "INTERGENIC" + ): + gene_name = primary_alignment.get_tag(gene_name_tag) + # overlaps multiple genes, drop query, and unfortunately there only one + # one alignment for this query + if len(gene_name.split(",")) != 1: + continue + else: + continue # drop query + else: # multi-map + implicated_gene_names: Set[str] = set() + for alignment in alignments: + if ( + alignment.has_tag(gene_name_tag) + and alignment.has_tag("XF") + and alignment.get_tag("XF") != "INTERGENIC" + ): + # consider its gene name only if it has only gene name + gene_name = alignment.get_tag(gene_name_tag) + if len(gene_name.split(",")) == 1: + implicated_gene_names.add(alignment.get_tag(gene_name_tag)) + + if len(implicated_gene_names) == 1: # only one gene + gene_name = implicated_gene_names.__iter__().__next__() + else: + continue # drop query + + if gene_name is None: + continue + + if ( + cell_barcode, + molecule_barcode, + gene_name, + ) in observed_cell_molecule_gene_set: + continue # optical/PCR duplicate -> drop query + else: + observed_cell_molecule_gene_set.add( + (cell_barcode, molecule_barcode, gene_name) + ) + + # find the indices that this molecule should correspond to + gene_index = gene_name_to_index[gene_name] + + # if we've seen this cell before, get its index, else set it + try: + cell_index = cell_barcode_to_index[cell_barcode] + except KeyError: + cell_index = n_cells + cell_barcode_to_index[cell_barcode] = n_cells + n_cells += 1 + + # record the molecule data + data.append(1) # one count of this molecule + cell_indices.append(cell_index) + gene_indices.append(gene_index) + + # convert into coo_matrix + coordinate_matrix = sp.coo_matrix( + (data, (cell_indices, gene_indices)), + shape=(n_cells, n_genes), + dtype=np.uint32, + ) + + # convert to a csr sparse matrix and return + col_index = np.asarray( + [ + k + for k, v in sorted( + gene_name_to_index.items(), key=operator.itemgetter(1) + ) + ] + ) + row_index = np.asarray( + [ + k + for k, v in sorted( + cell_barcode_to_index.items(), key=operator.itemgetter(1) + ) + ] + ) + + return cls(coordinate_matrix.tocsr(), row_index, col_index) + + def save(self, prefix: str) -> None: + sp.save_npz(prefix + ".npz", self._matrix, compressed=True) + np.save(prefix + "_row_index.npy", self._row_index) + np.save(prefix + "_col_index.npy", self._col_index) + + @classmethod + def load(cls, prefix: str) -> "CountMatrix": + matrix = sp.load_npz(prefix + ".npz") + row_index = np.load(prefix + "_row_index.npy") + col_index = np.load(prefix + "_col_index.npy") + return cls(matrix, row_index, col_index) + + @classmethod + def merge_matrices(cls, input_prefixes: str) -> "CountMatrix": + col_indices = [np.load(p + "_col_index.npy") for p in input_prefixes] + row_indices = [np.load(p + "_row_index.npy") for p in input_prefixes] + matrices = [sp.load_npz(p + ".npz") for p in input_prefixes] + + matrix: sp.csr_matrix = sp.vstack(matrices, format="csr") + # todo test that col_indices are all same shape + col_index = col_indices[0] + row_index = np.concatenate(row_indices) + return cls(matrix, row_index, col_index) + + @classmethod + def from_mtx( + cls, matrix_mtx: str, row_index_file: str, col_index_file: str + ) -> "CountMatrix": + """ + + Parameters + ---------- + matrix_mtx : str + file containing count matrix in matrix market sparse format + row_index_file : str + newline delimited row index file + col_index_file : str + newline delimited column index file + + Returns + ------- + CountMatrix + instance of class + """ + matrix: sp.csr_matrix = mmread(matrix_mtx).tocsr() + with open(row_index_file, "r") as fin: + row_index = np.array(fin.readlines()) + with open(col_index_file, "r") as fin: + col_index = np.array(fin.readlines()) + return cls(matrix, row_index, col_index) diff --git a/tools/scripts/sctools/build/lib/sctools/encodings.py b/tools/scripts/sctools/build/lib/sctools/encodings.py new file mode 100644 index 00000000..85f1cef8 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/encodings.py @@ -0,0 +1,296 @@ +""" +Compressed Barcode Encoding Methods +=================================== + +.. currentmodule:: sctools + +This module defines several classes to encode DNA sequences in memory-efficient forms, using 2 bits +to encode bases of a 4-letter DNA alphabet (ACGT) or 3 bits to encode a 5-letter DNA alphabet +that includes the ambiguous call often included by Illumina base calling software (ACGTN). The +classes also contain several methods useful for efficient querying and manipulation of the encoded +sequence. + +Classes +------- +Encoding Encoder base class +ThreeBit Three bit DNA encoder / decoder +TwoBit Two bit DNA encoder / decoder + +""" + +import random +from typing import Mapping, AnyStr, Set + + +class Encoding: + """ + + Attributes + ---------- + encoding_map : TwoBitEncodingMap + Class that mimics a Mapping[bytes, str] where bytes must be a single byte encoded character + (encoder) + decoding_map : Mapping[int, bytes] + Dictionary that maps integers to bytes human-readable representations (decoder) + bits_per_base : int + number of bits used to encode each base + + Methods + ------- + encode(bytes_encoded: bytes) + encode a DNA string in a compressed representation + decode(integer_encoded: int) + decode a compressed DNA string into a human readable bytes format + gc_content(integer_encoded: int) + calculate the GC content of an encoded DNA string + hamming_distance(a: int, b: int) + calculate the hamming distance between two encoded DNA strings + + """ + + encoding_map: Mapping[AnyStr, int] = NotImplemented + decoding_map: Mapping[int, AnyStr] = NotImplemented + bits_per_base: int = NotImplemented + + @classmethod + def encode(cls, bytes_encoded: bytes) -> int: + """Encode a DNA bytes string. + + Parameters + ---------- + bytes_encoded : bytes + bytes DNA string + + Returns + ------- + encoded : int + Encoded DNA sequence + + """ + raise NotImplementedError + + def decode(self, integer_encoded: int) -> bytes: + """Decode a DNA bytes string. + + Parameters + ---------- + integer_encoded : bytes + Integer encoded DNA string + + Returns + ------- + decoded : bytes + Bytes decoded DNA sequence + + """ + raise NotImplementedError + + def gc_content(self, integer_encoded: int) -> int: + """Return the number of G or C nucleotides in `integer_encoded` + + Parameters + ---------- + integer_encoded : int + Integer encoded DNA string + + Returns + ------- + gc_content, int + number of bases in `integer_encoded` input that are G or C. + + """ + raise NotImplementedError + + @staticmethod + def hamming_distance(a, b) -> int: + """Calculate the hamming distance between two DNA sequences + + The hamming distance counts the number of bases that are not the same nucleotide + + Parameters + ---------- + a, b : int + integer encoded + + + Returns + ------- + d : int + hamming distance between a and b + """ + raise NotImplementedError + + +class TwoBit(Encoding): + """Encode a DNA sequence using a 2-bit encoding. + + Two-bit encoding uses 0 for an encoded nucleotide. As such, it cannot distinguish between + the end of sequence and trailing A nucleotides, and thus decoding these strings requires + knowledge of their length. Therefore, it is only appropriate for encoding fixed sequence + lengths + + In addition, in order to encode in 2-bit, N-nucleotides must be randomized to one of A, C, + G, and T. + + Parameters + ---------- + sequence_length : int + number of nucleotides that are being encoded + + """ + + __doc__ += Encoding.__doc__ + + def __init__(self, sequence_length: int): + self.sequence_length: int = sequence_length + + class TwoBitEncodingMap: + """Dict-like class that maps bytes to 2-bit integer representations + + Generates random nucleotides for ambiguous nucleotides e.g. N + + """ + + map_ = { + ord("A"): 0, + ord("C"): 1, + ord("T"): 2, + ord("G"): 3, + ord("a"): 0, + ord("c"): 1, + ord("t"): 2, + ord("g"): 3, + } + + iupac_ambiguous: Set[int] = {ord(c) for c in "MRWSYKVHDBNmrwsykvhdbn"} + + def __getitem__(self, byte: int) -> int: + try: + return self.map_[byte] + except KeyError: + if byte not in self.iupac_ambiguous: + raise KeyError(f"{chr(byte)} is not a valid IUPAC nucleotide code") + return random.randint(0, 3) + + encoding_map: TwoBitEncodingMap = TwoBitEncodingMap() + decoding_map: Mapping[int, bytes] = {0: b"A", 1: b"C", 2: b"T", 3: b"G"} + bits_per_base: int = 2 + + @classmethod + def encode(cls, bytes_encoded: bytes) -> int: + encoded = 0 + for character in bytes_encoded: + encoded <<= 2 + encoded += cls.encoding_map[character] + return encoded + + def decode(self, integer_encoded: int) -> bytes: + decoded = b"" + for _ in range(self.sequence_length): + decoded = self.decoding_map[integer_encoded & 3] + decoded + integer_encoded >>= 2 + return decoded + + def gc_content(self, integer_encoded: int) -> int: + i = 0 + for _ in range(self.sequence_length): + i += integer_encoded & 1 + integer_encoded >>= 2 + return i + + @staticmethod + def hamming_distance(a: int, b: int) -> int: + difference = a ^ b + d_hamming = 0 + while difference: + if difference & 3: + d_hamming += 1 + difference >>= 2 + return d_hamming + + +class ThreeBit(Encoding): + """Encode a DNA sequence using a 3-bit encoding. + + Since no bases are encoded as 0, an empty triplet is interpreted as the end of the encoded + string; Three-bit encoding can be used to encode and decode strings without knowledge of their + length. + + """ + + __doc__ += Encoding.__doc__ + + def __init__(self, *args, **kwargs): + """ + Notes + ----- + args and kwargs are not used, but allow ThreeBit to be initialized the same way as TwoBit, + despite not requiring a sequence length parameter. + + """ + pass + + class ThreeBitEncodingMap: + """Dict-like class that maps bytes to 3-bit integer representations + + All IUPAC ambiguous codes are treated as "N" + + """ + + # C: 1, A: 2, G: 3, T: 4, N: 6; # note, not using 0 + map_ = { + ord("C"): 1, + ord("A"): 2, + ord("G"): 3, + ord("T"): 4, + ord("N"): 6, + ord("c"): 1, + ord("a"): 2, + ord("g"): 3, + ord("t"): 4, + ord("n"): 6, + } + + def __getitem__(self, byte: int) -> int: + try: + return self.map_[byte] + except KeyError: + return 6 # any non-standard nucleotide gets "N" + + encoding_map: ThreeBitEncodingMap = ThreeBitEncodingMap() + decoding_map: Mapping[int, bytes] = {1: b"C", 2: b"A", 3: b"G", 4: b"T", 6: b"N"} + bits_per_base: int = 3 + + @classmethod + def encode(cls, bytes_encoded: bytes) -> int: + encoded = 0 + for character in bytes_encoded: + encoded <<= 3 + encoded += cls.encoding_map[character] + return encoded + + @classmethod + def decode(cls, integer_encoded: int) -> bytes: + decoded = b"" + while integer_encoded: + decoded = cls.decoding_map[integer_encoded & 7] + decoded + integer_encoded >>= 3 + return decoded + + @classmethod + def gc_content(cls, integer_encoded: int) -> int: + i = 0 + while integer_encoded: + i += integer_encoded & 1 + integer_encoded >>= 3 + return i + + @staticmethod + def hamming_distance(a: int, b: int) -> int: + difference = a ^ b + d_hamming = 0 + while difference: + if difference & 7: + d_hamming += 1 + difference >>= 3 + return d_hamming diff --git a/tools/scripts/sctools/build/lib/sctools/fastq.py b/tools/scripts/sctools/build/lib/sctools/fastq.py new file mode 100644 index 00000000..c6749de0 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/fastq.py @@ -0,0 +1,404 @@ +""" +Efficient Fastq Iterators and Representations +============================================= + +.. currentmodule:: sctools + +This module implements classes for representing fastq records, reading and writing them, and +extracting parts of fastq sequence for transformation into bam format tags + +Methods +------- +extract_barcode(record, embedded_barcode) + extract a barcode, defined by `embedded_barcode` from `record` + +Classes +------- +Record Represents fastq records (input as bytes) +StrRecord Represents fastq records (input as str) +Reader Opens and iterates over fastq files +EmbeddedBarcodeGenerator Generates barcodes from a fastq file +BarcodeGeneratorWithCorrectedCellBarcodes Generates (corrected) barcodes from a fastq file + +References +---------- +https://en.wikipedia.org/wiki/FASTQ_format + +""" + +from collections import namedtuple +from typing import Iterable, AnyStr, Iterator, Union, Tuple + +from . import reader, consts +from .barcode import ErrorsToCorrectBarcodesMap + + +# todo the inheritance pattern of this class is a bit confusing, particularly the str vs. bytes +# in the daughter classes +class Record: + """Fastq Record. + + Parameters + ---------- + record : Iterable[bytes] + Iterable of 4 bytes strings that comprise a fastq record + + Attributes + ---------- + name : bytes + fastq record name + sequence : bytes + fastq nucleotide sequence + name2 : bytes + second fastq record name field (rarely used) + quality : bytes + base call quality for each nucleotide in sequence + + Methods + ------- + average_quality() + The average quality of the fastq record + + """ + + __slots__ = ["_name", "_sequence", "_name2", "_quality"] + + def __init__(self, record: Iterable[AnyStr]): + # use the setter functions + self.name, self.sequence, self.name2, self.quality = record + + @property + def name(self) -> AnyStr: + return self._name + + @name.setter + def name(self, value): + """fastq record name""" + if not isinstance(value, (bytes, str)): + raise TypeError("FASTQ name must be bytes") + elif not value.startswith(b"@"): + raise ValueError("FASTQ name must start with @") + else: + self._name = value + + @property + def sequence(self) -> AnyStr: + return self._sequence + + @sequence.setter + def sequence(self, value): + """FASTQ nucleotide sequence""" + if not isinstance(value, (bytes, str)): + raise TypeError("FASTQ sequence must be str or bytes") + else: + self._sequence = value + + @property + def name2(self) -> AnyStr: + return self._name2 + + @name2.setter + def name2(self, value): + """second FASTQ record name field (rarely used)""" + if not isinstance(value, (bytes, str)): + raise TypeError("FASTQ name2 must be str or bytes") + else: + self._name2 = value + + @property + def quality(self) -> AnyStr: + return self._quality + + @quality.setter + def quality(self, value): + """FASTQ record base call quality scores""" + if not isinstance(value, (bytes, str)): + raise TypeError("FASTQ quality must be str or bytes") + else: + self._quality = value + + def __bytes__(self): + return b"".join((self.name, self.sequence, self.name2, self.quality)) + + def __str__(self): + return b"".join((self.name, self.sequence, self.name2, self.quality)).decode() + + def __repr__(self): + return "Name: %s\nSequence: %s\nName2: %s\nQuality: %s\n" % ( + self.name, + self.sequence, + self.name2, + self.quality, + ) + + def __len__(self): + return len(self.sequence) + + def average_quality(self) -> float: + """return the average quality of this record""" + # -33 due to solexa/illumina phred conversion + return sum(c for c in self.quality[:-1]) / (len(self.quality) - 1) - 33 + + +class StrRecord(Record): + """Fastq Record. + + Parameters + ---------- + record : Iterable[str] + Iterable of 4 bytes strings that comprise a FASTQ record + + Attributes + ---------- + name : str + FASTQ record name + sequence : str + FASTQ nucleotide sequence + name2 : str + second FASTQ record name field (rarely used) + quality : str + base call quality for each nucleotide in sequence + + Methods + ------- + average_quality() + The average quality of the FASTQ record + + """ + + def __bytes__(self): + return "".join((self.name, self.sequence, self.name2, self.quality)).encode() + + def __str__(self): + return "".join((self.name, self.sequence, self.name2, self.quality)) + + # todo is this method necessary? + @property + def name(self) -> str: + return self._name + + @name.setter + def name(self, value): + """FASTQ record name""" + if not isinstance(value, (bytes, str)): + raise TypeError("FASTQ name must be str or bytes") + if not value.startswith("@"): + raise ValueError("FASTQ name must start with @") + else: + self._name = value + + def average_quality(self) -> float: + """return the average quality of this record""" + b = self.quality[:-1].encode() + return ( + sum(c for c in b) / len(b) - 33 + ) # -33 due to solexa/illumina phred conversion + + +class Reader(reader.Reader): + """Fastq Reader that defines some special methods for reading and summarizing FASTQ data. + + Simple reader class that exposes an __iter__ and __len__ method + + Examples + -------- + #todo add examples + + See Also + -------- + sctools.reader.Reader + + References + ---------- + https://en.wikipedia.org/wiki/FASTQ_format + + """ + + @staticmethod + def _record_grouper(iterable): + """Groups contents of an iterator, yielding 4 objects at a time instead of one + + This is a somewhat complex python function. It creates 4 iterators on the same iterable; + each moves the pointer to the position in the iterable forward when called, yielding 4 + objects at a time + + Returns + ------- + grouped_iterator : Iterator[Str], Iterator[Str], Iterator[Str], Iterator[Str] + + """ + args = [iter(iterable)] * 4 + return zip(*args) + + def __iter__(self) -> Iterator[Tuple[str]]: + """Iterate over a FASTQ file, returning records + + Yields + ------ + fastq_record : Tuple[str] + tuple of length 4 containing the name, sequence, name2, and quality for a FASTQ record + + """ + record_type = StrRecord if self._mode == "r" else Record + for record in self._record_grouper(super().__iter__()): + yield record_type(record) + + +# namedtuple that defines the start and end position of a barcode sequence and provides the name +# for both a quality and sequence tag +EmbeddedBarcode = namedtuple("Tag", ["start", "end", "sequence_tag", "quality_tag"]) + + +def extract_barcode( + record, embedded_barcode +) -> Tuple[Tuple[str, str, str], Tuple[str, str, str]]: + """Extracts barcodes from a FASTQ record at positions defined by an EmbeddedBarcode object. + + Parameters + ---------- + record : FastqRecord + Record to extract from + embedded_barcode : EmbeddedBarcode + Defines the barcode start and end positions and the tag name for the sequence and quality + tags + + Returns + ------- + sequence_tag : Tuple[str, str, 'Z'] + sequence tag identifier, sequence, SAM tag type ('Z' implies a string tag) + quality_tag : Tuple[str, str, 'Z'] + quality tag identifier, quality, SAM tag type ('Z' implies a string tag) + + """ + seq = record.sequence[embedded_barcode.start : embedded_barcode.end] + qual = record.quality[embedded_barcode.start : embedded_barcode.end] + return ( + (embedded_barcode.sequence_tag, seq, "Z"), + (embedded_barcode.quality_tag, qual, "Z"), + ) + + +# todo the reader subclasses need better docs +class EmbeddedBarcodeGenerator(Reader): + """Generate barcodes from a FASTQ file(s) from positions defined by EmbeddedBarcode(s) + + Extracted barcode objects are produced in a form that is consumable by pysam's bam and sam + set_tag methods. + + Parameters + ---------- + embedded_barcodes : Iterable[EmbeddedBarcode] + tag objects defining start and end of the sequence containing the tag, and the tag + identifiers for sequence and quality tags + fastq_files : str | List, optional + FASTQ file or files to be read. (default = sys.stdin) + mode : {'r', 'rb'}, optional + open mode for FASTQ files. If 'r', return string. If 'rb', return bytes (default = 'r') + + """ + + def __init__(self, fastq_files, embedded_barcodes, *args, **kwargs): + super().__init__(files=fastq_files, *args, **kwargs) + self.embedded_barcodes = embedded_barcodes + + def __iter__(self): + """iterates over barcodes extracted from FASTQ""" + for record in super().__iter__(): # iterates records; we extract barcodes. + barcodes = [] + for barcode in self.embedded_barcodes: + barcodes.extend(extract_barcode(record, barcode)) + yield barcodes + + +# todo the reader subclasses need better docs +class BarcodeGeneratorWithCorrectedCellBarcodes(Reader): + """Generate barcodes from FASTQ file(s) from positions defined by EmbeddedBarcode(s) + + Extracted barcode objects are produced in a form that is consumable by pysam's bam and sam + set_tag methods. In this class, one EmbeddedBarcode must be defined as an + `embedded_cell_barcode`, which is checked against a whitelist and error corrected during + generation + + Parameters + ---------- + fastq_files : str | List, optional + FASTQ file or files to be read. (default = sys.stdin) + mode : {'r', 'rb'}, optional + open mode for fastq files. If 'r', return string. If 'rb', return bytes (default = 'r') + whitelist : str + whitelist file containing "correct" cell barcodes for an experiment + embedded_cell_barcodes : EmbeddedBarcode + EmbeddedBarcode containing information about the position and names of cell barcode tags + other_embedded_barcodes : Iterable[EmbeddedBarcode], optional + tag objects defining start and end of the sequence containing the tag, and the tag + identifiers for sequence and quality tags (default = None) + + Methods + ------- + extract_cell_barcode(record: Record, cb: str) + + """ + + def __init__( + self, + fastq_files: Union[str, Iterable[str]], + embedded_cell_barcode: EmbeddedBarcode, + whitelist: str, + other_embedded_barcodes: Iterable[EmbeddedBarcode] = tuple(), + *args, + **kwargs + ): + + super().__init__(files=fastq_files, *args, **kwargs) + if isinstance(other_embedded_barcodes, (list, tuple)): + self.embedded_barcodes = other_embedded_barcodes + else: + raise TypeError( + "if passed, other_embedded_barcodes must be a list or tuple" + ) + + self._error_mapping = ErrorsToCorrectBarcodesMap.single_hamming_errors_from_whitelist( + whitelist + ) + self.embedded_cell_barcode = embedded_cell_barcode + + def __iter__(self): + """iterates over barcodes extracted from fastq""" + for record in super().__iter__(): # iterates records; we extract barcodes. + barcodes = [] + + barcodes.extend( + self.extract_cell_barcode(record, self.embedded_cell_barcode) + ) + for barcode in self.embedded_barcodes: + barcodes.extend(extract_barcode(record, barcode)) + + yield barcodes + + def extract_cell_barcode(self, record: Tuple[str], cb: EmbeddedBarcode): + """Extract a cell barcode from a fastq record + + Parameters + ---------- + record : Tuple[str] + fastq record comprised of four strings: name, sequence, name2, and quality + cb : EmbeddedBarcode + defines the position and tag identifier for a call barcode + + Returns + ------- + sequence_tag : Tuple[str, str, 'Z'] + raw sequence tag identifier, sequence, SAM tag type ('Z' implies a string tag) + quality_tag : Tuple[str, str, 'Z'] + quality tag identifier, quality, SAM tag type ('Z' implies a string tag) + corrected_tag : Optional[Tuple[str, str, 'Z']] + Whitelist verified sequence tag. Only present if the raw sequence tag is in the + whitelist or within 1 hamming distance of one of its barcodes + + """ + seq_tag, qual_tag = extract_barcode(record, cb) + try: + corrected_cb = self._error_mapping.get_corrected_barcode(seq_tag[1]) + return seq_tag, qual_tag, (consts.CELL_BARCODE_TAG_KEY, corrected_cb, "Z") + except KeyError: + return seq_tag, qual_tag diff --git a/tools/scripts/sctools/build/lib/sctools/groups.py b/tools/scripts/sctools/build/lib/sctools/groups.py new file mode 100644 index 00000000..2a3592f2 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/groups.py @@ -0,0 +1,195 @@ +""" +Group QC outputs + +""" + +from crimson import picard +import os +import pandas as pd + + +def write_aggregated_picard_metrics_by_row(file_names, output_name): + """Command line entrypoint to parse, aggreagete and write Picard row metrics. + Parameters + ---------- + args: + file_names: array of files. the basename of inputs should be formated + as 'samplename_qc',such as + "samplename_qc.alignment_summary_metrics.txt" and "samplename_qc.insert_size_metrics.txt" + output_name: prefix of output file name without extension. + Returns + ---------- + return: 0 + return if the program completes successfully. + """ + # initial output + metrics = {} + d = pd.DataFrame() + for file_name in file_names: + cell_id = os.path.basename(file_name).split("_qc")[0] + metrics[cell_id] = {} + parsed = picard.parse(file_name) + class_name = parsed["metrics"]["class"].split(".")[2] + # Alignment metrics return multiple lines, + # but only output PAIRED-READS/third line + contents = parsed["metrics"]["contents"] + if class_name == "AlignmentSummaryMetrics": + # parse out PE, R1 and R2. If the reads are unpaired, the contents + # will be a single dict rather than a list of dicts. + if isinstance(contents, dict): + contents = [contents] + rows = {} + for m in contents: + cat = m["CATEGORY"] + rows.update( + { + k + "." + cat: v + for k, v in m.items() + if k not in ["SAMPLE", "LIBRARY", "READ_GROUP", "CATEGORY"] + } + ) + # sometimes(very rare), insertion metrics also return multiple lines + # results to include TANDEM repeats. but we only output the first line. + elif class_name == "InsertSizeMetrics": + # if the element counts is less than 21, + # it means insertion metrics returns multiple line results. + if len(contents) < 21: + rows = contents[0] + else: + rows = contents + else: + # other metrics(so far) only return one line results. + rows = contents + metrics[cell_id].update( + { + k: rows[k] + for k in rows + if k not in ["SAMPLE", "LIBRARY", "READ_GROUP", "CATEGORY"] + } + ) + df = pd.DataFrame.from_dict(metrics, orient="columns") + df.insert(0, "Class", class_name) + d = d.append(df) + d_T = d.T + d_T.to_csv(output_name + ".csv") + + +def write_aggregated_picard_metrics_by_table(file_names, output_name): + """Command line entrypoint to parse and write Picard table metrics. + Parameters + ---------- + args: + file_names: array of files.the basename of inputs should be formated as 'samplename_qc' + output_name: prefix of output file name. the basename of outputs + includes the Picard metrics class name. + Returns + ---------- + return: 0 + return if the program completes successfully. + """ + for file_name in file_names: + cell_id = os.path.basename(file_name).split("_qc")[0] + class_name = os.path.basename(file_name).split(".")[1] + parsed = picard.parse(file_name) + dat = pd.DataFrame.from_dict(parsed["metrics"]["contents"]) + dat.insert(0, "Sample", cell_id) + dat.to_csv(output_name + "_" + class_name + ".csv", index=False) + + +def write_aggregated_qc_metrics(file_names, output_name): + """Command line entrypoint to merge Picard metrics along with RSEM and HISAT2 log + Parameters + ---------- + args: + file_names: array of files,such as Picard row metric, hisat2 metrics. + output_name: prefix of output file name. + Returns + ---------- + return: 0 + return if the program completes successfully. + """ + df = pd.DataFrame() + for file_name in file_names: + dat = pd.read_csv(file_name, index_col=0) + print(dat.index) + print(df.head()) + df = pd.concat([df, dat], axis=1, join="outer") + df.to_csv(output_name + ".csv", index=True) + + +def parse_hisat2_log(file_names, output_name): + """Command line entrypoint parse, aggreagete and write HISAT2 logs + Parameters + ---------- + args: + file_names: array of HISAT2 log files. Basename of file indicates + the alignment references 'samplename_qc.log' indicates the genome reference and + 'samplename_rsem.log' indicates the transcriptome reference alignment. + output_name: prefix of output file name. + Returns + ---------- + return: 0 + return if the program completes successfully. + """ + metrics = {} + tag = "NONE" + for file_name in file_names: + if "_qc" in file_name: + cell_id = os.path.basename(file_name).split("_qc")[0] + tag = "HISAT2G" + elif "_rsem" in file_name: + cell_id = os.path.basename(file_name).split("_rsem")[0] + tag = "HISAT2T" + with open(file_name) as f: + dat = f.readlines() + d = [x.strip().split(":") for x in dat] + # remove the first row of each section. + d.pop(0) + metrics[cell_id] = {x[0]: x[1].strip().split(" ")[0] for x in d} + df = pd.DataFrame.from_dict(metrics, orient="columns") + df.insert(0, "Class", tag) + df_T = df.T + df_T.to_csv(output_name + ".csv") + + +def parse_rsem_cnt(file_names, output_name): + """Command line entrypoint parse, aggreagete and write RSEM cnt + Parameters + ---------- + args: + file_names: array of RSEM cnt files. The basename of inputs should be + 'samplename_rsem.cnt' + output_name: prefix of output file name. + Returns + ---------- + return: 0 + return if the program completes successfully. + """ + metrics = {} + for file_name in file_names: + cell_id = os.path.basename(file_name).split("_rsem")[0] + i = 0 + with open(file_name) as f: + while i < 3: + if i == 0: + [N0, N1, N2, N_tot] = f.readline().strip().split(" ") + elif i == 1: + [n_unique, n_multi, n_uncertain] = f.readline().strip().split(" ") + elif i == 2: + [n_hits, read_type] = f.readline().strip().split(" ") + i = i + 1 + metrics[cell_id] = { + "unalignable reads": N0, + "alignable reads": N1, + "filtered reads": N2, + "total reads": N_tot, + "unique aligned": n_unique, + "multiple mapped": n_multi, + "total alignments": n_hits, + "strand": read_type, + "uncertain reads": n_uncertain, + } + df = pd.DataFrame.from_dict(metrics, orient="columns") + df.insert(0, "Class", "RSEM") + df_T = df.T + df_T.to_csv(output_name + ".csv") diff --git a/tools/scripts/sctools/build/lib/sctools/gtf.py b/tools/scripts/sctools/build/lib/sctools/gtf.py new file mode 100644 index 00000000..7f574a9e --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/gtf.py @@ -0,0 +1,446 @@ +""" +GTF Records and Iterators +========================= + +.. currentmodule:: sctools + +This module defines a GTF record class and a Reader class to iterate over GTF-format files + +Classes +------- +Record Data class that exposes GTF record fields by name +Reader GTF file reader that yields GTF Records + +References +---------- +https://useast.ensembl.org/info/website/upload/gff.html +""" + +import logging +import string +import re +from typing import List, Dict, Generator, Iterable, Union, Set + +from . import reader + +_logger = logging.getLogger(__name__) + + +class GTFRecord: + """Data class for storing and interacting with GTF records + + Subclassed to produce exon, transcript, and gene-specific record types. + A GTF record has 8 fixed fields which are followed by optional fields separated by ;\t, which + are stored by this class in the attributes field and accessible by get_attribute. Fixed fields + are accessible by name. + + Parameters + ---------- + record : str + an unparsed GTF record + + Attributes + ---------- + seqname : str + The name of the sequence (often chromosome) this record is found on. + chromosome : str + Synonym for seqname. + source : str + The group responsible for generating this annotation. + feature : str + The type of record (e.g. gene, exon, ...). + start : str + The start position of this feature relative to the beginning of seqname. + end : str + The end position of this feature relative to the beginning of seqname.... + score : str + The annotation score. Rarely used. + strand : {'+', '-'} + The strand of seqname that this annotation is found on + frame : {'0', '1', '2'} + '0' indicates that the first base of the feature is the first base of a codon, + '1' that the second base is the first base of a codon, and so on + size : int + the number of nucleotides spanned by this feature + + Methods + ------- + get_attribute(key: str) + attempt to retrieve a variable field with name equal to `key` + set_attribute(key: str, value: str) + set variable field `key` equal to `value`. Overwrites `key` if already present. + + """ + + __slots__ = ["_fields", "_attributes"] + + _del_letters: str = string.ascii_letters + _del_non_letters: str = "".join( + set(string.printable).difference(string.ascii_letters) + ) + + def __init__(self, record: str): + fields: List[str] = record.strip(";\n").split("\t") + + self._fields: List[str] = fields[:8] + + self._attributes: Dict[str, str] = {} + for field in fields[8].split(";"): + try: + key, _, value = field.strip().partition(" ") + self._attributes[key] = value.strip('"') + except Exception: + raise RuntimeError( + f'Error parsing field "{field}" of GTF record "{record}"' + ) + + def __repr__(self): + return "" % self.__str__() + + def __bytes__(self): + return self.__str__().encode() + + def __str__(self): + return "\t".join(self._fields) + self._format_attribute() + "\n" + + def __hash__(self) -> int: + return hash(self.__str__()) + + def _format_attribute(self): + return " ".join('%s "%s";' % (k, v) for k, v in self._attributes.items()) + + @property + def seqname(self) -> str: + return self._fields[0] + + @property + def chromosome(self) -> str: + return self._fields[0] # synonym for seqname + + @property + def source(self) -> str: + return self._fields[1] + + @property + def feature(self) -> str: + return self._fields[2] + + @property + def start(self) -> int: + return int(self._fields[3]) + + @property + def end(self) -> int: + return int(self._fields[4]) + + @property + def score(self) -> str: + return self._fields[5] + + @property + def strand(self) -> str: + return self._fields[6] + + @property + def frame(self) -> str: + return self._fields[7] + + @property + def size(self) -> int: + size = self.end - self.start + if size < 0: + raise ValueError(f"Invalid record: negative size {size} (start > end)") + else: + return size + + def get_attribute(self, key) -> str: + """access an item from the attribute field of a GTF file. + + Parameters + ---------- + key : str + Item to retrieve + + Returns + ------- + value : str + Contents of variable attribute `key` + + Raises + ------ + KeyError + if there is no variable attribute `key` associated with this record + + """ + return self._attributes.get(key) + + def set_attribute(self, key, value) -> None: + """Set variable attribute `key` equal to `value` + + If attribute `key` is already set for this record, its contents are overwritten by `value` + + Parameters + ---------- + key : str + attribute name + value : str + attribute content + + """ + self._attributes[key] = value + + def __eq__(self, other): + return hash(self) == hash(other) + + def __ne__(self, other): + return not self.__eq__(other) + + +class Reader(reader.Reader): + """GTF file iterator + + Parameters + ---------- + files : Union[str, List], optional + File(s) to read. If '-', read sys.stdin (default = '-') + mode : {'r', 'rb'}, optional + Open mode. If 'r', read strings. If 'rb', read bytes (default = 'r'). + header_comment_char : str, optional + lines beginning with this character are skipped (default = '#') + + Methods + ------- + filter(retain_types: Iterable[str]) + Iterate over a GTF file, only yielding records in `retain_types`. + __iter__() + iterate over GTF records in file, yielding `Record` objects + + See Also + -------- + sctools.reader.Reader + + """ + + def __init__(self, files="-", mode="r", header_comment_char="#"): + super().__init__( + files, mode, header_comment_char + ) # has different default args from super + + def __iter__(self): + for line in super().__iter__(): + yield GTFRecord(line) + + def filter(self, retain_types: Iterable[str]) -> Generator: + """Iterate over a GTF file, returning only record whose feature type is in retain_types. + + Features are stored in GTF field 2. + + Parameters + ---------- + retain_types : Iterable[str] + Record feature types to retain. + + Yields + ------ + gtf_record : Record + gtf `Record` object + + """ + retain_types = set(retain_types) + for record in self: + if record.feature in retain_types: + yield record + + +# todo this lenient behavior is deemed to change in the future (warning -> exception) +def _resolve_multiple_gene_names(gene_name: str): + _logger.warning( + f'Multiple entries encountered for "{gene_name}". Please validate the input GTF file(s). ' + f"Skipping the record for now; in the future, this will be considered as a " + f"malformed GTF file." + ) + + +def get_mitochondrial_gene_names( + files: Union[str, List[str]] = "-", mode: str = "r", header_comment_char: str = "#" +) -> Set[str]: + """Extract mitocholdrial gene names from GTF file(s) and returns a set of mitochondrial + gene id occurrence in the given file(s). + + Parameters + ---------- + files : Union[str, List], optional + File(s) to read. If '-', read sys.stdin (default = '-') + mode : {'r', 'rb'}, optional + Open mode. If 'r', read strings. If 'rb', read bytes (default = 'r'). + header_comment_char : str, optional + lines beginning with this character are skipped (default = '#') + + Returns + ------- + Set(str) + A set of the mitochondrial gene ids + """ + + mitochondrial_gene_ids: Set[str] = set() + for record in Reader(files, mode, header_comment_char).filter( + retain_types=["gene"] + ): + gene_name = record.get_attribute("gene_name") + gene_id = record.get_attribute("gene_id") + + if gene_name is None: + raise ValueError( + f"Malformed GTF file detected. Record is of type gene but does not have a " + f'"gene_name" field: {record}' + ) + if re.match("^mt-", gene_name, re.IGNORECASE): + if gene_id not in mitochondrial_gene_ids: + mitochondrial_gene_ids.add(gene_id) + + return mitochondrial_gene_ids + + +def extract_gene_names( + files: Union[str, List[str]] = "-", mode: str = "r", header_comment_char: str = "#" +) -> Dict[str, int]: + """Extract gene names from GTF file(s) and returns a map from gene names to their corresponding + occurrence orders in the given file(s). + + Parameters + ---------- + files : Union[str, List], optional + File(s) to read. If '-', read sys.stdin (default = '-') + mode : {'r', 'rb'}, optional + Open mode. If 'r', read strings. If 'rb', read bytes (default = 'r'). + header_comment_char : str, optional + lines beginning with this character are skipped (default = '#') + + Returns + ------- + Dict[str, int] + A map from gene names to their linear index + """ + gene_name_to_index: Dict[str, int] = dict() + gene_index = 0 + for record in Reader(files, mode, header_comment_char).filter( + retain_types=["gene"] + ): + gene_name = record.get_attribute("gene_name") + if gene_name is None: + raise ValueError( + f"Malformed GTF file detected. Record is of type gene but does not have a " + f'"gene_name" field: {record}' + ) + if gene_name in gene_name_to_index: + _resolve_multiple_gene_names(gene_name) + continue + gene_name_to_index[gene_name] = gene_index + gene_index += 1 + return gene_name_to_index + + +def extract_extended_gene_names( + files: Union[str, List[str]] = "-", mode: str = "r", header_comment_char: str = "#" +) -> Dict[str, List[tuple]]: + """Extract extended gene names from GTF file(s) and returns a map from gene names to their corresponding + occurrence locations the given file(s). + + Parameters + ---------- + files : Union[str, List], optional + File(s) to read. If '-', read sys.stdin (default = '-') + mode : {'r', 'rb'}, optional + Open mode. If 'r', read strings. If 'rb', read bytes (default = 'r'). + header_comment_char : str, optional + lines beginning with this character are skipped (default = '#') + + Returns + ------- + Dict[str, List[tuple]] + A dictionary of chromosome names mapping to a List of tuples, each containing + a range as the the first element and a gene name as the second. + Dict[str, List(Tuple((start,end), gene))) + """ + gene_name_to_start_end = dict() + for record in Reader(files, mode, header_comment_char).filter( + retain_types=["gene"] + ): + gene_name = record.get_attribute("gene_name") + if gene_name is None: + raise ValueError( + f"Malformed GTF file detected. Record is of type gene but does not have a " + f'"gene_name" field: {record}' + ) + # find gene collisions + if gene_name in gene_name_to_start_end: + _resolve_multiple_gene_names(gene_name) + continue + if record.chromosome not in gene_name_to_start_end: + gene_name_to_start_end[record.chromosome] = dict() + gene_name_to_start_end[record.chromosome][gene_name] = ( + record.start, + record.end, + ) + gene_locations = dict() + # For each chromosome invert the map to be in List[( (start,end), genename )] and sort it by start + for chromosome in gene_name_to_start_end: + gene_locations[chromosome] = [ + (locs, key) for key, locs in gene_name_to_start_end[chromosome].items() + ] + # Sort by starting location + gene_locations[chromosome].sort(key=lambda x: x[0]) + return gene_locations + + +def extract_gene_exons( + files: Union[str, List[str]] = "-", mode: str = "r", header_comment_char: str = "#" +) -> Dict[str, List[tuple]]: + """Extract extended gene names from GTF file(s) and returns a map from gene names to the the + list of exons in the ascending order of the start positions file(s). + + Parameters + ---------- + files : Union[str, List], optional + File(s) to read. If '-', read sys.stdin (default = '-') + mode : {'r', 'rb'}, optional + Open mode. If 'r', read strings. If 'rb', read bytes (default = 'r'). + header_comment_char : str, optional + lines beginning with this character are skipped (default = '#') + + Returns + ------- + Dict[str, List[tuple]] + A dictionary of chromosome names mapping to a List of tuples, each containing + a the exons in the ascending order of the start positions. + Dict[str, List(Tuple((start,end), gene))) + """ + gene_name_to_start_end = dict() + for record in Reader(files, mode, header_comment_char).filter( + retain_types=["exon"] + ): + gene_name = record.get_attribute("gene_name") + if gene_name is None: + raise ValueError( + f"Malformed GTF file detected. Record is of type gene but does not have a " + f'"gene_name" field: {record}' + ) + if record.chromosome not in gene_name_to_start_end: + gene_name_to_start_end[record.chromosome] = dict() + + if gene_name not in gene_name_to_start_end[record.chromosome]: + gene_name_to_start_end[record.chromosome][gene_name] = [] + + gene_name_to_start_end[record.chromosome][gene_name].append( + (record.start, record.end) + ) + + gene_locations_exons = dict() + # For each chromosome invert the map to be in List[( (start,end), genename )] and sort it by start + for chromosome in gene_name_to_start_end: + gene_locations_exons[chromosome] = [ + (locs, key) for key, locs in gene_name_to_start_end[chromosome].items() + ] + # Sort by starting location + gene_locations_exons[chromosome].sort(key=lambda x: x[0]) + return gene_locations_exons diff --git a/tools/scripts/sctools/build/lib/sctools/metrics/README.md b/tools/scripts/sctools/build/lib/sctools/metrics/README.md new file mode 100644 index 00000000..8ee554ae --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/metrics/README.md @@ -0,0 +1,59 @@ +## Metric Processing +This module implements a metric suite that generates information on data quality at the level of +both cells and genes. This QC information aligns with the cells and genes that make up the +expression matrix, providing easy access to information that the user can examine to make decisions +about which cells or genes are of adequate quality to include in downstream processing. + +Metric processing in sctools can be run on large individual files, but also implements a map-reduce +architecture execution at production scale. Specifically, the workflow is as follows: + +1. Chunk the input bam file using `SplitBam`, which generates several chunks, each of which is +guaranteed to contain all data for any cell it contains +2. Sort each chunk by cell, gene, and molecule tags to ensure that all the reads associated with +a molecule are stored sequentially by cell (`CalculateCellMetrics`) or by gene +(`CalculateGeneMetrics`) +3. For each cell or gene, parse the information by molecule, which typically loads fewer than +10,000 records into memory at a time. +4. Merge data across chunks using `MergeCellMetrics` or `MergeGeneMetrics`. + +This map-reduce approach is currently implemented by the +[HCA 3' pipeline](https://github.com/HumanCellAtlas/skylab/blob/master/pipelines/optimus/Optimus.wdl), +but an abbreviated WDL could be made in the future which would contain: + +``` +1. SplitBamByCellBarcode +2. scatter[CalculateMetrics] +3. MergeMetrics +``` + +## Implementation Details: + +This module implements 4 base classes that carry out metric processing. These are: + +``` +MetricAggregator: + - CellMetricAggregator + - GeneMetricAggregator + +MetricGatherer: + - CellMetricGatherer + - GeneMetricGatherer + +MetricCSVWriter + +MergeMetrics: + - MergeCellMetrics + - MergeGeneMetrics +``` +MetricGatherer defines generator functions to group records into molecules, the bam parsing pattern +necessary to process data iteratively. + +MetricAggregator stores the information for a unit of the relevant data (cell, gene), +and processses all the records with the `.parse_records()` method. + +When all records of a single unit (cell, gene) have been processed, `.finalize()` is called to +calculate any higher-order metrics (for example, the variance in quality scores across reads of the +cell or gene), and it is written to file by `MetricSCVWriter`. + +MergeMetrics merges multiple metric outputs from the scattered chunks. This is a trivial +concatenation in the case of cell metrics, and a more complex merge in the case of gene metrics. diff --git a/tools/scripts/sctools/build/lib/sctools/metrics/__init__.py b/tools/scripts/sctools/build/lib/sctools/metrics/__init__.py new file mode 100644 index 00000000..9ba20677 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/metrics/__init__.py @@ -0,0 +1,4 @@ +# flake8: noqa +from . import aggregator +from . import gatherer +from . import merge diff --git a/tools/scripts/sctools/build/lib/sctools/metrics/aggregator.py b/tools/scripts/sctools/build/lib/sctools/metrics/aggregator.py new file mode 100644 index 00000000..2d85199d --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/metrics/aggregator.py @@ -0,0 +1,595 @@ +""" +Sequence Metric Aggregators +=========================== + +.. currentmodule:: sctools.metrics + +This module provides classes useful for aggregating metric information for individual cells or +genes. These classes consume BAM files that have been pre-sorted such that all sequencing reads +that correspond to the molecules of a cell (CellMetrics) or the molecules of a gene (GeneMetrics) +are yielded sequentially. + +Classes +------- + +.. autosummary:: + :toctree: generated/ + + MetricAggregatorBase Aggregator Base Class + GeneMetrics Class to iteratively calculate metrics for a gene (by molecule) + CellMetrics Class to iteratively calculate metrics for a cell (by molecule) + +Notes +----- +This module can be rewritten with dataclass when python 3.7 stabilizes, see +https://www.python.org/dev/peps/pep-0557/ + + +See Also +-------- +sctools.metrics.gatherer +sctools.metrics.merge +sctools.metrics.writer + +""" + + +from typing import Iterable, Tuple, Counter, List, Sequence + +import numpy as np +import pysam + +from sctools import consts +from sctools.stats import OnlineGaussianSufficientStatistic + + +class MetricAggregator: + """Metric Aggregator Base Class + + The ``MetricAggregator`` class defines a set of metrics that can be extracted from an + aligned bam file. It defines all the metrics that are general across genes and cells. This + class is subclassed by ``GeneMetrics`` and ``CellMetrics``, which define data-specific metrics + in the ``parse_extra_fields`` method. An instance of ``GeneMetrics`` or ``CellMetrics`` is + instantiated for each gene or molecule in a bam file, respectively. + + Attributes + ---------- + n_reads : int + The number of reads associated with this entity + noise_reads : int, NotImplemented + Number of reads that are categorized by 10x genomics cellranger as "noise". Refers to + long polymers, or reads with high numbers of N (ambiguous) nucleotides + perfect_molecule_barcodes : int + The number of reads with molecule barcodes that have no errors (cell barcode tag == raw barcode tag) + reads_mapped_exonic : int + The number of reads for this entity that are mapped to exons + reads_mapped_intronic : int + The number of reads for this entity that are mapped to introns + reads_mapped_utr : int + The number of reads for this entity that are mapped to 3' untranslated regions (UTRs) + reads_mapped_uniquely : int + The number of reads mapped to a single unambiguous location in the genome + reads_mapped_multiple : int + The number of reads mapped to multiple genomic positions with equal confidence + # todo make sure equal confidence is accurate + duplicate_reads : int + The number of reads that are duplicates (see README.md for defition of a duplicate) + spliced_reads : int + The number of reads that overlap splicing junctions + antisense_reads : int + The number of reads that are mapped to the antisense strand instead of the transcribed + strand + molecule_barcode_fraction_bases_above_30_mean : float + The average fraction of bases in molecule barcodes that receive quality scores greater than + 30 across the reads of this entity + molecule_barcode_fraction_bases_above_30_variance : float + The variance in the fraction of bases in molecule barcodes that receive quality scores + greater than 30 across the reads of this entity + genomic_reads_fraction_bases_quality_above_30_mean : float + The average fraction of bases in the genomic read that receive quality scores greater than + 30 across the reads of this entity (included for 10x cell ranger count comparison) + genomic_reads_fraction_bases_quality_above_30_variance : float + The variance in the fraction of bases in the genomic read that receive quality scores + greater than 30 across the reads of this entity (included for 10x cell ranger count + comparison) + genomic_read_quality_mean : float + Average quality of Illumina base calls in the genomic reads corresponding to this entity + genomic_read_quality_variance : float + Variance in quality of Illumina base calls in the genomic reads corresponding to this + entity + n_molecules : float + Number of molecules corresponding to this entity. See README.md for the definition of a + Molecule + n_fragments : float + Number of fragments corresponding to this entity. See README.md for the definition of a + Fragment + reads_per_molecule : float + The average number of reads associated with each molecule in this entity + reads_per_fragment : float + The average number of reads associated with each fragment in this entity + fragments_per_molecule : float + The average number of fragments associated with each molecule in this entity + fragments_with_single_read_evidence : int + The number of fragments associated with this entity that are observed by only one read + molecules_with_single_read_evidence : int + The number of molecules associated with this entity that are observed by only one read + + Methods + ------- + parse_extra_fields(tags, record), NotImplemented + Abstract method that must be implemented by subclasses. Called by ``parse_molecule()`` + to gather information for subclass-specific metrics + parse_molecule(tags, record) + Extract information from a set of sequencing reads that correspond to a molecule and store + the data in the MetricAggregator class. + finalize() + Some metrics cannot be calculated until all the information for an entity has been + aggregated, for example, the number of `fragments_per_molecule`. Finalize calculates all + such higher-order metrics + + """ + + def __init__(self): + + # type definitions + Chromosome: int + Strand: bool # reverse = True, see pysam.AlignedSegment.is_reverse + Position: int + Fragment: Tuple[Chromosome, Position, Strand] # noqa: F821 + + # count information + self.n_reads: int = 0 + self.noise_reads: int = 0 # long polymers, N-sequences; NotImplemented + self._fragment_histogram: Counter[Fragment] = Counter() # noqa: F821 + self._molecule_histogram: Counter[str] = Counter() + + # molecule information + self._molecule_barcode_fraction_bases_above_30 = ( + OnlineGaussianSufficientStatistic() + ) + self.perfect_molecule_barcodes = 0 + + self._genomic_reads_fraction_bases_quality_above_30 = ( + OnlineGaussianSufficientStatistic() + ) + self._genomic_read_quality = OnlineGaussianSufficientStatistic() + + # alignment location information + self.reads_mapped_exonic = 0 + self.reads_mapped_intronic = 0 + self.reads_mapped_utr = 0 + + # todo implement this once we have a gene model + # self.reads_mapped_outside_window = 0 # reads should be within 1000 bases of UTR + # self._read_distance_from_termination_site = OnlineGaussianSufficientStatistic() + + # alignment uniqueness information + self.reads_mapped_uniquely = 0 + self.reads_mapped_multiple = 0 + self.duplicate_reads = 0 + + # alignment splicing information + self.spliced_reads = 0 + self.antisense_reads = 0 + self._plus_strand_reads = 0 # strand balance # todo implement property here + + # higher-order methods, filled in by finalize() when all data is extracted + self.molecule_barcode_fraction_bases_above_30_mean: float = None + self.molecule_barcode_fraction_bases_above_30_variance: float = None + self.genomic_reads_fraction_bases_quality_above_30_mean: float = None + self.genomic_reads_fraction_bases_quality_above_30_variance: float = None + self.genomic_read_quality_mean: float = None + self.genomic_read_quality_variance: float = None + self.n_molecules: float = None + self.n_fragments: float = None + self.reads_per_molecule: float = None + self.reads_per_fragment: float = None + self.fragments_per_molecule: float = None + self.fragments_with_single_read_evidence: int = None + self.molecules_with_single_read_evidence: int = None + + @staticmethod + def _quality_string_to_numeric(quality_sequence: Iterable[str]) -> List[int]: + """Convert an HTSlib ASCII quality string to an integer representation. + + Parameters + ---------- + quality_sequence : Iterable[str] + An iterable of Illumina base call qualities in ASCII encoding + + Returns + ------- + numeric_qualities : List[int] + A list of Illumina base call qualities converted to integers + + """ + return [ + ord(c) - 33 for c in quality_sequence + ] # todo look up if this is accurate + + @staticmethod + def _quality_above_threshold( + threshold: int, quality_sequence: Sequence[int] + ) -> float: + """Calculate the fraction of bases called with a quality above ``threshold``. + + Parameters + ---------- + threshold: int + The quality threshold + quality_sequence: Sequence[int] + A sequence of Illumina base qualities + + Returns + ------- + fraction : float + The fraction of bases in ``quality_sequence`` with quality greater than ``threshold`` + + """ + return sum(1 for base in quality_sequence if base > threshold) / len( + quality_sequence + ) + + def _is_noise(self, record: pysam.AlignedSegment) -> bool: + return NotImplemented # todo required because 10x measures this + + def parse_molecule( + self, tags: Sequence[str], records: Iterable[pysam.AlignedSegment] + ) -> None: + """Parse information from all records of a molecule. + + The parsed information is stored in the MetricAggregator in-place. + + Parameters + ---------- + tags : Sequence[str] + all the tags that define this molecule. one of {[CB, GE, UB], [GE, CB, UB]} + records : Iterable[pysam.AlignedSegment] + the sam records associated with the molecule + + """ + for record in records: + + # todo think about how I could use the duplicate tag to reduce computation; duplicates + # should normally come in order in a sorted file + + # extract sub-class-specific information + self.parse_extra_fields(tags=tags, record=record) + + self.n_reads += 1 + # self.noise_reads += self.is_noise(record) # todo implement me + + # the tags passed to this function define a molecule, this increments the counter, + # identifying a new molecule only if a new tag combination is observed + self._molecule_histogram[tags] += 1 + + self._molecule_barcode_fraction_bases_above_30.update( + self._quality_above_threshold( + 30, + self._quality_string_to_numeric( + record.get_tag(consts.QUALITY_MOLECULE_BARCODE_TAG_KEY) + ), + ) + ) + + # we should be tolerant and handle it if the pysam.AlignedSegment.get_tag + # cannot retrieve the data by a tag since it's not a fatal error + try: + self.perfect_molecule_barcodes += record.get_tag( + consts.RAW_MOLECULE_BARCODE_TAG_KEY + ) == record.get_tag(consts.MOLECULE_BARCODE_TAG_KEY) + except KeyError: + # An error occurred while retrieving the data from the optional alighment section, which + # indicates that the read did not have a corrected UMI sequence. In the future we would like to + # keep track of these reads. + pass + + self._genomic_reads_fraction_bases_quality_above_30.update( + self._quality_above_threshold(30, record.query_alignment_qualities) + ) + + mean_alignment_quality: float = np.mean(record.query_alignment_qualities) + self._genomic_read_quality.update(mean_alignment_quality) + + # the remaining portions deal with aligned reads, so if the read is not mapped, we are + # done with it + if record.is_unmapped: + continue + + # get components that define a unique sequence fragment and increment the histogram + position: int = record.pos + strand: bool = record.is_reverse + reference: int = record.reference_id + self._fragment_histogram[reference, position, strand, tags] += 1 + + alignment_location = record.get_tag(consts.ALIGNMENT_LOCATION_TAG_KEY) + if alignment_location == consts.CODING_ALIGNMENT_LOCATION_TAG_VALUE: + self.reads_mapped_exonic += 1 + elif alignment_location == consts.INTRONIC_ALIGNMENT_LOCATION_TAG_VALUE: + self.reads_mapped_intronic += 1 + elif alignment_location == consts.UTR_ALIGNMENT_LOCATION_TAG_VALUE: + self.reads_mapped_utr += 1 + + # todo check if read maps outside window (needs gene model) + # todo create distances from terminate side (needs gene model) + + # uniqueness + number_mappings = record.get_tag(consts.NUMBER_OF_HITS_TAG_KEY) + if number_mappings == 1: + self.reads_mapped_uniquely += 1 + else: + self.reads_mapped_multiple += ( + 1 # todo without multi-mapping, this number is zero! + ) + + if record.is_duplicate: + self.duplicate_reads += 1 + + # cigar N field (3) indicates a read is spliced if the value is non-zero + cigar_stats, num_blocks = record.get_cigar_stats() + if cigar_stats[3]: + self.spliced_reads += 1 + + # todo figure out antisense and make this notation clearer; info likely in dropseqtools + self._plus_strand_reads += not record.is_reverse + + def parse_extra_fields( + self, tags: Sequence[str], record: pysam.AlignedSegment + ) -> None: + """Defined by subclasses to extract class-specific information from molecules""" + raise NotImplementedError + + def finalize(self) -> None: + """Calculate metrics that require information from all molecules of an entity + + ``finalize()`` replaces attributes in-place that were initialized by the constructor as + ``None`` with a value calculated across all molecule data that has been aggregated. + + """ + + self.molecule_barcode_fraction_bases_above_30_mean: float = self._molecule_barcode_fraction_bases_above_30.mean + + self.molecule_barcode_fraction_bases_above_30_variance: float = self._molecule_barcode_fraction_bases_above_30.calculate_variance() + + self.genomic_reads_fraction_bases_quality_above_30_mean: float = self._genomic_reads_fraction_bases_quality_above_30.mean + + self.genomic_reads_fraction_bases_quality_above_30_variance: float = self._genomic_reads_fraction_bases_quality_above_30.calculate_variance() + + self.genomic_read_quality_mean: float = self._genomic_read_quality.mean + + self.genomic_read_quality_variance: float = self._genomic_read_quality.calculate_variance() + + self.n_molecules: int = len(self._molecule_histogram.keys()) + + self.n_fragments: int = len(self._fragment_histogram.keys()) + + try: + self.reads_per_molecule: float = self.n_reads / self.n_molecules + except ZeroDivisionError: + self.reads_per_molecule: float = float("nan") + + try: + self.reads_per_fragment: float = self.n_reads / self.n_fragments + except ZeroDivisionError: + self.reads_per_fragment: float = float("nan") + + try: + self.fragments_per_molecule: float = self.n_fragments / self.n_molecules + except ZeroDivisionError: + self.fragments_per_molecule: float = float("nan") + + self.fragments_with_single_read_evidence: int = sum( + 1 for v in self._fragment_histogram.values() if v == 1 + ) + + self.molecules_with_single_read_evidence: int = sum( + 1 for v in self._molecule_histogram.values() if v == 1 + ) + + +class CellMetrics(MetricAggregator): + """Cell Metric Aggregator + + Aggregator that captures metric information about a cell by parsing all of the molecules in + an experiment that were annotated with a specific cell barcode, as recorded in the ``CB`` tag. + + Attributes + ---------- + perfect_cell_barcodes : int + The number of reads whose cell barcodes contain no errors (tag ``CB`` == ``CR``) + reads_mapped_intergenic : int + The number of reads mapped to an intergenic region for this cell + reads_mapped_too_many_loci : int + The number of reads that were mapped to too many loci across the genome and as a + consequence, are reported unmapped by the aligner + cell_barcode_fraction_bases_above_30_variance : float + The variance of the fraction of Illumina base calls for the cell barcode sequence that + are greater than 30, across molecules + cell_barcode_fraction_bases_above_30_mean : float + The average fraction of Illumina base calls for the cell barcode sequence that + are greater than 30, across molecules + n_genes : int + The number of genes detected by this cell + genes_detected_multiple_observations : int + The number of genes that are observed by more than one read in this cell + n_mitochondrial_genes: int + The number of mitochondrial genes detected by this cell + n_mitochondrial_molecules: int + The number of molecules from mitochondrial genes detected for this cell + pct_mitochondrial_molecules: int + The percentage of molecules from mitochondrial genes detected for this cell + + """ + + extra_docs = """ + Examples + -------- + # todo implement me + + See Also + -------- + GeneMetrics + + """ + + __doc__ += MetricAggregator.__doc__ + extra_docs + + def __init__(self): + super().__init__() + + # barcode quality data + self._cell_barcode_fraction_bases_above_30 = OnlineGaussianSufficientStatistic() + self.perfect_cell_barcodes = 0 # inv: fraction cells with errors + + # track non-transcriptomic reads + self.reads_mapped_intergenic = 0 + self.reads_unmapped = 0 + self.reads_mapped_too_many_loci = 0 + + self._genes_histogram = Counter() + + # todo think about whether we can build molecule models that map to things that aren't genes + # i.e. to integentic regions or intronic regions. This could be a part of multi-mapping + # self.molecules_mapped_intergenic = 0 + + self.cell_barcode_fraction_bases_above_30_variance: float = None + self.cell_barcode_fraction_bases_above_30_mean: float = None + self.n_genes: int = None + self.genes_detected_multiple_observations: int = None + self.n_mitochondrial_genes: int = None + self.n_mitochondrial_molecules: int = None + self.pct_mitochondrial_molecules: float = None + + def finalize(self, mitochondrial_genes=set()): + super().finalize() + + self.cell_barcode_fraction_bases_above_30_mean: float = self._cell_barcode_fraction_bases_above_30.mean + + self.cell_barcode_fraction_bases_above_30_variance: float = self._cell_barcode_fraction_bases_above_30.calculate_variance() + + self.n_genes: int = len(self._genes_histogram.keys()) + + self.genes_detected_multiple_observations: int = sum( + 1 for v in self._genes_histogram.values() if v > 1 + ) + + self.n_mitochondrial_genes: int = sum( + 1 for g in self._genes_histogram.keys() if g in mitochondrial_genes + ) + + self.n_mitochondrial_molecules: int = sum( + c for g, c in self._genes_histogram.items() if g in mitochondrial_genes + ) + + if self.n_mitochondrial_molecules: + tot_molecules = sum(self._genes_histogram.values()) + self.pct_mitochondrial_molecules = ( + self.n_mitochondrial_molecules / tot_molecules * 100.0 + ) + else: + self.pct_mitochondrial_molecules = 0.00 + + def parse_extra_fields( + self, tags: Sequence[str], record: pysam.AlignedSegment + ) -> None: + """Parses a record to extract gene-specific information + + Gene-specific metric data is stored in-place in the MetricAggregator + + Parameters + ---------- + tags : Sequence[str] + The GE, UB and CB tags that define this molecule + record : pysam.AlignedSegment + SAM record to be parsed + + """ + self._cell_barcode_fraction_bases_above_30.update( + self._quality_above_threshold( + 30, + self._quality_string_to_numeric( + record.get_tag(consts.QUALITY_CELL_BARCODE_TAG_KEY) + ), + ) + ) + + # Exclude reads that do not have a CB tag from the perfect_cell_barcodes count + if record.has_tag(consts.CELL_BARCODE_TAG_KEY): + raw_cell_barcode_tag = record.get_tag(consts.RAW_CELL_BARCODE_TAG_KEY) + cell_barcode_tag = record.get_tag(consts.CELL_BARCODE_TAG_KEY) + self.perfect_cell_barcodes += raw_cell_barcode_tag == cell_barcode_tag + + try: + alignment_location = record.get_tag(consts.ALIGNMENT_LOCATION_TAG_KEY) + if alignment_location == consts.INTERGENIC_ALIGNMENT_LOCATION_TAG_VALUE: + self.reads_mapped_intergenic += 1 + except KeyError: + self.reads_unmapped += 1 + + # todo track reads_mapped_too_many_loci after multi-alignment is done + self._genes_histogram[tags[2]] += 1 # note that no gene == None + + +class GeneMetrics(MetricAggregator): + """Gene Metric Aggregator + + Aggregator that captures metric information about a gene by parsing all of the molecules in + an experiment that were annotated with a specific gene ID, as recorded in the ``GE`` tag. + + Attributes + ---------- + number_cells_detected_multiple : int + The number of cells which observe more than one read of this gene + number_cells_expressing : int + The number of cells that detect this gene + + """ + + extra_docs = """ + Examples + -------- + # todo implement me + + See Also + -------- + CellMetrics + + """ + + __doc__ += MetricAggregator.__doc__ + extra_docs + + def __init__(self): + super().__init__() + + self._cells_histogram = Counter() + # todo we don't tag exon right now. Not sure if we want to or not + # self._exon_histogram = Counter() + + self.number_cells_detected_multiple: int = None + self.number_cells_expressing: int = None + + def finalize(self): + super().finalize() + + self.number_cells_expressing: int = len(self._cells_histogram.keys()) + + self.number_cells_detected_multiple: int = sum( + 1 for c in self._cells_histogram.values() if c > 1 + ) + + def parse_extra_fields( + self, tags: Sequence[str], record: pysam.AlignedSegment + ) -> None: + """Parses a record to extract cell-specific information + + Cell-specific metric data is stored in-place in the MetricAggregator + + Parameters + ---------- + tags : Sequence[str] + The CB, UB and GE tags that define this molecule + record : pysam.AlignedSegment + SAM record to be parsed + + """ + self._cells_histogram[tags[1]] += 1 diff --git a/tools/scripts/sctools/build/lib/sctools/metrics/gatherer.py b/tools/scripts/sctools/build/lib/sctools/metrics/gatherer.py new file mode 100644 index 00000000..91f7287f --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/metrics/gatherer.py @@ -0,0 +1,232 @@ +""" +Sequence Metric Gatherers +========================= + +..currentmodule:: sctools.metrics + +This module defines classes to gather metrics across the cells or genes of an experiment and write +them to gzip-compressed csv files + +Classes +------- + +.. autosummary:: + :toctree: generated/ + + MetricGatherer Gatherer Base Class + GatherCellMetrics Class to gather metrics on all cells in an experiment + GatherGeneMetrics Class to gather metrics on all genes in an experiment + +See Also +-------- +sctools.metrics.aggregator +sctools.metrics.merge +sctools.metrics.writer + +""" + +from contextlib import closing + +import pysam +from typing import Set + +from sctools.bam import iter_cell_barcodes, iter_genes, iter_molecule_barcodes +from sctools.metrics.aggregator import CellMetrics, GeneMetrics +from sctools.metrics.writer import MetricCSVWriter + + +class MetricGatherer: + """Gathers Metrics from an experiment + + Because molecules tend to have relatively small numbers of reads, the memory footprint of + this method is typically small (tens of megabytes). + + Parameters + ---------- + bam_file : str + the bam file containing the reads that metrics should be calculated from. Can be a chunk + of cells or an entire experiment + output_stem : str + the file stem for the gzipped csv output + + Methods + ------- + extract_metrics + extracts metrics from ``bam_file`` and writes them to output_stem.csv.gz + + """ + + def __init__( + self, + bam_file: str, + output_stem: str, + mitochondrial_gene_ids: Set[str] = set(), + compress: bool = True, + ): + self._bam_file = bam_file + self._output_stem = output_stem + self._compress = compress + self._mitochondrial_gene_ids = mitochondrial_gene_ids + + @property + def bam_file(self) -> str: + """the bam file that metrics are generated from""" + return self._bam_file + + def extract_metrics(self, mode="rb") -> None: + """extract metrics from the provided bam file and write the results to csv. + + Parameters + ---------- + mode : {'r', 'rb'}, default 'rb' + the open mode for pysam.AlignmentFile. 'r' indicates the input is a sam file, and 'rb' + indicates a bam file. + + """ + raise NotImplementedError + + +class GatherCellMetrics(MetricGatherer): + + extra_docs = """ + Notes + ----- + ``bam_file`` must be sorted by gene (``GE``), molecule (``UB``), and cell (``CB``), where gene + varies fastest. + + Examples + -------- + >>> from sctools.metrics.gatherer import GatherCellMetrics + >>> import os, tempfile + + >>> # example data + >>> bam_file = os.path.abspath(__file__) + '../test/data/test.bam' + >>> temp_dir = tempfile.mkdtemp() + >>> g = GatherCellMetrics(bam_file=bam_file, output_stem=temp_dir + 'test', compress=True) + >>> g.extract_metrics() + + See Also + -------- + GatherGeneMetrics + + """ + + __doc__ += extra_docs + + def extract_metrics(self, mode: str = "rb") -> None: + """Extract cell metrics from self.bam_file + + Parameters + ---------- + mode : str, optional + Open mode for self.bam. 'r' -> sam, 'rb' -> bam (default = 'rb'). + + """ + # open the files + with pysam.AlignmentFile(self.bam_file, mode=mode) as bam_iterator, closing( + MetricCSVWriter(self._output_stem, self._compress) + ) as cell_metrics_output: + + # write the header + cell_metrics_output.write_header(vars(CellMetrics())) + + # break up the bam file into sub-iterators over cell barcodes + for cell_iterator, cell_tag in iter_cell_barcodes( + bam_iterator=bam_iterator + ): + metric_aggregator = CellMetrics() + + # break up cell barcodes by molecule barcodes + for molecule_iterator, molecule_tag in iter_molecule_barcodes( + bam_iterator=cell_iterator + ): + + # break up molecule barcodes by gene ids + for gene_iterator, gene_tag in iter_genes( + bam_iterator=molecule_iterator + ): + + # process the data + metric_aggregator.parse_molecule( + tags=(cell_tag, molecule_tag, gene_tag), + records=gene_iterator, + ) + + # write a record for each cell + metric_aggregator.finalize( + mitochondrial_genes=self._mitochondrial_gene_ids + ) + cell_metrics_output.write(cell_tag, vars(metric_aggregator)) + + +class GatherGeneMetrics(MetricGatherer): + + extra_docs = """ + Notes + ----- + ``bam_file`` must be sorted by molecule (``UB``), cell (``CB``), and gene (``GE``), where + molecule varies fastest. + + Examples + -------- + >>> from sctools.metrics.gatherer import GatherCellMetrics + >>> import os, tempfile + + >>> # example data + >>> bam_file = os.path.abspath(__file__) + '../test/data/test.bam' + >>> temp_dir = tempfile.mkdtemp() + >>> g = GatherCellMetrics(bam_file=bam_file, output_stem=temp_dir + 'test', compress=True) + >>> g.extract_metrics() + + See Also + -------- + GatherGeneMetrics + + """ + + __doc__ += extra_docs + + def extract_metrics(self, mode: str = "rb") -> None: + """Extract gene metrics from self.bam_file + + Parameters + ---------- + mode : str, optional + Open mode for self.bam. 'r' -> sam, 'rb' -> bam (default = 'rb'). + + """ + # open the files + with pysam.AlignmentFile(self.bam_file, mode=mode) as bam_iterator, closing( + MetricCSVWriter(self._output_stem, self._compress) + ) as gene_metrics_output: + + # write the header + gene_metrics_output.write_header(vars(GeneMetrics())) + + # break up the bam file into sub-iterators over gene ids + for gene_iterator, gene_tag in iter_genes(bam_iterator=bam_iterator): + metric_aggregator = GeneMetrics() + + # in case of multi-genes ignore as in the counting stage + if gene_tag and len(gene_tag.split(",")) > 1: + continue + + # break up gene ids by cell barcodes + for cell_iterator, cell_tag in iter_cell_barcodes( + bam_iterator=gene_iterator + ): + + # break up cell barcodes by molecular barcodes + for molecule_iterator, molecule_tag in iter_molecule_barcodes( + bam_iterator=cell_iterator + ): + + # process the data + metric_aggregator.parse_molecule( + tags=(gene_tag, cell_tag, molecule_tag), + records=molecule_iterator, + ) + + # write a record for each gene id + metric_aggregator.finalize() + gene_metrics_output.write(gene_tag, vars(metric_aggregator)) diff --git a/tools/scripts/sctools/build/lib/sctools/metrics/merge.py b/tools/scripts/sctools/build/lib/sctools/metrics/merge.py new file mode 100644 index 00000000..aa4d4831 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/metrics/merge.py @@ -0,0 +1,191 @@ +""" +Merge Sequence Metrics +====================== + +..currentmodule:: sctools.metrics + +This module defines classes to merge multiple metrics files that have been gathered from bam files +containing disjoint sets of cells. This is a common use pattern, as sequencing datasets are often +chunked to enable horizontal scaling using scatter-gather patterns. + +Classes +------- +MergeMetrics Merge Metrics base class +MergeCellMetrics Class to merge cell metrics +MergeGeneMetrics Class to merge gene metrics + +See Also +-------- +sctools.metrics.gatherer +sctools.metrics.aggregator +sctools.metrics.writer + +""" + +from typing import List, Sequence + +import pandas as pd +import numpy as np + + +class MergeMetrics: + """Merges multiple metrics files into a single gzip compressed csv file + + Parameters + ---------- + metric_files : Sequence[str] + metrics files to merge + output_file : str + file name for the merged output + + Methods + ------- + execute + merge metrics files + # todo this should probably be wrapped into __init__ to make this more like a function + + """ + + def __init__(self, metric_files: Sequence[str], output_file: str): + self._metric_files = metric_files + if not output_file.endswith(".csv.gz"): + output_file += ".csv.gz" + self._output_file = output_file + + def execute(self) -> None: + raise NotImplementedError # merge the metrics + + +class MergeCellMetrics(MergeMetrics): + def execute(self) -> None: + """Concatenate input cell metric files + + Since bam files that metrics are calculated from contain disjoint sets of cells, cell + metrics can simply be concatenated together. + + """ + metric_dataframes: List[pd.DataFrame] = [ + pd.read_csv(f, index_col=0) for f in self._metric_files + ] + concatenated_frame: pd.DataFrame = pd.concat(metric_dataframes, axis=0) + concatenated_frame.to_csv(self._output_file, compression="gzip") + + +class MergeGeneMetrics(MergeMetrics): + def execute(self) -> None: + """Merge input gene metric files + + The bam files that metrics are calculated from contain disjoint sets of cells, each + of which can measure the same genes. + As a result, the metric values must be summed (count based metrics) averaged over + (fractional, averge, or variance metrics) or recalculated (metrics that depend on other + metrics). + + """ + + count_data_to_sum = [ + "n_reads", + "noise_reads", + "perfect_molecule_barcodes", + "reads_mapped_exonic", + "reads_mapped_intronic", + "reads_mapped_utr", + "reads_mapped_uniquely", + "reads_mapped_multiple", + "duplicate_reads", + "spliced_reads", + "antisense_reads", + "n_molecules", + "n_fragments", + "fragments_with_single_read_evidence", + "molecules_with_single_read_evidence", + "number_cells_detected_multiple", + "number_cells_expressing", + ] + + sum_operations = {c: "sum" for c in count_data_to_sum} + + def weighted_average(data_frame: pd.DataFrame) -> pd.Series: + """Calculate the average of each metric, weighted by number of reads per chunk + + Parameters + ---------- + data_frame : pd.DataFrame + chunks x metrics data frame + + Returns + ------- + weighted_average_metrics : pd.Series + The average of each metric across chunks, weighted by the number of reads per chunk + + """ + weights = data_frame["n_reads"].values + + columns_to_average_by_read = [ + "molecule_barcode_fraction_bases_above_30_mean", + "molecule_barcode_fraction_bases_above_30_variance", + "genomic_reads_fraction_bases_quality_above_30_mean", + "genomic_reads_fraction_bases_quality_above_30_variance", + "genomic_read_quality_mean", + "genomic_read_quality_variance", + ] + + return pd.Series( + { + c: np.average(data_frame[c], weights=weights) + for c in columns_to_average_by_read + } + ) + + def recalculate_operation(data_frame) -> pd.DataFrame: + """Recalculate metrics that are dependent on other metric values + + Other metrics should be merged before this function is executed + + Parameters + ---------- + data_frame : pd.DataFrame + chunks x metrics data frame + + Returns + ------- + recalculated_metrics : pd.DataFrame + data frame containing recalculated metrics + + """ + return pd.DataFrame( + data={ + "reads_per_molecule": data_frame["n_reads"] + / data_frame["n_molecules"], + "fragments_per_molecule": data_frame["n_fragments"] + / data_frame["n_molecules"], + "reads_per_fragment": data_frame["n_reads"] + / data_frame["n_fragments"], + } + ) + + # pick one file as a nucleus and merge each subsequent dataframe into it + nucleus = pd.read_csv(self._metric_files[0], index_col=0) + for filename in self._metric_files[1:]: + leaf = pd.read_csv(filename, index_col=0) + + # concatenate this leaf with the nucleus metrics file + concatenated = pd.concat([nucleus, leaf], axis=0) + + # group all duplicate gene names together + grouped = concatenated.groupby(level=0, axis=0) + + # execute the merging operations + summed_columns = grouped.agg(sum_operations) + averaged_columns = grouped.apply(weighted_average) + + # stitch the columns back together, add the metrics that need to be recalculated + merged = pd.concat([summed_columns, averaged_columns], axis=1) + recalculated_columns = recalculate_operation(merged) + merged = pd.concat([merged, recalculated_columns], axis=1) + + # set as nucleus and continue + nucleus = merged + + # write the data + nucleus.to_csv(self._output_file, compression="gzip") diff --git a/tools/scripts/sctools/build/lib/sctools/metrics/writer.py b/tools/scripts/sctools/build/lib/sctools/metrics/writer.py new file mode 100644 index 00000000..2379418c --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/metrics/writer.py @@ -0,0 +1,107 @@ +""" +Metric Writers +============== + +..currentmodule:: sctools.metrics + +This module defines a class to write metrics to csv as the data is generated, cell by cell or gene +by gene. This strategy keeps memory usage low, as no more than a single molecule's worth of sam +records and one cell or gene's worth of metric data are in-memory at a time. + +Classes +------- +MetricCSVWriter Class to write metrics to file + +See Also +-------- +sctools.metrics.gatherer +sctools.metrics.aggregator +sctools.metrics.merge + +""" +from typing import TextIO, List, Mapping, Any +from numbers import Number +import gzip + + +class MetricCSVWriter: + """Writes metric information iteratively to (optionally compressed) csv. + + Parameters + ---------- + output_stem : str + File stem for the output file. + compress : bool, optional + Whether or not to compress the output file (default = True). + + Methods + ------- + write_header + Write the metric header to file. + write + Write an array of cell or gene metrics to file. + close + Close the metric file. + + """ + + def __init__(self, output_stem: str, compress=True): + + # check and fix extension: + if compress: + if not output_stem.endswith(".csv.gz"): + output_stem += ".csv.gz" + else: + if not output_stem.endswith(".csv"): + output_stem += ".csv" + self._filename: str = output_stem + + # open the file + if compress: + self._open_fid: TextIO = gzip.open(self._filename, "wt") + else: + self._open_fid: TextIO = open(self._filename, "w") + self._header: List[str] = None + + @property + def filename(self) -> str: + """filename with correct suffix added""" + return self._filename + + def write_header(self, record: Mapping[str, Any]) -> None: + """Write the metric keys to file, producing the header line of the csv file. + + Parameters + ---------- + record : Mapping[str, Any] + Output of ``vars()`` called on an sctools.metrics.aggregator.MetricAggregator instance, + producing a dictionary of keys to metric values. + + """ + self._header = list(key for key in record.keys() if not key.startswith("_")) + self._open_fid.write("," + ",".join(self._header) + "\n") + + def write(self, index: str, record: Mapping[str, Number]) -> None: + """Write the array of metric values for a cell or gene to file. + + Parameters + ---------- + index : str + The name of the cell or gene that these metrics summarize + record : Mapping[str, Number] + Output of ``vars()`` called on an sctools.metrics.aggregator.MetricAggregator instance, + producing a dictionary of keys to metric values. + + """ + ordered_fields = [str(record[k]) for k in self._header] + + # genes and cells can be None, call repr to convert to string when this induces a TypeError + try: + self._open_fid.write(index + "," + ",".join(ordered_fields) + "\n") + except TypeError: + index = repr(index) + self._open_fid.write(index + "," + ",".join(ordered_fields) + "\n") + + def close(self) -> None: + """Close the metrics file.""" + self._open_fid.close() diff --git a/tools/scripts/sctools/build/lib/sctools/platform.py b/tools/scripts/sctools/build/lib/sctools/platform.py new file mode 100644 index 00000000..460f26ac --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/platform.py @@ -0,0 +1,1126 @@ +""" +Command Line Interface for SC Tools: +==================================== + +.. currentmodule:: sctools + +This module defines the command line interface for SC Tools. Tools are separated into those that +are specific to particular chemistries (e.g. Smart-seq 2) or experimental platforms (e.g. 10x +Genomics v2) and those that are general across any sequencing experiment. + +Currently, only general modules and those used for 10x v2 are implemented + +Classes +------- +GenericPlatform Class containing all general command line utilities +TenXV2 Class containing 10x v2 specific command line utilities + +""" + +import argparse +from typing import Iterable, List, Dict, Set, Optional, Sequence +from itertools import chain + +import pysam +from sctools import fastq, bam, metrics, count, consts, gtf, groups + + +class GenericPlatform: + """Platform-agnostic command line functions available in SC Tools. + + Platform-Agnostic Methods + ------------------------- + tag_sort_bam(): + sort a bam file by zero or more tags and then by queryname + verify_bam_sort(): + verifies whether bam file is correctly sorted by given list of zero or more tags, then queryname + split_bam() + split a bam file into subfiles of equal size + calculate_gene_metrics() + calculate information about genes captured by a sequencing experiment + calculate_cell_metrics() + calculate information about cells captured by a sequencing experiment + merge_gene_metrics() + merge multiple gene metrics files into a single output + merge_cell_metrics() + merge multiple cell metrics files into a single output + bam_to_count() + construct a compressed sparse row count file from a tagged, aligned bam file + merge_count_matrices() + merge multiple csr-format count matrices into a single csr matrix + group_qc_outputs() + aggregate Picard, HISAT2 and RSME QC statisitics + """ + + @classmethod + def tag_sort_bam(cls, args: Iterable = None) -> int: + """Command line entrypoint for sorting a bam file by zero or more tags, followed by queryname. + + Parameters + ---------- + args : Iterable[str], optional + arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + description = "Sorts bam by list of zero or more tags, followed by query name" + parser = argparse.ArgumentParser(description=description) + parser.add_argument("-i", "--input_bam", required=True, help="input bamfile") + parser.add_argument("-o", "--output_bam", required=True, help="output bamfile") + parser.add_argument( + "-t", + "--tags", + nargs="+", + action="append", + help="tag(s) to sort by, separated by space, e.g. -t CB GE UB", + ) + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + tags = cls.get_tags(args.tags) + with pysam.AlignmentFile(args.input_bam, "rb") as f: + header = f.header + records = f.fetch(until_eof=True) + sorted_records = bam.sort_by_tags_and_queryname(records, tags) + with pysam.AlignmentFile(args.output_bam, "wb", header=header) as f: + for record in sorted_records: + f.write(record) + + return 0 + + @classmethod + def verify_bam_sort(cls, args: Iterable = None) -> int: + """Command line entrypoint for verifying bam is properly sorted by zero or more tags, followed by queryname. + + Parameters + ---------- + args : Iterable[str], optional + arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + description = "Verifies whether bam is sorted by the list of zero or more tags, followed by query name" + parser = argparse.ArgumentParser(description=description) + parser.add_argument("-i", "--input_bam", required=True, help="input bamfile") + parser.add_argument( + "-t", + "--tags", + nargs="+", + action="append", + help="tag(s) to use to verify sorting, separated by space, e.g. -t CB GE UB", + ) + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + tags = cls.get_tags(args.tags) + with pysam.AlignmentFile(args.input_bam, "rb") as f: + aligned_segments = f.fetch(until_eof=True) + sortable_records = ( + bam.TagSortableRecord.from_aligned_segment(r, tags) + for r in aligned_segments + ) + bam.verify_sort(sortable_records, tags) + + print( + "{0} is correctly sorted by {1} and query name".format(args.input_bam, tags) + ) + return 0 + + @classmethod + def get_tags(cls, raw_tags: Optional[Sequence[str]]) -> Iterable[str]: + if raw_tags is None: + raw_tags = [] + # Flattens into single list when tags specified like -t A -t B -t C + return [t for t in chain.from_iterable(raw_tags)] + + @classmethod + def split_bam(cls, args: Iterable = None) -> int: + """Command line entrypoint for splitting a bamfile into subfiles of equal size. + + prints filenames of chunks to stdout + + Parameters + ---------- + args : Iterable[str], optional + arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "-b", "--bamfile", nargs="+", required=True, help="input bamfile" + ) + parser.add_argument( + "-p", "--output-prefix", required=True, help="prefix for output chunks" + ) + parser.add_argument( + "-s", + "--subfile-size", + required=False, + default=1000, + type=float, + help="approximate size target for each subfile (in MB)", + ) + parser.add_argument( + "--num-processes", + required=False, + default=None, + type=int, + help="Number of processes to parallelize over", + ) + parser.add_argument( + "-t", + "--tags", + nargs="+", + help="tag(s) to split bamfile over. Tags are checked sequentially, " + "and tags after the first are only checked if the first tag is " + "not present.", + ) + parser.set_defaults(raise_missing=True) + parser.add_argument( + "--drop-missing", + action="store_false", + help="drop records without tag specified by -t/--tag (default " + "behavior is to raise an exception", + ) + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + filenames = bam.split( + args.bamfile, + args.output_prefix, + args.tags, + approx_mb_per_split=args.subfile_size, + raise_missing=args.drop_missing, + num_processes=args.num_processes, + ) + + print(" ".join(filenames)) + return 0 + + @classmethod + def calculate_gene_metrics(cls, args: Iterable[str] = None) -> int: + """Command line entrypoint for calculating gene metrics from a sorted bamfile. + + Writes metrics to .csv + + Parameters + ---------- + args : Iterable[str], optional + arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "-i", "--input-bam", required=True, help="Input bam file name." + ) + parser.add_argument( + "-o", "--output-filestem", required=True, help="Output file stem." + ) + + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + gene_metric_gatherer = metrics.gatherer.GatherGeneMetrics( + args.input_bam, args.output_filestem + ) + gene_metric_gatherer.extract_metrics() + return 0 + + @classmethod + def calculate_cell_metrics(cls, args: Iterable[str] = None) -> int: + """Command line entrypoint for calculating cell metrics from a sorted bamfile. + + Writes metrics to .csv + + Parameters + ---------- + args : Iterable[str], optional + Arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "-i", "--input-bam", required=True, help="Input bam file name." + ) + parser.add_argument( + "-o", "--output-filestem", required=True, help="Output file stem." + ) + parser.add_argument( + "-a", + "--gtf-annotation-file", + required=False, + default=None, + help="gtf annotation file that bam_file was aligned against", + ) + + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + # load mitochondrial gene ids from the annotation file + mitochondrial_gene_ids: Set(str) = set() + if args.gtf_annotation_file: + mitochondrial_gene_ids = gtf.get_mitochondrial_gene_names( + args.gtf_annotation_file + ) + + cell_metric_gatherer = metrics.gatherer.GatherCellMetrics( + args.input_bam, args.output_filestem, mitochondrial_gene_ids + ) + cell_metric_gatherer.extract_metrics() + return 0 + + @classmethod + def merge_gene_metrics(cls, args: Iterable[str] = None) -> int: + """Command line entrypoint for merging multiple gene metrics files. + + Merges multiple metrics inputs into a single metrics file that matches the shape and + order of the generated count matrix. + + Parameters + ---------- + args : Iterable[str], optional + Arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument("metric_files", nargs="+", help="Input metric files") + parser.add_argument( + "-o", "--output-filestem", required=True, help="Output file stem." + ) + + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + merge = metrics.merge.MergeGeneMetrics(args.metric_files, args.output_filestem) + merge.execute() + return 0 + + @classmethod + def merge_cell_metrics(cls, args: Iterable[str] = None) -> int: + """Command line entrypoint for merging multiple cell metrics files. + + Merges multiple metrics inputs into a single metrics file that matches the shape and + order of the generated count matrix. + + Parameters + ---------- + args : Iterable[str], optional + Arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument("metric_files", nargs="+", help="Input metric files") + parser.add_argument( + "-o", "--output-filestem", required=True, help="Output file stem." + ) + + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + merge = metrics.merge.MergeCellMetrics(args.metric_files, args.output_filestem) + merge.execute() + return 0 + + @classmethod + def bam_to_count_matrix(cls, args: Iterable[str] = None) -> int: + """Command line entrypoint for constructing a count matrix from a tagged bam file. + + Constructs a count matrix from an aligned bam file sorted by cell barcode, molecule + barcode, and gene id. + + Parameters + ---------- + args : Iterable[str], optional + Arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.set_defaults( + cell_barcode_tag=consts.CELL_BARCODE_TAG_KEY, + molecule_barcode_tag=consts.MOLECULE_BARCODE_TAG_KEY, + gene_name_tag=consts.GENE_NAME_TAG_KEY, + sn_rna_seq_mode=False, + ) + parser.add_argument("-b", "--bam-file", help="input_bam_file", required=True) + parser.add_argument( + "-o", "--output-prefix", help="file stem for count matrix", required=True + ) + parser.add_argument( + "-a", + "--gtf-annotation-file", + required=True, + help="gtf annotation file that bam_file was aligned against", + ) + parser.add_argument( + "-c", + "--cell-barcode-tag", + help=f"tag that identifies the cell barcode (default = {consts.CELL_BARCODE_TAG_KEY})", + ) + parser.add_argument( + "-m", + "--molecule-barcode-tag", + help=f"tag that identifies the molecule barcode (default = {consts.MOLECULE_BARCODE_TAG_KEY})", + ) + parser.add_argument( + "-g", + "--gene-id-tag", + help=f"tag that identifies the gene name (default = {consts.GENE_NAME_TAG_KEY})", + ) + + parser.add_argument( + "-n", + "--sn-rna-seq-mode", + action="store_true", + help=f"snRNA Seq mode (default = False)", + ) + + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + # assume bam file unless the file explicitly has a sam suffix + open_mode = "r" if args.bam_file.endswith(".sam") else "rb" + + # load gene names from the annotation file + gene_name_to_index: Dict[str, int] = gtf.extract_gene_names( + args.gtf_annotation_file + ) + + # For snRNA-seq we need the extended gene information + if args.sn_rna_seq_mode: + gene_locations = gtf.extract_extended_gene_names(args.gtf_annotation_file) + else: + gene_locations = None + + matrix = count.CountMatrix.from_sorted_tagged_bam( + bam_file=args.bam_file, + gene_name_to_index=gene_name_to_index, + chromosomes_gene_locations_extended=gene_locations, + cell_barcode_tag=args.cell_barcode_tag, + molecule_barcode_tag=args.molecule_barcode_tag, + gene_name_tag=args.gene_id_tag, + open_mode=open_mode, + ) + matrix.save(args.output_prefix) + + return 0 + + @classmethod + def merge_count_matrices(cls, args: Iterable[str] = None) -> int: + """Command line entrypoint for constructing a count matrix from a tagged bam file. + + Constructs a count matrix from an aligned bam file sorted by cell barcode, molecule + barcode, and gene id. + + Parameters + ---------- + args : Iterable[str], optional + Arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "-i", + "--input-prefixes", + nargs="+", + help="prefix for count matrices to be concatenated. e.g. test_counts " + "for test_counts.npz, test_counts_col_index.npy, and test_counts_" + "row_index.npy", + ) + parser.add_argument( + "-o", "--output-stem", help="file stem for merged csr matrix", required=True + ) + + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + count_matrix = count.CountMatrix.merge_matrices(args.input_prefixes) + count_matrix.save(args.output_stem) + + return 0 + + @classmethod + def group_qc_outputs(cls, args: Iterable[str] = None) -> int: + """Commandline entrypoint for parsing picard metrics files, hisat2 and rsem statistics log files. + Parameters + ---------- + args: + file_names: array of files + output_name: prefix of output file name. + metrics_type: Picard, PicardTable, HISAT2, RSEM and Core. + Returns + ---------- + return: 0 + return if the program completes successfully. + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "-f", + "--file_names", + dest="file_names", + nargs="+", + required=True, + help="a list of files to be parsed out.", + ) + parser.add_argument( + "-o", + "--output_name", + dest="output_name", + required=True, + help="The output file name", + ) + parser.add_argument( + "-t", + "--metrics_type", + dest="metrics_type", + choices=["Picard", "PicardTable", "Core", "HISAT2", "RSEM"], + required=True, + help="a list of string to represent metrics types,such Picard, PicardTable, HISAT2,RSEM, Core", + ) + + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + + if args.metrics_type == "Picard": + groups.write_aggregated_picard_metrics_by_row( + args.file_names, args.output_name + ) + elif args.metrics_type == "PicardTable": + groups.write_aggregated_picard_metrics_by_table( + args.file_names, args.output_name + ) + elif args.metrics_type == "Core": + groups.write_aggregated_qc_metrics(args.file_names, args.output_name) + elif args.metrics_type == "HISAT2": + groups.parse_hisat2_log(args.file_names, args.output_name) + elif args.metrics_type == "RSEM": + groups.parse_rsem_cnt(args.file_names, args.output_name) + return 0 + + +class TenXV2(GenericPlatform): + """Command Line Interface for 10x Genomics v2 RNA-sequencing programs + + This class defines several methods that are created as CLI tools when sctools is installed + (see setup.py) + + Attributes + ---------- + cell_barcode : fastq.EmbeddedBarcode + A data class that defines the start and end position of the cell barcode and the tags to + assign the sequence and quality of the cell barcode + molecule_barcode : fastq.EmbeddedBarcode + A data class that defines the start and end position of the molecule barcode and the tags + to assign the sequence and quality of the molecule barcode + sample_barcode : fastq.EmbeddedBarcode + A data class that defines the start and end position of the sample barcode and the tags + to assign the sequence and quality of the sample barcode + + Methods + ------- + attach_barcodes() + Attach barcodes from the forward (r1) and optionally index (i1) fastq files to the reverse + (r2) bam file + + """ + + # 10x contains three barcodes embedded within sequencing reads. The below objects define the + # start and end points of those barcodes relative to the start of the sequence, and the + # GA4GH standard tags that the extracted barcodes should be labeled with in the BAM file. + cell_barcode = fastq.EmbeddedBarcode( + start=0, + end=16, + quality_tag=consts.QUALITY_CELL_BARCODE_TAG_KEY, + sequence_tag=consts.RAW_CELL_BARCODE_TAG_KEY, + ) + molecule_barcode = fastq.EmbeddedBarcode( + start=16, + end=26, + quality_tag=consts.QUALITY_MOLECULE_BARCODE_TAG_KEY, + sequence_tag=consts.RAW_MOLECULE_BARCODE_TAG_KEY, + ) + sample_barcode = fastq.EmbeddedBarcode( + start=0, + end=8, + quality_tag=consts.QUALITY_SAMPLE_BARCODE_TAG_KEY, + sequence_tag=consts.RAW_SAMPLE_BARCODE_TAG_KEY, + ) + + @classmethod + def _tag_bamfile( + cls, + input_bamfile_name: str, + output_bamfile_name: str, + tag_generators: Iterable[fastq.EmbeddedBarcodeGenerator], + ) -> None: + """Adds tags from fastq file(s) to a bam file. + + Attaches tags extracted from fastq files by `tag_generators`, attaches them to records from + `input_bamfile_name`, and writes the result to `output_bamfile_name` + + Parameters + ---------- + input_bamfile_name : str + input bam + output_bamfile_name : str + output bam + tag_generators : Iterable[fastq.EmbeddedBarcodeGenerator] + Iterable of generators that yield barcodes from fastq files + + """ + bam_tagger = bam.Tagger(input_bamfile_name) + bam_tagger.tag(output_bamfile_name, tag_generators) + + @classmethod + def _make_tag_generators( + cls, r1, i1=None, whitelist=None + ) -> List[fastq.EmbeddedBarcodeGenerator]: + """Create tag generators from fastq files. + + Tag generators are iterators that run over fastq records, they extract and yield all of the + barcodes embedded in each fastq record. For 10x, this means extracting the cell, umi, and + optionally, the sample barcode. + + Parameters + ---------- + r1 : str + forward fastq file + i1 : str, optional + index fastq file + whitelist : str, optional + A file that contains a list of acceptable cell barcodes + + Returns + ------- + tag_generators, List[EmbeddedBarcodeGenerator] + EmbeddedBarcodeGenerators containing barcodes from 10x fastq records + + """ + tag_generators = [] + + # generator for cell and molecule barcodes + if whitelist is not None: + tag_generators.append( + fastq.BarcodeGeneratorWithCorrectedCellBarcodes( + fastq_files=r1, + embedded_cell_barcode=cls.cell_barcode, + whitelist=whitelist, + other_embedded_barcodes=[cls.molecule_barcode], + ) + ) + else: + tag_generators.append( + fastq.EmbeddedBarcodeGenerator( + fastq_files=r1, + embedded_barcodes=[cls.cell_barcode, cls.molecule_barcode], + ) + ) + + # generator for sample barcodes + if i1 is not None: + tag_generators.append( + fastq.EmbeddedBarcodeGenerator( + fastq_files=i1, embedded_barcodes=[cls.sample_barcode] + ) + ) + return tag_generators + + @classmethod + def attach_barcodes(cls, args=None): + """Command line entrypoint for attaching barcodes to a bamfile. + + Parameters + ---------- + args : Iterable[str], optional + arguments list, for testing (see test/test_entrypoints.py for example). The default + value of None, when passed to `parser.parse_args` causes the parser to + read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "--r1", + required=True, + help="read 1 fastq file for a 10x genomics v2 experiment", + ) + parser.add_argument( + "--u2", + required=True, + help="unaligned bam containing cDNA fragments. Can be converted from fastq read 2" + "using picard FastqToSam", + ) + parser.add_argument( + "--i1", + default=None, + help="(optional) i7 index fastq file for a 10x genomics experiment", + ) + parser.add_argument( + "-o", "--output-bamfile", required=True, help="filename for tagged bam" + ) + parser.add_argument( + "-w", + "--whitelist", + default=None, + help="optional cell barcode whitelist. If provided, corrected barcodes " + "will also be output when barcodes are observed within 1ED of a " + "whitelisted barcode", + ) + if args is not None: + args = parser.parse_args(args) + else: + args = parser.parse_args() + tag_generators = cls._make_tag_generators(args.r1, args.i1, args.whitelist) + cls._tag_bamfile(args.u2, args.output_bamfile, tag_generators) + + return 0 + + +class BarcodePlatform(GenericPlatform): + """Command Line Interface for extracting and attaching barcodes with specified positions + generalizing TenXV2 attach barcodes + + Sample, cell and/or molecule barcodes can be extracted and attached to an unmapped bam when the + corresponding barcode's start position and and length are provided. The sample barcode is extracted + from the index i7 fastq file and the cell and molecule barcode are extracted from the r1 fastq file + + This class defines several methods that are created as CLI tools when sctools is installed + (see setup.py) + + Attributes + ---------- + cell_barcode : fastq.EmbeddedBarcode + A data class that defines the start and end position of the cell barcode and the tags to + assign the sequence and quality of the cell barcode + molecule_barcode : fastq.EmbeddedBarcode + A data class that defines the start and end position of the molecule barcode and the tags + to assign the sequence and quality of the molecule barcode + sample_barcode : fastq.EmbeddedBarcode + A data class that defines the start and end position of the sample barcode and the tags + to assign the sequence and quality of the sample barcode + + Methods + ------- + attach_barcodes() + Attach barcodes from the forward (r1) and optionally index (i1) fastq files to the reverse + (r2) bam file + + """ + + cell_barcode = None + molecule_barcode = None + sample_barcode = None + + @classmethod + def _validate_barcode_args(cls, args): + """Validates that the barcode start position is greater than 0 + + Parameters + ---------- + args : object + arguments list, The default value of None, when passed to `parser.parse_args` + causes the parser to read `sys.argv` + + Returns + ------- + args : object + return arguments list if valid + + """ + # check that if a barcode start position is provided, its length is also (and vice versa) + cls._validate_barcode_length_and_position( + args.cell_barcode_start_pos, args.cell_barcode_length + ) + cls._validate_barcode_length_and_position( + args.molecule_barcode_start_pos, args.molecule_barcode_length + ) + cls._validate_barcode_length_and_position( + args.sample_barcode_start_pos, args.sample_barcode_length + ) + + # check that an index fastq is provided sample barcode length and position are given + if args.i1 is None and args.sample_barcode_length: + raise argparse.ArgumentError( + "An i7 index fastq file must be given to attach a sample barcode" + ) + + # check that cell and molecule barcodes don't overlap + if args.cell_barcode_length and args.molecule_barcode_length: + cls._validate_barcode_input( + args.molecule_barcode_start_pos, + args.cell_barcode_start_pos + args.cell_barcode_length, + ) + + return args + + @classmethod + def _validate_barcode_length_and_position( + cls, barcode_start_position, barcode_length + ): + """Checks that either that both barcode length and position are given or that neither are given as arguments + + Parameters + ---------- + barcode_start_position : int + the user defined start position (base pairs) of the barcode + + barcode_length : int + the user defined length (base pairs) of the barcode + + Returns + ------- + given_value : int + return given value if valid + + """ + barcode_start_pos_exists = bool(barcode_start_position) or ( + barcode_start_position == 0 + ) + barcode_length_exists = bool(barcode_length) + # (XOR boolean logic) + if barcode_start_pos_exists != barcode_length_exists: + raise argparse.ArgumentError( + "Invalid position/length, both position and length must be provided by the user together" + ) + + @classmethod + def _validate_barcode_input(cls, given_value, min_value): + """Validates that the barcode input is greater than a min value + + Parameters + ---------- + given_value : int + the given value that must be greater than the min_value, + (barcode length or barcode starting position) + + min_value : int + the min value that the given_value must be greater than + + Returns + ------- + given_value : int + return given value if valid + + """ + if given_value < min_value: + raise argparse.ArgumentTypeError("Invalid barcode length/position") + return given_value + + @classmethod + def _validate_barcode_start_pos(cls, given_value): + """Validates that the barcode start position is greater than 0 + + Parameters + ---------- + given_value : Union[int, str] + the given start position of the barcode to validate + + Returns + ------- + given_value : int + returns the start position if it is valid + + """ + return cls._validate_barcode_input(int(given_value), 0) + + @classmethod + def _validate_barcode_length(cls, given_value): + """Validates that the barcode length is greater than 1 + + Parameters + ---------- + given_value : Union[int, str] + the given length of the barcode to validate + + Returns + ------- + given_value : int + returns the length if it is valid + + """ + return cls._validate_barcode_input(int(given_value), 1) + + @classmethod + def _tag_bamfile( + cls, + input_bamfile_name: str, + output_bamfile_name: str, + tag_generators: Iterable[fastq.EmbeddedBarcodeGenerator], + ) -> None: + """Adds tags from fastq file(s) to a bam file. + + Attaches tags extracted from fastq files by `tag_generators`, attaches them to records from + `input_bamfile_name`, and writes the result to `output_bamfile_name` + + Parameters + ---------- + input_bamfile_name : str + input bam + output_bamfile_name : str + output bam + tag_generators : Iterable[fastq.EmbeddedBarcodeGenerator] + Iterable of generators that yield barcodes from fastq files + + """ + bam_tagger = bam.Tagger(input_bamfile_name) + bam_tagger.tag(output_bamfile_name, tag_generators) + + @classmethod + def _make_tag_generators( + cls, r1, i1=None, whitelist=None + ) -> List[fastq.EmbeddedBarcodeGenerator]: + """Create tag generators from fastq files. + + Tag generators are iterators that run over fastq records, they extract and yield all of the + barcodes embedded in each fastq record. This means extracting the cell, umi, and/or the sample barcode. + + Parameters + ---------- + r1 : str + forward fastq file, where possibly the cell and/or molecule barcode is found + i1 : str, optional + index fastq file, where the sample barcode is found + whitelist : str, optional + A file that contains a list of acceptable cell barcodes + + Returns + ------- + tag_generators : List[EmbeddedBarcodeGenerator] + EmbeddedBarcodeGenerators containing barcodes from the given fastq + + """ + tag_generators = [] + barcode_args = {"fastq_files": r1} + + if i1: + sample_barcode_args = dict(barcode_args) + sample_barcode_args["embedded_barcodes"] = [cls.sample_barcode] + tag_generators.append(fastq.EmbeddedBarcodeGenerator(**sample_barcode_args)) + + if whitelist: + barcode_args["whitelist"] = whitelist + if cls.cell_barcode: + barcode_args["embedded_cell_barcode"] = cls.cell_barcode + if cls.molecule_barcode: + barcode_args["other_embedded_barcodes"] = [cls.molecule_barcode] + tag_generators.append( + fastq.BarcodeGeneratorWithCorrectedCellBarcodes(**barcode_args) + ) + + else: + # for all the barcodes that have a length and starting position specified + barcode_args["embedded_barcodes"] = [ + barcode + for barcode in [cls.cell_barcode, cls.molecule_barcode] + if barcode + ] + tag_generators.append(fastq.EmbeddedBarcodeGenerator(**barcode_args)) + + return tag_generators + + @classmethod + def attach_barcodes(cls, args=None): + """Command line entrypoint for attaching barcodes to a bamfile. + + Parameters + ---------- + args : Iterable[str], optional + arguments list, The default value of None, when passed to `parser.parse_args` + causes the parser to read `sys.argv` + + Returns + ------- + return_call : 0 + return call if the program completes successfully + + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "--r1", + required=True, + help="read 1 fastq file, where the cell and molecule barcode is found", + ) + parser.add_argument( + "--u2", + required=True, + help="unaligned bam, can be converted from fastq read 2" + "using picard FastqToSam", + ) + parser.add_argument( + "-o", "--output-bamfile", required=True, help="filename for tagged bam" + ) + parser.add_argument( + "-w", + "--whitelist", + default=None, + help="optional cell barcode whitelist. If provided, corrected barcodes " + "will also be output when barcodes are observed within 1ED of a " + "whitelisted barcode", + ) + parser.add_argument( + "--i1", + default=None, + help="(optional) i7 index fastq file, where the sample barcode is found", + ) + parser.add_argument( + "--sample-barcode-start-position", + dest="sample_barcode_start_pos", + default=None, + help="the user defined start position (base pairs) of the sample barcode", + type=cls._validate_barcode_start_pos, + ) + parser.add_argument( + "--sample-barcode-length", + dest="sample_barcode_length", + default=None, + help="the user defined length (base pairs) of the sample barcode", + type=cls._validate_barcode_length, + ) + parser.add_argument( + "--cell-barcode-start-position", + dest="cell_barcode_start_pos", + default=None, + help="the user defined start position, in base pairs, of the cell barcode", + type=cls._validate_barcode_start_pos, + ) + parser.add_argument( + "--cell-barcode-length", + dest="cell_barcode_length", + default=None, + help="the user defined length, in base pairs, of the cell barcode", + type=cls._validate_barcode_length, + ) + parser.add_argument( + "--molecule-barcode-start-position", + dest="molecule_barcode_start_pos", + default=None, + help="the user defined start position, in base pairs, of the molecule barcode " + "(must be not overlap cell barcode if cell barcode is provided)", + type=cls._validate_barcode_start_pos, + ) + parser.add_argument( + "--molecule-barcode-length", + dest="molecule_barcode_length", + default=None, + help="the user defined length, in base pairs, of the molecule barcode", + type=cls._validate_barcode_length, + ) + + # parse and validate the args + if args: + args = parser.parse_args(args) + else: + args = parser.parse_args() + cls._validate_barcode_args(args) + + # if the length and there for the start pos have been given as args + # get the appropriate barcodes + if args.cell_barcode_length: + cls.cell_barcode = fastq.EmbeddedBarcode( + start=args.cell_barcode_start_pos, + end=args.cell_barcode_start_pos + args.cell_barcode_length, + quality_tag=consts.QUALITY_CELL_BARCODE_TAG_KEY, + sequence_tag=consts.RAW_CELL_BARCODE_TAG_KEY, + ) + if args.molecule_barcode_length: + cls.molecule_barcode = fastq.EmbeddedBarcode( + start=args.molecule_barcode_start_pos, + end=args.molecule_barcode_start_pos + args.molecule_barcode_length, + quality_tag=consts.QUALITY_MOLECULE_BARCODE_TAG_KEY, + sequence_tag=consts.RAW_MOLECULE_BARCODE_TAG_KEY, + ) + if args.sample_barcode_length: + cls.sample_barcode = fastq.EmbeddedBarcode( + start=args.sample_barcode_start_pos, + end=args.sample_barcode_start_pos + args.sample_barcode_length, + quality_tag=consts.QUALITY_SAMPLE_BARCODE_TAG_KEY, + sequence_tag=consts.RAW_SAMPLE_BARCODE_TAG_KEY, + ) + + # make the tags and attach the barcodes + tag_generators = cls._make_tag_generators(args.r1, args.i1, args.whitelist) + cls._tag_bamfile(args.u2, args.output_bamfile, tag_generators) + + return 0 diff --git a/tools/scripts/sctools/build/lib/sctools/reader.py b/tools/scripts/sctools/build/lib/sctools/reader.py new file mode 100644 index 00000000..bc26f1cf --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/reader.py @@ -0,0 +1,204 @@ +""" +Sequence File Iterators +======================= + +.. currentmodule:: sctools + +This module defines a general iterator and some helper functions for iterating over files +that contain sequencing data + +Methods +------- +infer_open(file_: str, mode: str) + helper function that determines the compression type of a file without relying on its extension +zip_readers(*readers, indices=None) + helper function that iterates over one or more readers, optionally extracting only the records + that correspond to indices + +Classes +------- +Reader Basic reader that loops over one or more input files. + +See Also +-------- +sctools.gtf.Reader +sctools.fastq.Reader + +""" + +import os +import gzip +import bz2 +from copy import copy +from functools import partial +from typing import Callable, Iterable, Generator, Set, List + + +def infer_open(file_: str, mode: str) -> Callable: + """Helper function to infer the correct compression type of an input file + + Identifies files that are .gz or .bz2 compressed without requiring file extensions + + Parameters + ---------- + file_ : str + the file to open + mode : {'r', 'rb'} + the mode to open the file in. 'r' returns strings, 'rb' returns bytes + + Returns + ------- + open_function : Callable + the correct open function for the file's compression with mode pre-set through functools + partial + + """ + with open(file_, "rb") as f: + data: bytes = f.read(3) + + # gz and bzip treat 'r' = bytes, 'rt' = string + if data[:2] == b"\x1f\x8b": # gzip magic number + inferred_openhook: Callable = gzip.open + inferred_mode: str = "rt" if mode == "r" else mode + + elif data == b"BZh": # bz2 magic number + inferred_openhook: Callable = bz2.open + inferred_mode: str = "rt" if mode == "r" else mode + + else: + inferred_openhook: Callable = open + inferred_mode: str = mode + + return partial(inferred_openhook, mode=inferred_mode) + + +class Reader: + """Basic reader object that seamlessly loops over multiple input files. + + Is subclassed to create readers for specific file types (e.g. fastq, gtf, etc.) + + Parameters + ---------- + files : Union[str, List], optional + The file(s) to read. If '-', read sys.stdin (default = '-') + mode : {'r', 'rb'}, optional + The open mode for files. If 'r', yield string data, if 'rb', yield bytes data + (default = 'r'). + header_comment_char : str, optional + If not None, skip lines beginning with this character (default = None). + + """ + + def __init__(self, files="-", mode="r", header_comment_char=None): + if isinstance(files, str): + self._files = [files] + elif isinstance(files, Iterable): # test items of iterable + files = list(files) + if all(isinstance(f, str) for f in files): + self._files = files + else: + raise TypeError("All passed files must be type str") + else: + raise TypeError("Files must be a string filename or a list of such names.") + + # set open mode: + if mode not in {"r", "rb"}: + raise ValueError("Mode must be one of 'r', 'rb'") + self._mode = mode + + if isinstance(header_comment_char, str) and mode == "rb": + self._header_comment_char = header_comment_char.encode() + else: + self._header_comment_char = header_comment_char + + @property + def filenames(self) -> List[str]: + return self._files + + def __len__(self): + """Return the length of the Reader object. + + Notes + ----- + This function requires reading the complete file, and should typically not be + used with sys.stdin, as it will consume the input. + + """ + return sum(1 for _ in self) + + def __iter__(self): + for file_ in self._files: + + f = infer_open(file_, self._mode)(file_) + + # iterate over the file, dropping header lines if requested + try: + file_iterator = iter(f) + if self._header_comment_char is not None: + first_record = next(file_iterator) + while first_record.startswith(self._header_comment_char): + first_record = next(file_iterator) + + yield first_record # avoid loss of first non-comment line + + for record in file_iterator: # now, run to exhaustion + yield record + finally: # clean up + f.close() + + @property + def size(self) -> int: + """return the collective size of all files being read in bytes""" + return sum(os.stat(f).st_size for f in self._files) + + def select_record_indices(self, indices: Set) -> Generator: + """Iterate over provided indices only, skipping other records. + + Parameters + ---------- + indices : Set[int] + indices to include in the output + + Yields + ------ + record, str + records from file corresponding to indices + + """ + indices = copy( + indices + ) # passed indices is a reference, need own copy to modify + for idx, record in enumerate(self): + if idx in indices: + yield record + indices.remove(idx) + + # stopping condition + if not indices: + break + + +def zip_readers(*readers, indices=None) -> Generator: + """Zip together multiple reader objects, yielding records simultaneously. + + If indices is passed, only return lines in file that correspond to indices + + Parameters + ---------- + *readers : List[Reader] + Reader objects to simultaneously iterate over + indices : Set[int], optional + indices to include in the output + + Yields + ------ + records : Tuple[str] + one record per reader passed + + """ + if indices: + iterators = zip(*(r.select_record_indices(indices) for r in readers)) + else: + iterators = zip(*readers) + for record_tuple in iterators: + yield record_tuple diff --git a/tools/scripts/sctools/build/lib/sctools/stats.py b/tools/scripts/sctools/build/lib/sctools/stats.py new file mode 100644 index 00000000..a303f5fd --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/stats.py @@ -0,0 +1,103 @@ +""" +Statistics Functions for Sequence Data Analysis +=============================================== + +.. currentmodule:: sctools + +This module implements statistical modules for sequence analysis + +Methods +------- +base4_entropy(x: np.array, axis: int=1) + calculate the entropy of a 4 x sequence length base frequency matrix + +Classes +------- +OnlineGaussianSuficientStatistic Empirical (online) calculation of mean and variance + +""" + +from typing import Tuple +import numpy as np + + +def base4_entropy(x, axis=1): + """Calculate entropy in base four of a data matrix x + + Useful for measuring DNA entropy (with 4 nucleotides) as the output is restricted to [0, 1] + + Parameters + ---------- + x : np.ndarray + array of dimension one or more containing numeric types + axis : int, optional + axis to calculate entropy across. Values in this axis are treated as observation frequencies + + Returns + ------- + entropy : np.ndarray + array of input dimension - 1 containin entropy values bounded in [0, 1] + + """ + + # convert to probabilities + if axis == 1: + x = np.divide(x, np.sum(x, axis=axis)[:, None]) + else: + x = np.divide(x, np.sum(x, axis=axis)) + + with np.errstate(divide="ignore"): + r = np.log(x) / np.log(4) + + # convention: 0 * log(0) = 0, != -INF. + r[np.isinf(r)] = 0 + + return np.abs(-1 * np.sum(x * r, axis=axis)) + + +class OnlineGaussianSufficientStatistic: + """ + Implementation of Welford's online mean and variance algorithm + + Methods + ------- + update(new_value: float) + incorporate new_value into the online estimate of mean and variance + mean() + return the mean value + calculate_variance() + calculate and return the variance + mean_and_variance() + return both mean and variance + + """ + + __slots__ = ["_count", "_mean", "_mean_squared_error"] + + def __init__(self): + self._mean_squared_error: float = 0.0 + self._mean: float = 0.0 + self._count: int = 0 + + def update(self, new_value: float) -> None: + self._count += 1 + delta = new_value - self._mean + self._mean += delta / self._count + delta2 = new_value - self._mean + self._mean_squared_error += delta * delta2 + + @property + def mean(self) -> float: + """return the mean value""" + return self._mean + + def calculate_variance(self): + """calculate and return the variance""" + if self._count < 2: + return float("nan") + else: + return self._mean_squared_error / (self._count - 1) + + def mean_and_variance(self) -> Tuple[float, float]: + """calculate and return the mean and variance""" + return self.mean, self.calculate_variance() diff --git a/tools/scripts/sctools/build/lib/sctools/test/__init__.py b/tools/scripts/sctools/build/lib/sctools/test/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/scripts/sctools/build/lib/sctools/test/characterize-cell-testing-data.ipynb b/tools/scripts/sctools/build/lib/sctools/test/characterize-cell-testing-data.ipynb new file mode 100644 index 00000000..37fc8747 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/test/characterize-cell-testing-data.ipynb @@ -0,0 +1,1057 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Testing Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pysam\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:24: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n" + ] + } + ], + "source": [ + "def parse_record(record):\n", + " \"\"\"line parser to build dataframe, supports missing tags in test data\"\"\"\n", + " data = {\n", + " 'qname': record.query_name,\n", + " 'flag': record.flag,\n", + " 'reference': record.reference_id,\n", + " 'position': record.pos,\n", + " 'mapq': record.query_alignment_qualities,\n", + " 'cigar': record.cigarstring,\n", + " 'rnext': record.rnext, \n", + " 'pnext': record.pnext,\n", + " 'tlen': record.tlen, \n", + " 'sequence': record.seq,\n", + " 'quality': record.qual,\n", + " }\n", + " for name, tag in record.get_tags():\n", + " data[name] = tag\n", + " return pd.Series(data)\n", + "\n", + "input_sam_file = 'data/small-cell-sorted.bam'\n", + "with pysam.AlignmentFile(input_sam_file, 'rb') as f:\n", + " records = []\n", + " for record in f:\n", + " records.append(parse_record(record))\n", + "\n", + "data = pd.concat(records, axis=1).T\n", + "\n", + "results_scalar = {} # will hold the calculations we make\n", + "\n", + "# add a strand field\n", + "data['strand'] = [f & 16 for f in data['flag']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build Expectations for Testing Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Reads" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "656\n" + ] + } + ], + "source": [ + "results_scalar['n_reads'] = len(data)\n", + "print(results_scalar['n_reads'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Genes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11\n" + ] + } + ], + "source": [ + "results_scalar['n_genes'] = len(data.groupby(['GE']))\n", + "print(results_scalar['n_genes'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.9827586206896552\n" + ] + } + ], + "source": [ + "mean_n_genes = data.groupby(['CB']).apply(lambda x: len(set(x['GE']))).mean()\n", + "print(mean_n_genes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gene table should have 8 entries plus a header for a total of 9 lines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Molecules\n", + "\n", + "Molecules are defined as a unique triplet of CB, UB, and GE" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "249\n" + ] + } + ], + "source": [ + "results_scalar['n_molecules'] = len(data.groupby(['CB', 'UB', 'GE']))\n", + "print(results_scalar['n_molecules'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Fragments\n", + "\n", + "Fragments are defined as molecules are (CB, UB, GE) but must additionally have a unique position" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "499\n" + ] + } + ], + "source": [ + "results_scalar['n_fragments'] = len(data.groupby(['CB', 'UB', 'GE', 'position']))\n", + "print(results_scalar['n_fragments'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Most Abundant Gene\n", + "\n", + "Based on the above, at least one of the genes has to be observed more than once. Which is it? " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MTATP6P1 300\n" + ] + } + ], + "source": [ + "results_scalar['most_abundant'] = data.groupby(['GE']).size().idxmax()\n", + "results_scalar['most_abundant_gene_n_observations'] = data.groupby(['GE']).size().max()\n", + "print(results_scalar['most_abundant'], results_scalar['most_abundant_gene_n_observations'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cell with most reads" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "94" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['CB']).apply(lambda x: len(x)).max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## perfect molecule barcodes" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['perfect_molecule_barcodes'] = 0\n", + "for c, r in zip(data['UB'], data['UR']):\n", + " if c == r:\n", + " results_scalar['perfect_molecule_barcodes'] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "655" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_scalar['perfect_molecule_barcodes']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the alignment metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_exonic'] = sum(data['XF'] == 'CODING')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_intronic'] = sum(data['XF'] == 'INTRONIC')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_utr'] = sum(data['XF'] == 'UTR')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_uniquely'] = sum(data['NH'] == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['duplicate_reads'] = sum((data['flag'] & 1024).astype(bool))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['spliced_reads'] = sum(1 for v in data['cigar'] if 'N' in v)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicate_reads': 107,\n", + " 'most_abundant': 'MTATP6P1',\n", + " 'most_abundant_gene_n_observations': 300,\n", + " 'n_fragments': 499,\n", + " 'n_genes': 11,\n", + " 'n_molecules': 249,\n", + " 'n_reads': 656,\n", + " 'perfect_molecule_barcodes': 655,\n", + " 'reads_mapped_exonic': 609,\n", + " 'reads_mapped_intronic': 28,\n", + " 'reads_mapped_uniquely': 656,\n", + " 'reads_mapped_utr': 19,\n", + " 'spliced_reads': 2}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_scalar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the higher-order metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "calc_func_fraction_from_acii = lambda x: sum(1 for c in x if ord(c) > 63) / len(x)\n", + "calc_func_fraction = lambda x: sum(1 for c in x if c > 30) / len(x)\n", + "calc_func_mean = lambda x: np.mean([c for c in x])\n", + "\n", + "data['num_UY_qual_fraction'] = data['UY'].apply(calc_func_fraction_from_acii)\n", + "\n", + "data['num_base_qual_fraction'] = data['mapq'].apply(calc_func_fraction)\n", + "data['num_base_qual_mean'] = data['mapq'].apply(calc_func_mean)\n", + "\n", + "grouped_by_cell = data.groupby(['CB'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_series = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "# vector values\n", + "# I changed these to retain the index to make merging into a dataframe easier, and guarantee same order. \n", + "results_series['molecule_barcode_fraction_bases_above_30_mean'] = grouped_by_cell.mean()['num_UY_qual_fraction']\n", + "results_series['molecule_barcode_fraction_bases_above_30_variance'] = grouped_by_cell.var()['num_UY_qual_fraction']\n", + "\n", + "results_series['genomic_reads_fraction_bases_quality_above_30_mean'] = grouped_by_cell.mean()['num_base_qual_fraction']\n", + "results_series['genomic_reads_fraction_bases_quality_above_30_variance'] = grouped_by_cell.var()['num_base_qual_fraction']\n", + "results_series['genomic_read_quality_mean'] = grouped_by_cell.mean()['num_base_qual_mean']\n", + "results_series['genomic_read_quality_variance'] = grouped_by_cell.var()['num_base_qual_mean']\n", + "\n", + "reads_per_cell = data.groupby(['CB']).size()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AS 96\n", + "CB AAACCTGAGAAACCTA\n", + "CR AAACCTGAGAAACCTA\n", + "CY AAFFFJJJJJJJJJJJ\n", + "GE NaN\n", + "GS NaN\n", + "HI 1\n", + "MD 98\n", + "NH 1\n", + "NM 0\n", + "RG A\n", + "SR GTAATTGC\n", + "SY AAAFFJ\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# compare two numpy arrays that are slightly different\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0meps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1e-8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompare_me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompare_me\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (58,) (11,) " + ] + } + ], + "source": [ + "# compare two numpy arrays that are slightly different\n", + "eps = np.random.rand(11) * 1e-8\n", + "np.allclose(compare_me, compare_me + eps)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "# it is actually discriminative, though\n", + "np.allclose(compare_me, np.arange(11))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Look at the metrics output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "cell_metrics = pd.read_csv('data/cell_metrics.csv', index_col=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "cell_metrics['n_genes']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "!cat data/cell_metrics.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.4" + }, + "nav_menu": {}, + "toc": { + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 6, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/scripts/sctools/build/lib/sctools/test/characterize-gene-testing-data.ipynb b/tools/scripts/sctools/build/lib/sctools/test/characterize-gene-testing-data.ipynb new file mode 100644 index 00000000..a6a31002 --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/test/characterize-gene-testing-data.ipynb @@ -0,0 +1,1159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Testing Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pysam\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:24: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n" + ] + } + ], + "source": [ + "def parse_record(record):\n", + " \"\"\"line parser to build dataframe, supports missing tags in test data\"\"\"\n", + " data = {\n", + " 'qname': record.query_name,\n", + " 'flag': record.flag,\n", + " 'reference': record.reference_id,\n", + " 'position': record.pos,\n", + " 'mapq': record.query_alignment_qualities,\n", + " 'cigar': record.cigarstring,\n", + " 'rnext': record.rnext, \n", + " 'pnext': record.pnext,\n", + " 'tlen': record.tlen, \n", + " 'sequence': record.seq,\n", + " 'quality': record.qual,\n", + " }\n", + " for name, tag in record.get_tags():\n", + " data[name] = tag\n", + " return pd.Series(data)\n", + "\n", + "input_sam_file = 'data/small-gene-sorted.bam'\n", + "with pysam.AlignmentFile(input_sam_file, 'rb') as f:\n", + " records = []\n", + " for record in f:\n", + " records.append(parse_record(record))\n", + "\n", + "data = pd.concat(records, axis=1).T\n", + "\n", + "results_scalar = {} # will hold the calculations we make" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build Expectations for Testing Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Reads" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "300\n" + ] + } + ], + "source": [ + "results_scalar['n_reads'] = len(data)\n", + "print(results_scalar['n_reads'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Genes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "results_scalar['n_genes'] = len(data.groupby(['GE']))\n", + "print(results_scalar['n_genes'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gene table should have 8 entries plus a header for a total of 9 lines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Molecules\n", + "\n", + "Molecules are defined as a unique triplet of CB, UB, and GE" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "88\n" + ] + } + ], + "source": [ + "results_scalar['n_molecules'] = len(data.groupby(['CB', 'UB', 'GE']))\n", + "print(results_scalar['n_molecules'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of Fragments\n", + "\n", + "Fragments are defined as molecules are (CB, UB, GE) but must additionally have a unique position" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "217\n" + ] + } + ], + "source": [ + "results_scalar['n_fragments'] = len(data.groupby(['CB', 'UB', 'GE', 'position']))\n", + "print(results_scalar['n_fragments'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Most Abundant Gene\n", + "\n", + "Based on the above, at least one of the genes has to be observed more than once. Which is it? " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AL627309.7 245\n" + ] + } + ], + "source": [ + "results_scalar['most_abundant'] = data.groupby(['GE']).size().idxmax()\n", + "results_scalar['most_abundant_gene_n_observations'] = data.groupby(['GE']).size().max()\n", + "print(results_scalar['most_abundant'], results_scalar['most_abundant_gene_n_observations'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['perfect_molecule_barcodes'] = 0\n", + "for c, r in zip(data['UB'], data['UR']):\n", + " if c == r:\n", + " results_scalar['perfect_molecule_barcodes'] += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the alignment metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'most_abundant': 'AL627309.7',\n", + " 'most_abundant_gene_n_observations': 245,\n", + " 'n_fragments': 217,\n", + " 'n_genes': 8,\n", + " 'n_molecules': 88,\n", + " 'n_reads': 300,\n", + " 'perfect_molecule_barcodes': 300}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_scalar" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_exonic'] = sum(data['XF'] == 'CODING')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_intronic'] = sum(data['XF'] == 'INTRONIC')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_utr'] = sum(data['XF'] == 'UTR')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['reads_mapped_uniquely'] = sum(data['NH'] == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['duplicate_reads'] = sum((data['flag'] & 1024).astype(bool))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_scalar['spliced_reads'] = sum(1 for v in data['cigar'] if 'N' in v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the higher-order metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "calc_func_fraction_from_acii = lambda x: sum(1 for c in x if ord(c) > 63) / len(x)\n", + "calc_func_fraction = lambda x: sum(1 for c in x if c > 30) / len(x)\n", + "calc_func_mean = lambda x: np.mean([c for c in x])\n", + "\n", + "data['num_UY_qual_fraction'] = data['UY'].apply(calc_func_fraction_from_acii)\n", + "\n", + "data['num_base_qual_fraction'] = data['mapq'].apply(calc_func_fraction)\n", + "data['num_base_qual_mean'] = data['mapq'].apply(calc_func_mean)\n", + "\n", + "grouped_by_gene = data.groupby(['GE'])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "results_series = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "# vector values\n", + "# I changed these to retain the index to make merging into a dataframe easier, and guarantee same order. \n", + "results_series['molecule_barcode_fraction_bases_above_30_mean'] = grouped_by_gene.mean()['num_UY_qual_fraction']\n", + "results_series['molecule_barcode_fraction_bases_above_30_variance'] = grouped_by_gene.var()['num_UY_qual_fraction']\n", + "\n", + "results_series['genomic_reads_fraction_bases_quality_above_30_mean'] = grouped_by_gene.mean()['num_base_qual_fraction']\n", + "results_series['genomic_reads_fraction_bases_quality_above_30_variance'] = grouped_by_gene.var()['num_base_qual_fraction']\n", + "results_series['genomic_read_quality_mean'] = grouped_by_gene.mean()['num_base_qual_mean']\n", + "results_series['genomic_read_quality_variance'] = grouped_by_gene.var()['num_base_qual_mean']\n", + "\n", + "reads_per_gene = data.groupby(['GE']).size()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "molecules_per_gene = grouped_by_gene.apply(lambda x: len(x.groupby(['UB', 'CB']).size()))\n", + "fragments_per_gene = grouped_by_gene.apply(lambda x: len(x.groupby(['UB', 'CB', 'position']).size()))\n", + "reads_per_molecule = reads_per_gene / molecules_per_gene\n", + "reads_per_fragment = reads_per_gene / fragments_per_gene\n", + "fragments_per_molecule = fragments_per_gene / molecules_per_gene\n", + "results_series['reads_per_molecule'] = reads_per_molecule\n", + "results_series['reads_per_fragment'] = reads_per_fragment\n", + "results_series['fragments_per_molecule'] = fragments_per_molecule\n", + "\n", + "# scalar values\n", + "results_scalar['fragments_with_single_read_evidence'] = np.sum(data.groupby(['CB', 'UB', 'GE', 'position']).size() == 1)\n", + "results_scalar['molecules_with_single_read_evidence'] = np.sum(data.groupby(['CB', 'UB', 'GE']).size() == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fragments_per_molecule np.array([1.0000, 1.0000, 1.0000, 1.8750, 2.9831, 1.2500, 1.0000, 1.3077])\n", + "genomic_read_quality_mean np.array([36.2143, 24.8469, 25.4792, 35.3664, 34.0956, 33.0364, 20.7423, 27.3078])\n", + "genomic_read_quality_variance np.array([nan, nan, nan, 18.4553, 21.6745, 33.6572, nan, 53.5457])\n", + "genomic_reads_fraction_bases_quality_above_30_mean np.array([0.8878, 0.3980, 0.4271, 0.8148, 0.7681, 0.7216, 0.1546, 0.5089])\n", + "genomic_reads_fraction_bases_quality_above_30_variance np.array([nan, nan, nan, 0.0282, 0.0346, 0.0537, nan, 0.0849])\n", + "molecule_barcode_fraction_bases_above_30_mean np.array([1.0000, 1.0000, 0.8000, 0.9885, 0.9833, 0.9857, 0.7000, 0.9444])\n", + "molecule_barcode_fraction_bases_above_30_variance np.array([nan, nan, nan, 0.0011, 0.0051, 0.0014, nan, 0.0120])\n", + "reads_per_fragment np.array([1.0000, 1.0000, 1.0000, 1.7333, 1.3920, 1.4000, 1.0000, 1.0588])\n", + "reads_per_molecule np.array([1.0000, 1.0000, 1.0000, 3.2500, 4.1525, 1.7500, 1.0000, 1.3846])\n" + ] + } + ], + "source": [ + "# write out the array information for the testing file\n", + "for k, vals in pd.DataFrame(results_series).iteritems():\n", + " print(k, 'np.array([' + ', '.join('{:.4f}'.format(i) for i in vals.values) + '])')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write Results to File for Automated Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "pd.Series(results_scalar).to_csv('%s_testing_knowledge_scalar.csv' % input_sam_file.replace('.bam', ''))\n", + "pd.DataFrame(results_series).to_csv('%s_testing_knowledge_series.csv' % input_sam_file.replace('.bam', ''))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# do a comparison of the whole 2d dataframe at once\n", + "np.allclose(\n", + " pd.DataFrame(results_series).fillna(0).values, # fill nans with zero, call values to get the numpy array the dataframe is based on\n", + " pd.read_csv('data/small-gene-sorted_testing_knowledge_series.csv', index_col=0, header=0).fillna(0).values\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "# to get most_abundant alone: " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_read_scalar = pd.read_csv('data/small-gene-sorted_testing_knowledge_scalar.csv', index_col=0, header=None, squeeze=True)\n", + "\n", + "# extract this, we're going to drop it from the array to do some conversion to numeric\n", + "most_abundant = test_read_scalar['most_abundant'] \n", + "\n", + "# drop most abundant, convert to float, fill any NaN values with 0, and call .values to get the numpy array pandas objects are based on.\n", + "for_comparison = test_read_scalar.drop('most_abundant').astype(float).fillna(0).values\n", + "\n", + "\n", + "# note, have to drop the string value and convert to float before this works. \n", + "np.allclose(\n", + " pd.Series(results_scalar).drop('most_abundant').fillna(0).values, # do the same thing as above to the one in memory\n", + " for_comparison\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GE\n", + "ACAP3 36.214286\n", + "AGRN 24.846939\n", + "AL627309.1 25.479167\n", + "AL627309.5 35.366414\n", + "AL627309.7 34.095625\n", + "AL645608.2 33.036443\n", + "AL645608.3 20.742268\n", + "AL645608.4 27.307758\n", + "Name: genomic_read_quality_mean, dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get a metric from a dataframe: \n", + "df = pd.DataFrame(results_series)\n", + "df['genomic_read_quality_mean']" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "# get a numpy array from the dataframe\n", + "compare_me = df['genomic_read_quality_mean'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compare two numpy arrays that are slightly different\n", + "eps = np.random.rand(8) * 1e-8\n", + "np.allclose(compare_me, compare_me + eps)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# it is actually discriminative, though\n", + "np.allclose(compare_me, np.arange(8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Look at the metrics output" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + } + }, + "outputs": [], + "source": [ + "gene_metrics = pd.read_csv('data/gene_metrics.csv', index_col=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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n_readsnoise_readsperfect_molecule_barcodesreads_mapped_exonicreads_mapped_intronicreads_mapped_utrreads_mapped_uniquelyreads_mapped_multipleduplicate_readsspliced_reads...genomic_read_quality_variancen_moleculesn_fragmentsreads_per_moleculereads_per_fragmentfragments_per_moleculefragments_with_single_read_evidencemolecules_with_single_read_evidencenumber_cells_detected_multiplenumber_cells_expressing
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AL645608.418018180018010...53.54574013171.3846151.0588241.3076921612113
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8 rows × 26 columns

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" + ], + "text/plain": [ + " n_reads noise_reads perfect_molecule_barcodes \\\n", + "ACAP3 1 0 1 \n", + "AGRN 1 0 1 \n", + "AL627309.1 1 0 1 \n", + "AL627309.5 26 0 26 \n", + "AL627309.7 245 0 245 \n", + "AL645608.2 7 0 7 \n", + "AL645608.3 1 0 1 \n", + "AL645608.4 18 0 18 \n", + "\n", + " reads_mapped_exonic reads_mapped_intronic reads_mapped_utr \\\n", + "ACAP3 1 0 0 \n", + "AGRN 1 0 0 \n", + "AL627309.1 1 0 0 \n", + "AL627309.5 26 0 0 \n", + "AL627309.7 245 0 0 \n", + "AL645608.2 7 0 0 \n", + "AL645608.3 1 0 0 \n", + "AL645608.4 18 0 0 \n", + "\n", + " reads_mapped_uniquely reads_mapped_multiple duplicate_reads \\\n", + "ACAP3 1 0 0 \n", + "AGRN 1 0 0 \n", + "AL627309.1 1 0 0 \n", + "AL627309.5 26 0 11 \n", + "AL627309.7 245 0 76 \n", + "AL645608.2 7 0 2 \n", + "AL645608.3 1 0 0 \n", + "AL645608.4 18 0 1 \n", + "\n", + " spliced_reads ... \\\n", + "ACAP3 1 ... \n", + "AGRN 1 ... \n", + "AL627309.1 1 ... \n", + "AL627309.5 26 ... \n", + "AL627309.7 0 ... \n", + "AL645608.2 0 ... \n", + "AL645608.3 0 ... \n", + "AL645608.4 0 ... \n", + "\n", + " genomic_read_quality_variance n_molecules n_fragments \\\n", + "ACAP3 NaN 1 1 \n", + "AGRN NaN 1 1 \n", + "AL627309.1 NaN 1 1 \n", + "AL627309.5 18.455293 8 15 \n", + "AL627309.7 21.674500 59 176 \n", + "AL645608.2 33.657186 4 5 \n", + "AL645608.3 NaN 1 1 \n", + "AL645608.4 53.545740 13 17 \n", + "\n", + " reads_per_molecule reads_per_fragment fragments_per_molecule \\\n", + "ACAP3 1.000000 1.000000 1.000000 \n", + "AGRN 1.000000 1.000000 1.000000 \n", + "AL627309.1 1.000000 1.000000 1.000000 \n", + "AL627309.5 3.250000 1.733333 1.875000 \n", + "AL627309.7 4.152542 1.392045 2.983051 \n", + "AL645608.2 1.750000 1.400000 1.250000 \n", + "AL645608.3 1.000000 1.000000 1.000000 \n", + "AL645608.4 1.384615 1.058824 1.307692 \n", + "\n", + " fragments_with_single_read_evidence \\\n", + "ACAP3 1 \n", + "AGRN 1 \n", + "AL627309.1 1 \n", + "AL627309.5 7 \n", + "AL627309.7 124 \n", + "AL645608.2 4 \n", + "AL645608.3 1 \n", + "AL645608.4 16 \n", + "\n", + " molecules_with_single_read_evidence \\\n", + "ACAP3 1 \n", + "AGRN 1 \n", + "AL627309.1 1 \n", + "AL627309.5 2 \n", + "AL627309.7 22 \n", + "AL645608.2 2 \n", + "AL645608.3 1 \n", + "AL645608.4 12 \n", + "\n", + " number_cells_detected_multiple number_cells_expressing \n", + "ACAP3 0 1 \n", + "AGRN 0 1 \n", + "AL627309.1 0 1 \n", + "AL627309.5 6 8 \n", + "AL627309.7 38 57 \n", + "AL645608.2 2 4 \n", + "AL645608.3 0 1 \n", + "AL645608.4 1 13 \n", + "\n", + "[8 rows x 26 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gene_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "run_control": { + "frozen": false, + "read_only": false + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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a/tools/scripts/sctools/build/lib/sctools/test/data/1k-august-2016.txt b/tools/scripts/sctools/build/lib/sctools/test/data/1k-august-2016.txt new file mode 100644 index 00000000..54b0b83b --- /dev/null +++ b/tools/scripts/sctools/build/lib/sctools/test/data/1k-august-2016.txt @@ -0,0 +1,1001 @@ +AAACCTGAGAAACCAT +AAACCTGAGAAACCGC +AAACCTGAGAAACCTA +AAACCTGAGAAACGAG +AAACCTGAGAAACGCC +AAACCTGAGAAAGTGG +AAACCTGAGAACAACT +AAACCTGAGAACAATC +AAACCTGAGAACTCGG +AAACCTGAGAACTGTA +AAACCTGAGAAGAAGC +AAACCTGAGAAGATTC +AAACCTGAGAAGCCCA +AAACCTGAGAAGGACA +AAACCTGAGAAGGCCT +AAACCTGAGAAGGGTA +AAACCTGAGAAGGTGA +AAACCTGAGAAGGTTT +AAACCTGAGAATAGGG +AAACCTGAGAATCTCC +AAACCTGAGAATGTGT +AAACCTGAGAATGTTG +AAACCTGAGAATTCCC +AAACCTGAGAATTGTG +AAACCTGAGACAAAGG +AAACCTGAGACAAGCC +AAACCTGAGACAATAC +AAACCTGAGACACGAC +AAACCTGAGACACTAA +AAACCTGAGACAGACC +AAACCTGAGACAGAGA +AAACCTGAGACAGGCT +AAACCTGAGACATAAC +AAACCTGAGACCACGA +AAACCTGAGACCCACC +AAACCTGAGACCGGAT +AAACCTGAGACCTAGG +AAACCTGAGACCTTTG +AAACCTGAGACGACGT 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