python/intro/intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to cBioPortal REST API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This [Jupyter notebook](https://jupyter.org/) gives examples on how to use the [REST](https://en.wikipedia.org/wiki/Representational_state_transfer) web service from [cBioPortal](https://www.cbioportal.org) as well as other webservices from the [Knowledge Systems Group at MSKCC](https://www.mskcc.org/research-areas/labs/nikolaus-schultz). We will pull data from those APIs to make visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to run the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook can be executed on your own machine after installing Jupyter. Please install the Python 3 version of anaconda: https://www.anaconda.com/download/. After having that set up you can install Jupyter with:\n",
    "\n",
    "```bash\n",
    "conda install jupyter\n",
    "\n",
    "```\n",
    "\n",
    "For these examples we also require the [Swagger API](https://swagger.io/specification/) client `bravado`.\n",
    "\n",
    "```bash\n",
    "conda install -c conda-forge bravado\n",
    "```\n",
    "\n",
    "And the popular data analysis libraries pandas, matplotlib and seaborn:\n",
    "\n",
    "```\n",
    "conda install pandas matplotlib seaborn\n",
    "```\n",
    "\n",
    "Then clone this repo:\n",
    "\n",
    "```\n",
    "git clone https://github.com/cbioportal/workbench\n",
    "```\n",
    "\n",
    "And run Jupyter in this folder\n",
    "```\n",
    "cd workbench/intro\n",
    "jupyter notebook\n",
    "```\n",
    "That should open Jupyter in a new browser window and you should be able to open this notebook using the web interface. You can then follow along with the next steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to use the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook consists of cells which can be executed by clicking on one and pressing shift+f. In the toolbar at the top there is a dropdown which indicates what type of cell you have selected e.g. `Code` or [Markdown](https://en.wikipedia.org/wiki/Markdown). The former will be executed as raw Python code the latter is a markup language and will be run through a Markdown parser. Both generate HTML that will be printed directly to the notebook page.\n",
    "\n",
    "There a few keyboard shortcuts that are good to know. That is: `b` creates a new cell below the one you've selected and `a` above the one you selected. Editing a cell can be done with a single click for a code cell and a double click for a Markdown cell. A complete list of all keyboard shortcuts can be found by pressing the keyboard icon in the toolbar at the top.\n",
    "\n",
    " Give it a shot by editing one of the cells and pressing shift+f."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the REST APIs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All [REST](https://en.wikipedia.org/wiki/Representational_state_transfer) web services from the [Knowledge Systems Group](https://www.mskcc.org/research-areas/labs/nikolaus-schultz) we will be using in this tutorial have their REST APIs defined following the [Open API / Swagger specification](https://swagger.io/specification/). This allows us to use `bravado` to connect to them directly, and explore the API interactively.\n",
    "\n",
    "For example this is how to connect to the [cBioPortal](https://www.cbioportal.org) API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SwaggerClient(https://www.cbioportal.org/api)\n"
     ]
    }
   ],
   "source": [
    "from bravado.client import SwaggerClient\n",
    "\n",
    "cbioportal = SwaggerClient.from_url('https://www.cbioportal.org/api/v2/api-docs',\n",
    "                                config={\"validate_requests\":False,\n",
    "                                        \"validate_responses\":False,\n",
    "                                       \"validate_swagger_spec\": False})\n",
    "print(cbioportal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now explore the API by using code completion, press `Tab` after typing `cbioportal.`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbioportal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will give a dropdown with all the different APIs, similar to how you can see them here on the cBioPortal website: https://www.cbioportal.org/api/swagger-ui.html#/. If you don't see the dropdown, some people have found that running `%config Completer.use_jedi = False` fixes the problem. \n",
    "\n",
    "You can also get the parameters to a specific endpoint by pressing shift+tab twice after typing the name of the specific endpoint e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bravado.http_future.HttpFuture at 0x1180fc6a0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbioportal.Cancer_Types.getAllCancerTypesUsingGET("
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That shows one of the parameters is `cancerTypeId` of type `string`, the example `acc` is mentioned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TypeOfCancer(cancerTypeId='acc', dedicatedColor='Purple', name='Adrenocortical Carcinoma', parent='adrenal_gland', shortName='ACC')\n"
     ]
    }
   ],
   "source": [
    "acc = cbioportal.Cancer_Types.getCancerTypeUsingGET(cancerTypeId='acc').result()\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the JSON output returned by the cBioPortal API gets automatically converted into an object called `TypeOfCancer`. This object can be explored interactively as well by pressing tab after typing `acc.`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'adrenal_gland'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cBioPortal API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[cBioPortal](https://www.cbioportal.org) stores cancer genomics data from a large number of published studies. Let's figure out:\n",
    "\n",
    "- how many studies are there?\n",
    "- how many cancer types do they span?\n",
    "- how many samples in total?\n",
    "- which study has the largest number of samples?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In total there are 409 studies in cBioPortal, spanning 885 different types of cancer.\n"
     ]
    }
   ],
   "source": [
    "studies = cbioportal.Studies.getAllStudiesUsingGET().result()\n",
    "cancer_types = cbioportal.Cancer_Types.getAllCancerTypesUsingGET().result()\n",
    "\n",
    "print(\"In total there are {} studies in cBioPortal, spanning {} different types of cancer.\".format(\n",
    "    len(studies),\n",
    "    len(cancer_types)\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the total number of samples in each study we have to look a bit more at the response of the studies endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CancerStudy(allSampleCount=12, cancerType=None, cancerTypeId='acbc', citation='Martelotto et al. J Pathol 2015', cnaSampleCount=None, completeSampleCount=None, description='Whole exome sequencing of 12 breast AdCCs.', groups='ACYC;PUBLIC', importDate='2023-12-06 19:10:36', massSpectrometrySampleCount=None, methylationHm27SampleCount=None, miRnaSampleCount=None, mrnaMicroarraySampleCount=None, mrnaRnaSeqSampleCount=None, mrnaRnaSeqV2SampleCount=None, name='Adenoid Cystic Carcinoma of the Breast (MSK, J Pathol. 2015)', pmid='26095796', publicStudy=True, readPermission=True, referenceGenome='hg19', rppaSampleCount=None, sequencedSampleCount=None, status=0, studyId='acbc_mskcc_2015', treatmentCount=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "studies[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['allSampleCount',\n",
       " 'cancerType',\n",
       " 'cancerTypeId',\n",
       " 'citation',\n",
       " 'cnaSampleCount',\n",
       " 'completeSampleCount',\n",
       " 'description',\n",
       " 'groups',\n",
       " 'importDate',\n",
       " 'massSpectrometrySampleCount',\n",
       " 'methylationHm27SampleCount',\n",
       " 'miRnaSampleCount',\n",
       " 'mrnaMicroarraySampleCount',\n",
       " 'mrnaRnaSeqSampleCount',\n",
       " 'mrnaRnaSeqV2SampleCount',\n",
       " 'name',\n",
       " 'pmid',\n",
       " 'publicStudy',\n",
       " 'readPermission',\n",
       " 'referenceGenome',\n",
       " 'rppaSampleCount',\n",
       " 'sequencedSampleCount',\n",
       " 'status',\n",
       " 'studyId',\n",
       " 'treatmentCount']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(studies[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sum the `allSampleCount` values of each study in cBioPortal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of samples in all studies is: 258868\n"
     ]
    }
   ],
   "source": [
    "print(\"The total number of samples in all studies is: {}\".format(sum([x.allSampleCount for x in studies])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which study has the largest number of samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CancerStudy(allSampleCount=47530, cancerType=None, cancerTypeId='mixed', citation='Stonestrom et al. Blood Adv 2023', cnaSampleCount=None, completeSampleCount=None, description='Clonal hematopoiesis mutations identified in blood samples from 47,532 patients whose tumor-blood pairs were analyzed using MSK-IMPACT.', groups='', importDate='2023-12-21 13:11:31', massSpectrometrySampleCount=None, methylationHm27SampleCount=None, miRnaSampleCount=None, mrnaMicroarraySampleCount=None, mrnaRnaSeqSampleCount=None, mrnaRnaSeqV2SampleCount=None, name='Cancer Therapy and Clonal Hematopoiesis (MSK, Blood Adv 2023)', pmid=None, publicStudy=True, readPermission=True, referenceGenome='hg19', rppaSampleCount=None, sequencedSampleCount=None, status=0, studyId='msk_ch_2023', treatmentCount=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_studies = sorted(studies, key=lambda x: x.allSampleCount)\n",
    "sorted_studies[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also easily see e.g. the top 3 largest studies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[CancerStudy(allSampleCount=24146, cancerType=None, cancerTypeId='mixed', citation='Kelly et al. Nat Genet 2020', cnaSampleCount=None, completeSampleCount=None, description='Clonal hematopoiesis mutations identified in blood samples from 24,146 patients whose tumor-blood pairs were analyzed using MSK-IMPACT.', groups='PUBLIC', importDate='2023-12-08 13:24:19', massSpectrometrySampleCount=None, methylationHm27SampleCount=None, miRnaSampleCount=None, mrnaMicroarraySampleCount=None, mrnaRnaSeqSampleCount=None, mrnaRnaSeqV2SampleCount=None, name='Cancer Therapy and Clonal Hematopoiesis (MSK, Nat Genet 2020)', pmid='33106634', publicStudy=True, readPermission=True, referenceGenome='hg19', rppaSampleCount=None, sequencedSampleCount=None, status=0, studyId='msk_ch_2020', treatmentCount=None),\n",
       " CancerStudy(allSampleCount=25775, cancerType=None, cancerTypeId='mixed', citation='Nguyen et al. Cell 2022', cnaSampleCount=None, completeSampleCount=None, description='MSK-MET (Memorial Sloan Kettering - Metastatic Events and Tropisms) is an integrated pan-cancer cohort of tumor genomic and clinical outcome data from 25,000 patients. We analyzed this dataset to identify associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma and HR+/HER2+ breast ductal carcinoma, but not in others, such as colorectal MSS, pancreatic adenocarcinoma and high-grade serous ovarian cancer. We also identified specific somatic alterations associated with increased metastatic burden and specific routes of metastatic spread. Our data offer a unique resource for the investigation of the biologic basis for metastatic spread and highlight the crucial role of chromosomal instability in cancer progression', groups='', importDate='2023-12-08 13:25:01', massSpectrometrySampleCount=None, methylationHm27SampleCount=None, miRnaSampleCount=None, mrnaMicroarraySampleCount=None, mrnaRnaSeqSampleCount=None, mrnaRnaSeqV2SampleCount=None, name='MSK MetTropism (MSK, Cell 2021)', pmid='35120664', publicStudy=True, readPermission=True, referenceGenome='hg19', rppaSampleCount=None, sequencedSampleCount=None, status=0, studyId='msk_met_2021', treatmentCount=None),\n",
       " CancerStudy(allSampleCount=47530, cancerType=None, cancerTypeId='mixed', citation='Stonestrom et al. Blood Adv 2023', cnaSampleCount=None, completeSampleCount=None, description='Clonal hematopoiesis mutations identified in blood samples from 47,532 patients whose tumor-blood pairs were analyzed using MSK-IMPACT.', groups='', importDate='2023-12-21 13:11:31', massSpectrometrySampleCount=None, methylationHm27SampleCount=None, miRnaSampleCount=None, mrnaMicroarraySampleCount=None, mrnaRnaSeqSampleCount=None, mrnaRnaSeqV2SampleCount=None, name='Cancer Therapy and Clonal Hematopoiesis (MSK, Blood Adv 2023)', pmid=None, publicStudy=True, readPermission=True, referenceGenome='hg19', rppaSampleCount=None, sequencedSampleCount=None, status=0, studyId='msk_ch_2023', treatmentCount=None)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_studies = sorted(studies, key=lambda x: x.allSampleCount)\n",
    "sorted_studies[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've answered the inital questions we can dig a little deeper into a specific study. Let's use the second largest study (`msk_impact_2017`):\n",
    "\n",
    "- How many patients are in this study?\n",
    "- What gene is most commonly mutated across the different samples?\n",
    "- Does this study span one or more types of cancer?\n",
    "\n",
    "The description of the study with id `msk_impact_2017` study mentions there are 24,146 patients sequenced. Can we find this data in the cBioPortal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The msk_impact_2017 study spans 10336 patients\n"
     ]
    }
   ],
   "source": [
    "patients = cbioportal.Patients.getAllPatientsInStudyUsingGET(studyId='msk_impact_2017').result()\n",
    "print(\"The msk_impact_2017 study spans {} patients\".format(len(patients)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to figure out what gene is most commonly mutated. For this we can check the endpoints in the group `Mutations`. When looking at these endpoints it seems that a study can have multiple molecular profiles. This is because samples might have been sequenced using different assays (e.g. targeting a subset of genes or all genes). An example for the `acc_tcga` study is given for a molecular profile (`acc_tcga_mutations`) and a collection of samples (`acc_tcga_all`). We can use the same approach for the `msk_impact_2017` study. This will take a few seconds.  You can use the command `%%time` to time a cell):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.03 s, sys: 65.1 ms, total: 1.1 s\n",
      "Wall time: 5.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "mutations = cbioportal.Mutations.getMutationsInMolecularProfileBySampleListIdUsingGET(\n",
    "    molecularProfileId='msk_impact_2017_mutations',\n",
    "    sampleListId='msk_impact_2017_all'\n",
    ").result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can explore what the mutation data structure looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mutation(alleleSpecificCopyNumber=None, aminoAcidChange=None, center='NA', chr='9', driverFilter=None, driverFilterAnnotation=None, driverTiersFilter=None, driverTiersFilterAnnotation=None, endPosition=133760514, entrezGeneId=25, gene=None, keyword='ABL1 truncating', molecularProfileId='msk_impact_2017_mutations', mutationStatus='NA', mutationType='Frame_Shift_Del', namespaceColumns=None, ncbiBuild='GRCh37', normalAltCount=None, normalRefCount=None, patientId='P-0000507', proteinChange='K947Sfs*122', proteinPosEnd=947, proteinPosStart=946, referenceAllele='C', refseqMrnaId='NM_005157.4', sampleId='P-0000507-T01-IM3', startPosition=133760514, studyId='msk_impact_2017', tumorAltCount=80, tumorRefCount=759, uniquePatientKey='UC0wMDAwNTA3Om1za19pbXBhY3RfMjAxNw', uniqueSampleKey='UC0wMDAwNTA3LVQwMS1JTTM6bXNrX2ltcGFjdF8yMDE3', validationStatus='NA', variantAllele='-', variantType='DEL')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutations[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the `gene` field is not filled in. To keep the response size of the API small, the API uses a parameter called `projection` that indicates whether or not to return all fields of an object or only a portion of the fields. By default it will use the `SUMMARY` projection. But because in this case we want to `gene` information, we'll use the `DETAILED` projection instead, so let's update the previous statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.59 s, sys: 70.6 ms, total: 1.66 s\n",
      "Wall time: 6.02 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "mutations = cbioportal.Mutations.getMutationsInMolecularProfileBySampleListIdUsingGET(\n",
    "    molecularProfileId='msk_impact_2017_mutations',\n",
    "    sampleListId='msk_impact_2017_all',\n",
    "    projection='DETAILED'\n",
    ").result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the response time is slightly slower. Let's check if the gene field is filled in now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mutation(alleleSpecificCopyNumber=None, aminoAcidChange=None, center='NA', chr='9', driverFilter=None, driverFilterAnnotation=None, driverTiersFilter=None, driverTiersFilterAnnotation=None, endPosition=133760514, entrezGeneId=25, gene=Gene(entrezGeneId=25, geneticEntityId=None, hugoGeneSymbol='ABL1', type='protein-coding'), keyword='ABL1 truncating', molecularProfileId='msk_impact_2017_mutations', mutationStatus='NA', mutationType='Frame_Shift_Del', namespaceColumns=None, ncbiBuild='GRCh37', normalAltCount=None, normalRefCount=None, patientId='P-0000507', proteinChange='K947Sfs*122', proteinPosEnd=947, proteinPosStart=946, referenceAllele='C', refseqMrnaId='NM_005157.4', sampleId='P-0000507-T01-IM3', startPosition=133760514, studyId='msk_impact_2017', tumorAltCount=80, tumorRefCount=759, uniquePatientKey='UC0wMDAwNTA3Om1za19pbXBhY3RfMjAxNw', uniqueSampleKey='UC0wMDAwNTA3LVQwMS1JTTM6bXNrX2ltcGFjdF8yMDE3', validationStatus='NA', variantAllele='-', variantType='DEL')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutations[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the gene field we can check what gene is most commonly mutated: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TP53', 4954),\n",
       " ('APC', 1674),\n",
       " ('KRAS', 1670),\n",
       " ('TERT', 1549),\n",
       " ('PIK3CA', 1517)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "mutation_counts = Counter([m.gene.hugoGeneSymbol for m in mutations])\n",
    "mutation_counts.most_common(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify that these results are correct by looking at the study view of the MSK-IMPACT study on the cBioPortal website: https://www.cbioportal.org/study/summary?id=msk_impact_2017. Note that the website uses the REST API we've been using in this hackathon, so we would expect those numbers to be the same, but good to do a sanity check. We see that the number of patients is indeed 10,336. But the number of samples with a mutation in TP53 is 4,561 instead of 4,985. Can you spot why they differ?\n",
    "\n",
    "Next question:\n",
    "\n",
    "- How many samples have a TP53 mutation?\n",
    "\n",
    "For this exercise it might be useful to use a [pandas dataframe](https://pandas.pydata.org/) to be able to do grouping operations. You can convert the mutations result to a dataframe like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alleleSpecificCopyNumber': None,\n",
       " 'aminoAcidChange': None,\n",
       " 'center': 'NA',\n",
       " 'chr': '9',\n",
       " 'driverFilter': None,\n",
       " 'driverFilterAnnotation': None,\n",
       " 'driverTiersFilter': None,\n",
       " 'driverTiersFilterAnnotation': None,\n",
       " 'endPosition': 133760514,\n",
       " 'entrezGeneId': 25,\n",
       " 'gene': Gene(entrezGeneId=25, geneticEntityId=None, hugoGeneSymbol='ABL1', type='protein-coding'),\n",
       " 'keyword': 'ABL1 truncating',\n",
       " 'molecularProfileId': 'msk_impact_2017_mutations',\n",
       " 'mutationStatus': 'NA',\n",
       " 'mutationType': 'Frame_Shift_Del',\n",
       " 'namespaceColumns': None,\n",
       " 'ncbiBuild': 'GRCh37',\n",
       " 'normalAltCount': None,\n",
       " 'normalRefCount': None,\n",
       " 'patientId': 'P-0000507',\n",
       " 'proteinChange': 'K947Sfs*122',\n",
       " 'proteinPosEnd': 947,\n",
       " 'proteinPosStart': 946,\n",
       " 'referenceAllele': 'C',\n",
       " 'refseqMrnaId': 'NM_005157.4',\n",
       " 'sampleId': 'P-0000507-T01-IM3',\n",
       " 'startPosition': 133760514,\n",
       " 'studyId': 'msk_impact_2017',\n",
       " 'tumorAltCount': 80,\n",
       " 'tumorRefCount': 759,\n",
       " 'uniquePatientKey': 'UC0wMDAwNTA3Om1za19pbXBhY3RfMjAxNw',\n",
       " 'uniqueSampleKey': 'UC0wMDAwNTA3LVQwMS1JTTM6bXNrX2ltcGFjdF8yMDE3',\n",
       " 'validationStatus': 'NA',\n",
       " 'variantAllele': '-',\n",
       " 'variantType': 'DEL'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{k:getattr(mutations[0],k) for k in dir(mutations[0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "mdf = pd.DataFrame.from_dict([\n",
    "    # python magic that combines two dictionaries:\n",
    "    dict(\n",
    "        {k:getattr(m,k) for k in dir(m)},\n",
    "        **{k:getattr(m.gene,k) for k in dir(m.gene)}) \n",
    "    # create one item in the list for each mutation\n",
    "    for m in mutations\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['alleleSpecificCopyNumber', 'aminoAcidChange', 'center', 'chr',\n",
       "       'driverFilter', 'driverFilterAnnotation', 'driverTiersFilter',\n",
       "       'driverTiersFilterAnnotation', 'endPosition', 'entrezGeneId', 'gene',\n",
       "       'keyword', 'molecularProfileId', 'mutationStatus', 'mutationType',\n",
       "       'namespaceColumns', 'ncbiBuild', 'normalAltCount', 'normalRefCount',\n",
       "       'patientId', 'proteinChange', 'proteinPosEnd', 'proteinPosStart',\n",
       "       'referenceAllele', 'refseqMrnaId', 'sampleId', 'startPosition',\n",
       "       'studyId', 'tumorAltCount', 'tumorRefCount', 'uniquePatientKey',\n",
       "       'uniqueSampleKey', 'validationStatus', 'variantAllele', 'variantType',\n",
       "       'geneticEntityId', 'hugoGeneSymbol', 'type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>startPosition</th>\n",
       "      <th>endPosition</th>\n",
       "      <th>referenceAllele</th>\n",
       "      <th>variantAllele</th>\n",
       "      <th>hugoGeneSymbol</th>\n",
       "      <th>proteinChange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>133760514</td>\n",
       "      <td>133760514</td>\n",
       "      <td>C</td>\n",
       "      <td>-</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>K947Sfs*122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>133760322</td>\n",
       "      <td>133760322</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>S882C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>133759443</td>\n",
       "      <td>133759443</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>R589H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>133759488</td>\n",
       "      <td>133759488</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>I604N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>133759683</td>\n",
       "      <td>133759683</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>P669L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chr  startPosition  endPosition referenceAllele variantAllele  \\\n",
       "0   9      133760514    133760514               C             -   \n",
       "1   9      133760322    133760322               C             G   \n",
       "2   9      133759443    133759443               G             A   \n",
       "3   9      133759488    133759488               T             A   \n",
       "4   9      133759683    133759683               C             T   \n",
       "\n",
       "  hugoGeneSymbol proteinChange  \n",
       "0           ABL1   K947Sfs*122  \n",
       "1           ABL1         S882C  \n",
       "2           ABL1         R589H  \n",
       "3           ABL1         I604N  \n",
       "4           ABL1         P669L  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf['chr startPosition endPosition referenceAllele variantAllele hugoGeneSymbol proteinChange'.split()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame is a data type originally from `Matlab` and `R` that makes it easier to work with columnar data. Pandas brings that data type to Python. There are also several performance optimizations by it using the data types from [numpy](https://www.numpy.org/).\n",
    "\n",
    "Now that you have the data in a Dataframe you can group the mutations by the gene name and count the number of unique samples in TP53:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4538 samples with a mutation in TP53\n"
     ]
    }
   ],
   "source": [
    "sample_count_per_gene = mdf.groupby('hugoGeneSymbol')['uniqueSampleKey'].nunique()\n",
    "\n",
    "print(\"There are {} samples with a mutation in TP53\".format(\n",
    "    sample_count_per_gene['TP53']\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be nice to visualize this result in context of the other genes by plotting the top 10 most mutated genes. For this you can use the matplotlib interface that integrates with pandas.\n",
    "\n",
    "First  inline plotting in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hugoGeneSymbol'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_count_per_gene.sort_values(ascending=False).head(10).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make it look a little nicer by importing seaborn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_count_per_gene.sort_values(ascending=False).head(10).plot(kind='bar')\n",
    "sns.despine(trim=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can further change the plot a bit by using the arguments to the plot function or using the matplotlib interface directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of mutations in genes in MSK-IMPACT (2017)')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sample_count_per_gene.sort_values(ascending=False).head(10).plot(\n",
    "    kind='bar',\n",
    "    ylim=[0,5000],\n",
    "    color='green'\n",
    ")\n",
    "sns.despine(trim=False)\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation=300)\n",
    "plt.ylabel('Number of samples',labelpad=20)\n",
    "plt.title('Number of mutations in genes in MSK-IMPACT (2017)',pad=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A further extension of this plot could be to color the bar chart by the type of mutation in that sample (`mdf.mutationType`) and to include copy number alterations (see `Discrete Copy Number Alterations` endpoints)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gene Frequency for specific Cancer Type\n",
    "First get the cancer type for each sample via the clinical data endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "clin_data = cbioportal.Clinical_Data.getAllClinicalDataInStudyUsingGET(studyId='msk_impact_2017').result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert into dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cdf = pd.DataFrame.from_dict(\n",
    "    {k:getattr(cd,k) for k in dir(cd)} for cd in clin_data\n",
    ").pivot(index='sampleId patientId'.split(),columns='clinicalAttributeId',values='value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge together with the mutations MAF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "clin_mut_df = mdf.merge(cdf, on='sampleId', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then plot the top 10 most mutated genes for colorectal cancer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter clin_mut_df for Colorectal Cancer\n",
    "colorectal_df = clin_mut_df[clin_mut_df['CANCER_TYPE'] == 'Colorectal Cancer']\n",
    "\n",
    "# Group by hugoGeneSymbol and count unique patientId\n",
    "gene_counts = colorectal_df.groupby('hugoGeneSymbol')['patientId'].nunique()\n",
    "\n",
    "# Sort in descending order and take top 10\n",
    "sorted_counts = gene_counts.sort_values(ascending=False).head(30)\n",
    "\n",
    "# Create bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=sorted_counts.index, y=sorted_counts.values)\n",
    "plt.xlabel('hugoGeneSymbol')\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.title('Top 30 hugoGeneSymbols in Colorectal Cancer')\n",
    "plt.xticks(rotation=45)\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do the same by frequency as well. For MSK-IMPACT this is slightly tricky because not all samples were profiled for the same gene. For convenience we will ignore this fact and assume that all samples were profiled for all genes. This is not true, but it will give you a rough idea of the frequency of mutations in the MSK-IMPACT study. First get the total number of cases for colorectal cancer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_type_num_patients = cdf.reset_index().groupby(\"CANCER_TYPE\")[\"patientId\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the top 10 most mutated genes for colorectal cancer by frequency in cohort:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter clin_mut_df for Colorectal Cancer\n",
    "cancer_type = 'Colorectal Cancer'\n",
    "colorectal_df = clin_mut_df[clin_mut_df['CANCER_TYPE'] == cancer_type]\n",
    "nr_patients_for_cancer_type = cancer_type_num_patients[cancer_type]\n",
    "\n",
    "# Group by hugoGeneSymbol and count unique patientId\n",
    "gene_counts = colorectal_df.groupby('hugoGeneSymbol')['patientId'].nunique() * 100.0 / nr_patients_for_cancer_type\n",
    "\n",
    "# Sort in descending order and take top 10\n",
    "sorted_counts = gene_counts.sort_values(ascending=False).head(30)\n",
    "\n",
    "# Create bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=sorted_counts.index, y=sorted_counts.values)\n",
    "plt.xlabel('hugoGeneSymbol')\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.title('Top 30 hugoGeneSymbols in {} Cancer (n={})'.format(cancer_type, nr_patients_for_cancer_type))\n",
    "plt.xticks(rotation=45)\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Alteration frequency across cancer types per gene\n",
    "We previously only worked with mutation data. We will need to get the copy number as well. SV data is not available yet in the API. First get the copy number data for the MSK-IMPACT study:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hugoGeneSymbol</th>\n",
       "      <th>alteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMS2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RAC1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRCA2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CD79B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NBN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  hugoGeneSymbol  alteration\n",
       "0           PMS2           2\n",
       "1           RAC1           2\n",
       "2          BRCA2           2\n",
       "3          CD79B           2\n",
       "4            NBN           2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cna_data = cbioportal.Discrete_Copy_Number_Alterations.getDiscreteCopyNumbersInMolecularProfileUsingGET(molecularProfileId='msk_impact_2017_cna',sampleListId='msk_impact_2017_all',projection='DETAILED').result()\n",
    "cna_df = pd.DataFrame.from_dict([\n",
    "    # python magic that combines two dictionaries:\n",
    "    dict(\n",
    "        {k:getattr(cna,k) for k in dir(cna)},\n",
    "        **{k:getattr(cna.gene,k) for k in dir(cna.gene)}) \n",
    "    # create one item in the list for each mutation\n",
    "    for cna in cna_data\n",
    "])\n",
    "del cna_df['gene']\n",
    "cna_df['hugoGeneSymbol alteration'.split()].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an alterations dataframe that combines mutations, CNA and clinical data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            sampleId  patientId hugoGeneSymbol            alteration  \\\n",
      "0  P-0000004-T01-IM3  P-0000004           AKT1              Mutation   \n",
      "1  P-0000004-T01-IM3  P-0000004            ALK              Mutation   \n",
      "2  P-0000004-T01-IM3  P-0000004          BRCA2         Amplification   \n",
      "3  P-0000004-T01-IM3  P-0000004           FLT4  Multiple Alterations   \n",
      "4  P-0000004-T01-IM3  P-0000004          KMT2C              Mutation   \n",
      "\n",
      "     CANCER_TYPE              CANCER_TYPE_DETAILED DNA_INPUT  \\\n",
      "0  Breast Cancer  Breast Invasive Ductal Carcinoma       250   \n",
      "1  Breast Cancer  Breast Invasive Ductal Carcinoma       250   \n",
      "2  Breast Cancer  Breast Invasive Ductal Carcinoma       250   \n",
      "3  Breast Cancer  Breast Invasive Ductal Carcinoma       250   \n",
      "4  Breast Cancer  Breast Invasive Ductal Carcinoma       250   \n",
      "\n",
      "  FRACTION_GENOME_ALTERED MATCHED_STATUS METASTATIC_SITE  ... PRIMARY_SITE  \\\n",
      "0                  0.2782        Matched             NaN  ...       Breast   \n",
      "1                  0.2782        Matched             NaN  ...       Breast   \n",
      "2                  0.2782        Matched             NaN  ...       Breast   \n",
      "3                  0.2782        Matched             NaN  ...       Breast   \n",
      "4                  0.2782        Matched             NaN  ...       Breast   \n",
      "\n",
      "  SAMPLE_CLASS SAMPLE_COLLECTION_SOURCE SAMPLE_COVERAGE SAMPLE_TYPE  \\\n",
      "0        Tumor                  Outside             428     Primary   \n",
      "1        Tumor                  Outside             428     Primary   \n",
      "2        Tumor                  Outside             428     Primary   \n",
      "3        Tumor                  Outside             428     Primary   \n",
      "4        Tumor                  Outside             428     Primary   \n",
      "\n",
      "  SOMATIC_STATUS SPECIMEN_PRESERVATION_TYPE SPECIMEN_TYPE TMB_NONSYNONYMOUS  \\\n",
      "0        Matched                       FFPE        Biopsy       17.74648481   \n",
      "1        Matched                       FFPE        Biopsy       17.74648481   \n",
      "2        Matched                       FFPE        Biopsy       17.74648481   \n",
      "3        Matched                       FFPE        Biopsy       17.74648481   \n",
      "4        Matched                       FFPE        Biopsy       17.74648481   \n",
      "\n",
      "  TUMOR_PURITY  \n",
      "0           50  \n",
      "1           50  \n",
      "2           50  \n",
      "3           50  \n",
      "4           50  \n",
      "\n",
      "[5 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "# First, we need to add a column to each DataFrame that indicates the type of alteration\n",
    "mdf['alteration'] = 'Mutation'\n",
    "cna_df['alteration'] = cna_df['alteration'].map({-2: 'Deep Deletion', 2: 'Amplification'})\n",
    "\n",
    "# Concatenate the two DataFrames\n",
    "alterations_df = pd.concat([mdf, cna_df])\n",
    "\n",
    "# Group by patient and gene and count the number of alterations\n",
    "alterations_df = alterations_df.groupby(['sampleId','patientId', 'hugoGeneSymbol'])['alteration'].apply(list).reset_index()\n",
    "\n",
    "# Create a new column that indicates whether there are multiple alterations\n",
    "alterations_df['alteration'] = alterations_df['alteration'].apply(lambda x: 'Multiple Alterations' if len(x) > 1 else x[0])\n",
    "\n",
    "alterations_df = alterations_df.merge(cdf, on='sampleId', how='left')\n",
    "\n",
    "print(alterations_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then make the stacked barchart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "\n",
    "gene = 'ERBB2'\n",
    "colors = {\n",
    "    'Mutation': '#008000',  # rgb(0, 128, 0)\n",
    "    'Amplification': '#FF0000',  # rgb(255, 0, 0)\n",
    "    'Multiple Alterations': '#666666',  # rgb(102, 102, 102)\n",
    "    'Deep Deletion': '#0000FF'  # rgb(0, 0, 255)\n",
    "}\n",
    "order = ['Multiple Alterations', 'Deep Deletion', 'Amplification', 'Mutation']\n",
    "\n",
    "# Count the number of patients for each cancer type and alteration\n",
    "counts_df = alterations_df[alterations_df['hugoGeneSymbol'] == gene].groupby(['CANCER_TYPE', 'alteration'])['patientId'].nunique().unstack().fillna(0)\n",
    "\n",
    "# Ensure that the index of cancer_type_num_patients matches the index of counts_df\n",
    "cancer_type_num_patients = cancer_type_num_patients.reindex(counts_df.index)\n",
    "\n",
    "# Divide counts_df by cancer_type_num_patients to get alteration frequencies\n",
    "alteration_frequencies = counts_df.div(cancer_type_num_patients, axis='index') * 100.0\n",
    "\n",
    "# Sort by frequencies and select top 10\n",
    "alteration_frequencies_sorted = alteration_frequencies.loc[alteration_frequencies.sum(axis=1).sort_values(ascending=False).index[:20]]\n",
    "alteration_frequencies_sorted = alteration_frequencies_sorted[order]\n",
    "\n",
    "# Plot\n",
    "ax = alteration_frequencies_sorted.plot(kind='bar', stacked=True, color=[colors[col] for col in alteration_frequencies_sorted.columns])\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.xlabel('')\n",
    "plt.title('{} Alteration Frequency per Cancer Type'.format(gene))\n",
    "\n",
    "# Reorder the legend\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "order_index = [labels.index(alteration) for alteration in reversed(order)]\n",
    "ax.legend([handles[idx] for idx in order_index],[labels[idx] for idx in order_index])\n",
    "\n",
    "# Despine\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Add percentage sign to y ticks\n",
    "formatter = FuncFormatter(lambda y, pos: \"%d%%\" % (y))\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "# Add horizontal grid lines\n",
    "ax.yaxis.grid(True, linestyle='--', linewidth=0.5, color='lightgray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genome Nexus API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Genome Nexus](https://www.genomenexus.org) is a web service that aggregates all cancer related information about a particular mutation. Similarly to cBioPortal it provides a REST API following the [Swagger / OpenAPI specification](https://swagger.io/specification/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SwaggerClient(https://www.genomenexus.org/)\n"
     ]
    }
   ],
   "source": [
    "from bravado.client import SwaggerClient\n",
    "\n",
    "gn = SwaggerClient.from_url('https://www.genomenexus.org/v2/api-docs',\n",
    "                            config={\"validate_requests\":False,\n",
    "                                    \"validate_responses\":False,\n",
    "                                    \"validate_swagger_spec\":False})\n",
    "print(gn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To look up annotations for a single variant, one can use the following endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "variant = gn.annotation_controller.fetchVariantAnnotationByGenomicLocationGET(\n",
    "    genomicLocation='7,140453136,140453136,A,T',\n",
    "    # adds extra annotation resources, not included in default response:\n",
    "    fields='hotspots mutation_assessor annotation_summary'.split()\n",
    ").result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see a lot of information is provided for that particular variant if you type tab after `variant.`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "variant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will focus on the hotspot annotation and ignore the others. [Cancer hotspots](https://www.cancerhotspots.org/) is a popular web resource  which indicates whether particular variants have been found to be recurrently mutated in large scale cancer genomics data.\n",
    "\n",
    "The example variant above is a hotspot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HotspotAnnotation(annotation=[[Hotspot(hugoSymbol='BRAF', inframeCount=0, missenseCount=897, residue='V600', spliceCount=0, transcriptId='ENST00000288602', truncatingCount=0, tumorCount=897, type='single residue'), Hotspot(hugoSymbol='BRAF', inframeCount=None, missenseCount=None, residue='V600', spliceCount=None, transcriptId='ENST00000288602', truncatingCount=None, tumorCount=545, type='3d')], [], [], []], license='https://opendatacommons.org/licenses/odbl/1.0/')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variant.hotspots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many hotspot mutations there are in the Cholangiocarcinoma (TCGA, PanCancer Atlas) study with study id `chol_tcga_pan_can_atlas_2018` from the cBioPortal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 776 ms, sys: 96.9 ms, total: 873 ms\n",
      "Wall time: 1.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "cbioportal = SwaggerClient.from_url('https://www.cbioportal.org/api/api-docs',\n",
    "                                config={\"validate_requests\":False,\"validate_responses\":False})\n",
    "\n",
    "mutations = cbioportal.Mutations.getMutationsInMolecularProfileBySampleListIdUsingGET(\n",
    "    molecularProfileId='chol_tcga_pan_can_atlas_2018_mutations',\n",
    "    sampleListId='chol_tcga_pan_can_atlas_2018_all',\n",
    "    projection='DETAILED'\n",
    ").result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the results to a dataframe again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "mdf = pd.DataFrame.from_dict([\n",
    "    # python magic that combines two dictionaries:\n",
    "    dict(\n",
    "        {k:getattr(m,k) for k in dir(m)},\n",
    "        **{k:getattr(m.gene,k) for k in dir(m.gene)}) \n",
    "    # create one item in the list for each mutation\n",
    "    for m in mutations\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then get only the unique mutations, to avoid calling the web service with the same variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants = mdf['chr startPosition endPosition referenceAllele variantAllele'.split()]\\\n",
    "    .drop_duplicates()\\\n",
    "    .dropna(how='any',axis=0)\\\n",
    "    .reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert them to input that genome nexus will understand:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>startPosition</th>\n",
       "      <th>endPosition</th>\n",
       "      <th>referenceAllele</th>\n",
       "      <th>variantAllele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>51921499</td>\n",
       "      <td>51921499</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>167675119</td>\n",
       "      <td>167675119</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>142960620</td>\n",
       "      <td>142960620</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>65373425</td>\n",
       "      <td>65373425</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>57247159</td>\n",
       "      <td>57247159</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>7</td>\n",
       "      <td>151935871</td>\n",
       "      <td>151935871</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>1</td>\n",
       "      <td>27105848</td>\n",
       "      <td>27105852</td>\n",
       "      <td>ATGAT</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>9</td>\n",
       "      <td>125752410</td>\n",
       "      <td>125752410</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>9</td>\n",
       "      <td>125752411</td>\n",
       "      <td>125752411</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>5</td>\n",
       "      <td>55147437</td>\n",
       "      <td>55147440</td>\n",
       "      <td>TGTC</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1968 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chr  startPosition  endPosition referenceAllele variantAllele\n",
       "0      6       51921499     51921499               G             A\n",
       "1      5      167675119    167675119               C             T\n",
       "2      7      142960620    142960620               C             T\n",
       "3     11       65373425     65373425               C             G\n",
       "4     17       57247159     57247159               G             A\n",
       "...   ..            ...          ...             ...           ...\n",
       "1963   7      151935871    151935871               C             A\n",
       "1964   1       27105848     27105852           ATGAT             -\n",
       "1965   9      125752410    125752410               G             C\n",
       "1966   9      125752411    125752411               C             T\n",
       "1967   5       55147437     55147440            TGTC             -\n",
       "\n",
       "[1968 rows x 5 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants = variants.rename(columns={'chr':'chromosome','startPosition':'start','endPosition':'end'})\\\n",
    "    .to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1968 mutations left to annotate\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} mutations left to annotate\".format(len(variants)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annotate them with genome nexus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.47 s, sys: 99 ms, total: 1.57 s\n",
      "Wall time: 6.88 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "variants_annotated = gn.annotation_controller.fetchVariantAnnotationByGenomicLocationPOST(\n",
    "    genomicLocations=variants,\n",
    "    fields='hotspots annotation_summary'.split()\n",
    ").result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index the variants to make it easier to query them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "gn_dict = {\n",
    "    \"{},{},{},{},{}\".format(\n",
    "        v.annotation_summary.genomicLocation.chromosome,\n",
    "        v.annotation_summary.genomicLocation.start,\n",
    "        v.annotation_summary.genomicLocation.end,\n",
    "        v.annotation_summary.genomicLocation.referenceAllele,\n",
    "        v.annotation_summary.genomicLocation.variantAllele)\n",
    "        :\n",
    "        v for v in variants_annotated\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a new column to indicate whether something is a hotspot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_hotspot(x):\n",
    "    \"\"\"TODO: Current structure for hotspots in Genome Nexus is a little funky.\n",
    "    Need to check whether all lists in the annotation field are empty.\"\"\"\n",
    "    if x:\n",
    "        return sum([len(a) for a in x.hotspots.annotation]) > 0\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "def create_dict_query_key(x):\n",
    "    return \"{},{},{},{},{}\".format(\n",
    "        x.chr, x.startPosition, x.endPosition, x.referenceAllele, x.variantAllele\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdf['is_hotspot'] = mdf.apply(lambda x: is_hotspot(gn_dict.get(create_dict_query_key(x), None)), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context('notebook')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hotspots in Cholangiocarcinoma (TCGA, PanCancer Atlas)')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdf.groupby('hugoGeneSymbol').is_hotspot.sum().sort_values(ascending=False).head(10).plot(kind='bar')\n",
    "\n",
    "sns.despine(trim=False)\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation=300)\n",
    "plt.ylabel('Number of non-unique hotspots',labelpad=20)\n",
    "plt.title('Hotspots in Cholangiocarcinoma (TCGA, PanCancer Atlas)',pad=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OncoKB API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[OncoKB](https://oncokb.org) is is a precision oncology knowledge base and contains information about the effects and treatment implications of specific cancer gene alterations. Similarly to cBioPortal and Genome Nexus it provides a REST API following the [Swagger / OpenAPI specification](https://swagger.io/specification/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SwaggerClient(https://www.oncokb.org:443/api/v1)\n"
     ]
    }
   ],
   "source": [
    "oncokb = SwaggerClient.from_url('https://www.oncokb.org/api/v1/v2/api-docs',\n",
    "                            config={\"validate_requests\":False,\n",
    "                                    \"validate_responses\":False,\n",
    "                                    \"validate_swagger_spec\":False})\n",
    "print(oncokb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To look up annotations for a variant, one can use the following endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "variant = oncokb.Annotations.annotateMutationsByGenomicChangeGetUsingGET(\n",
    "    genomicLocation='7,140453136,140453136,A,T',\n",
    ").result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see a lot of information is provided for that particular variant if you type tab after `variant.`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The BRAF V600E mutation is known to be oncogenic.'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For instance we can see the summary information about it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The BRAF V600E mutation is known to be oncogenic.'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variant.variantSummary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look up this variant on the OncoKB website: https://www.oncokb.org/gene/BRAF/V600E. You can see that there are various combinations of drugs and their level of evidence listed. This is a classification system for indicating how much we know about whether or not a patient might respond to a particular treatment. Please see https://www.oncokb.org/levels for more information about the levels of evidence for therapeutic biomarkers.\n",
    "\n",
    "We can use the same `variants` we pulled from cBioPortal in the previous section to figure out the highest level of each variant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 363 ms, sys: 16.4 ms, total: 379 ms\n",
      "Wall time: 9.89 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "variants_annotated = oncokb.Annotations.annotateMutationsByGenomicChangePostUsingPOST(\n",
    "    body=[\n",
    "        {\"genomicLocation\":\"{chromosome},{start},{end},{referenceAllele},{variantAllele}\".format(**v)} \n",
    "        for v in variants\n",
    "    ],\n",
    ").result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count the highes level for each variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts_per_level = Counter([va.highestSensitiveLevel for va in variants_annotated if va.highestSensitiveLevel])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(counts_per_level,index=[0]).plot(kind='bar', colors=['#4D8834','#2E2E2C','#753579'])\n",
    "plt.xticks([])\n",
    "plt.ylabel('Number of variants')\n",
    "plt.title('Actionable variants in chol_tcga_pan_can_atlas_2018')\n",
    "sns.despine()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}