3882 lines (3881 with data), 532.9 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt \n",
"import numpy as np \n",
"from scipy.stats import zscore\n",
"import seaborn as sns\n",
"import sys,os\n",
"import gzip\n",
"import ftplib\n",
"import re\n",
"#pd.options.mode.chained_assignment = None # default='warn'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clinical trial cohorts profiled with microarrays\n",
"https://support.bioconductor.org/p/36041/ - no way to access phenoData without loading the whole dataset. \n",
"Therefore we download _series_matrix.txt.gz and parse its header. \n",
"Alternative way could be applying getGEO() followed by phenoData(), but this also means downloading the whole dataset. \n",
"\n",
"# PDX \n",
"RECIST Response Categories\n",
"\n",
"# TCGA\n",
"RECIST Response Categories\n",
"\n",
"# GDSC \n",
"- binary \n",
"- continious \n",
"\n",
"# ToDo:\n",
" - python wrapper for getGEO function\n",
" - automatic download of supplementary files \n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"nine_drugs = ['Docetaxel', 'Cisplatin', 'Erlotinib', 'Bortezomib','5-Fluorouracil',\n",
" 'Tamoxifen', 'Cetuximab', 'Paclitaxel', 'Gemcitabine']\n",
"\n",
"EGFRi_drugs = ['Cetuximab', 'Panitumumab','Erlotinib','Pelitinib','Gefitinib','Lapatinib','Afatinib','ZD-6474']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"root_dir = \"/home/olya/SFU/Hossein/v2/\"\n",
"tmp_dir = \"/home/olya/SFU/Hossein/arrays/annotations/\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# create folders for training,testing and pre-training in the root_dir \n",
"for folder in [\"preprocessed/\",\"preprocessed/annotations/\"]:\n",
" if not os.path.exists(root_dir+\"/\"+folder):\n",
" os.makedirs(root_dir+\"/\"+folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clinical trial cohorts\n",
"### GSE6434 - pre-treatmen expression of breast tumours from 24 patients with assessed tumour response to neoadjuvant docetaxel\n",
"consists of two subsets GSE349 and GSE350 comprising of resistant (14) and sensitive (10) patients respectively."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Docetaxel R: 14 S: 10\n"
]
},
{
"data": {
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" }\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>drug</th>\n",
" <th>response</th>\n",
" <th>response_original</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sample_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GSM4901</th>\n",
" <td>Docetaxel</td>\n",
" <td>R</td>\n",
" <td>residual tumor of 25% or greater remaining volume</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM4902</th>\n",
" <td>Docetaxel</td>\n",
" <td>R</td>\n",
" <td>residual tumor of 25% or greater remaining volume</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM4903</th>\n",
" <td>Docetaxel</td>\n",
" <td>S</td>\n",
" <td>less than 25% residual tumor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM4904</th>\n",
" <td>Docetaxel</td>\n",
" <td>R</td>\n",
" <td>residual tumor of 25% or greater remaining volume</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM4905</th>\n",
" <td>Docetaxel</td>\n",
" <td>R</td>\n",
" <td>residual tumor of 25% or greater remaining volume</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drug response \\\n",
"sample_name \n",
"GSM4901 Docetaxel R \n",
"GSM4902 Docetaxel R \n",
"GSM4903 Docetaxel S \n",
"GSM4904 Docetaxel R \n",
"GSM4905 Docetaxel R \n",
"\n",
" response_original \n",
"sample_name \n",
"GSM4901 residual tumor of 25% or greater remaining volume \n",
"GSM4902 residual tumor of 25% or greater remaining volume \n",
"GSM4903 less than 25% residual tumor \n",
"GSM4904 residual tumor of 25% or greater remaining volume \n",
"GSM4905 residual tumor of 25% or greater remaining volume "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"resistans_GSE349 = [\"GSM4901\",\"GSM4902\",\"GSM4904\",\"GSM4905\",\"GSM4906\",\"GSM4909\",\"GSM4910\",\n",
" \"GSM4911\",\"GSM4912\",\"GSM4913\",\"GSM4916\",\"GSM4918\",\"GSM4922\",\"GSM4924\"]\n",
"sensitive_GSE350 = [\"GSM4903\",\"GSM4907\",\"GSM4908\",\"GSM4914\",\"GSM4915\",\"GSM4917\",\"GSM4919\",\n",
" \"GSM4920\",\"GSM4921\",\"GSM4923\"]\n",
"\n",
"responses_dict = {}\n",
"#print(\"R:\",len(resistans_GSE349 ),\"S:\",len(sensitive_GSE350))\n",
"drug = \"Docetaxel\"\n",
"for s in resistans_GSE349:\n",
" responses_dict[s] = {\"response\":\"R\",\"drug\":drug,\"response_original\":\"residual tumor of 25% or greater remaining volume\"} \n",
"for s in sensitive_GSE350:\n",
" responses_dict[s] = {\"response\":\"S\",\"drug\":drug,\"response_original\":\"less than 25% residual tumor\"} \n",
"df = pd.DataFrame.from_dict(responses_dict).T\n",
"df.index.name = \"sample_name\"\n",
"df.sort_values(by=\"sample_name\",inplace=True)\n",
"df.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE6434_response.\"+drug+\".tsv\",sep = \"\\t\")\n",
"print(drug,\"R:\",df[df[\"response\"]==\"R\"].shape[0],\"S:\",df[df[\"response\"]==\"S\"].shape[0])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GSE18864 - Pretreatment tumor samples from the clinical trial of cisplatin monotherapy in triple negative breast cancer \n",
"- download matrix file from ftp: \n",
"ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE18nnn/GSE18864/matrix/GSE18864_series_matrix.txt.gz\n",
"- parse header\n",
"- convert =Miller-Payne response into binary S/R :\n",
" - 5 >=Miller-Payne response > 1 → “Sensitive”;\n",
" - Miller-Payne response == 1 → “Resistant”;\n",
"Miller-Payne response scale is explained here: https://www.researchgate.net/publication/263296704_Correlation_of_clinico-pathologic_and_radiologic_parameters_of_response_to_neoadjuvant_chemotherapy_in_breast_cancer \n",
"- 51 samples without response excluded "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def download_GEO_matrix(fname,ftppath,destination=os.getcwd(),ftp='ftp.ncbi.nlm.nih.gov'):\n",
" ftp = ftplib.FTP(ftp) \n",
" ftp.login() \n",
" ftp.cwd(ftppath)\n",
" #ftp.retrlines('LIST')\n",
" file_handle = open(destination+\"/\"+fname, 'wb')\n",
" ftp.retrbinary('RETR '+fname, file_handle.write)\n",
" file_handle.close()\n",
" ftp.quit()\n",
" return destination+\"/\"+fname"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def millerPayne2RECIST(response):\n",
" try :\n",
" response = int(response)\n",
" except:\n",
" #print(response,file=sys.stderr)\n",
" return response\n",
" if response <= 1:\n",
" return \"R\"\n",
" elif 5 >= response > 1:\n",
" return \"S\"\n",
" else:\n",
" print(response,file=sys.stderr)\n",
" return None\n",
"\n",
"def read_matrix(fname,index = \"GSM\"):\n",
" df = {}\n",
" with gzip.open(fname) as infile:\n",
" for line in infile.readlines():\n",
" if line.startswith(\"!\"):\n",
" line = line.rstrip().replace('\"','').split(\"\\t\")\n",
" line = map(lambda x : x.rstrip().lstrip(),line)\n",
" #print(line)\n",
" if line[0] == '!Sample_title':\n",
" df[\"title\"] = line[1:]\n",
" if line[0] == '!Sample_geo_accession':\n",
" df[\"GSM\"] = line[1:]\n",
" if line[0] == '!Sample_source_name_ch1':\n",
" df[\"source\"] = line[1:]\n",
" if line[0] == '!Sample_characteristics_ch1':\n",
" if \":\" in line[1]:\n",
" sep = \": \"\n",
" elif \"=\" in line[1]:\n",
" sep = \"= \"\n",
" else:\n",
" pass\n",
" field = line[1].split(sep)[0]\n",
" df[field] = map(lambda x : x.replace(field+sep,\"\").rstrip().lstrip(),line[1:])\n",
" df=pd.DataFrame.from_dict(df)\n",
" df.set_index(index,drop=True,inplace=True)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cisplatin R: 8 S: 16\n"
]
},
{
"data": {
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>drug</th>\n",
" <th>response</th>\n",
" <th>miller-payne response</th>\n",
" <th>grade</th>\n",
" <th>brca genotype</th>\n",
" <th>p53 status</th>\n",
" <th>er/pr/her2 status</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sample_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GSM467523</th>\n",
" <td>Cisplatin</td>\n",
" <td>S</td>\n",
" <td>3</td>\n",
" <td>III</td>\n",
" <td>WT</td>\n",
" <td>unknown</td>\n",
" <td>neg/neg/neg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM467524</th>\n",
" <td>Cisplatin</td>\n",
" <td>S</td>\n",
" <td>4</td>\n",
" <td>III</td>\n",
" <td>WT</td>\n",
" <td>MSM</td>\n",
" <td>neg/neg/neg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM467525</th>\n",
" <td>Cisplatin</td>\n",
" <td>S</td>\n",
" <td>5</td>\n",
" <td>III</td>\n",
" <td>WT</td>\n",
" <td>NSM</td>\n",
" <td>neg/neg/neg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM467526</th>\n",
" <td>Cisplatin</td>\n",
" <td>R</td>\n",
" <td>1</td>\n",
" <td>III</td>\n",
" <td>WT</td>\n",
" <td>MSM</td>\n",
" <td>neg/neg/neg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM467527</th>\n",
" <td>Cisplatin</td>\n",
" <td>S</td>\n",
" <td>5</td>\n",
" <td>III</td>\n",
" <td>BRCA1 germline mutation</td>\n",
" <td>MSM</td>\n",
" <td>neg/neg/neg</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drug response miller-payne response grade \\\n",
"sample_name \n",
"GSM467523 Cisplatin S 3 III \n",
"GSM467524 Cisplatin S 4 III \n",
"GSM467525 Cisplatin S 5 III \n",
"GSM467526 Cisplatin R 1 III \n",
"GSM467527 Cisplatin S 5 III \n",
"\n",
" brca genotype p53 status er/pr/her2 status \n",
"sample_name \n",
"GSM467523 WT unknown neg/neg/neg \n",
"GSM467524 WT MSM neg/neg/neg \n",
"GSM467525 WT NSM neg/neg/neg \n",
"GSM467526 WT MSM neg/neg/neg \n",
"GSM467527 BRCA1 germline mutation MSM neg/neg/neg "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fpath = download_GEO_matrix(\"GSE18864_series_matrix.txt.gz\",'/geo/series/GSE18nnn/GSE18864/matrix/'\n",
" ,destination=tmp_dir)\n",
"df = read_matrix(fpath)\n",
"os.remove(fpath)\n",
"df[\"response\"] = df[\"miller-payne response\"].apply(millerPayne2RECIST)\n",
"df = df[df[\"response\"] != \"n/a\"]\n",
"df[\"drug\"] = [\"Cisplatin\"]*df.shape[0]\n",
"df.index.name = \"sample_name\"\n",
"df.sort_values(by=\"sample_name\",inplace=True)\n",
"df = df[[\"drug\",\"response\"]+[\"miller-payne response\",\"grade\",\"brca genotype\",\"p53 status\",\"er/pr/her2 status\"]]\n",
"df.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE18864_response.\"+\"Cisplatin\"+\".tsv\",sep = \"\\t\")\n",
"print(\"Cisplatin\",\"R:\",df[df[\"response\"]==\"R\"].shape[0],\"S:\",df[df[\"response\"]==\"S\"].shape[0])\n",
"df.head(5) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GSE25065 - response and survival following neoadjuvant taxane-anthracycline chemotherapy in in HER2-negative invasive breast cancer.\n",
"- two taxanes studied: Taxol == Paclitaxel and Taxotere == Docetaxel\n",
"- download matrix file from ftp: \n",
"ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE25nnn/GSE25065/matrix/GSE25065_series_matrix.txt.gz\n",
"- parse header\n",
"- rename drugs and response groups as following:\n",
"\n",
"#### Response \"pathologic_response_pcr_rd\" and \"pathologic_response_rcb_class\"\n",
"- *pathologic_response_pcr_rd*\n",
" - pCR = pathologic complete response \n",
" - RD = residual disease\n",
"- *pathologic_response_rcb_class*,RCB = residual cancer burden: \n",
" - RCB-I is minimal RD\n",
" - RCB-II is moderate RD\n",
" - RCB-III extensive RD \n",
" - pCR is always RCB-0/I.\n",
"\n",
"In the paper, pCR+RCB-I group was compared with RCB-II/III and pCR+RCB-I/II with RCB-III\n",
"\n",
"I suggest :\n",
"* R = RCB-III\n",
"* S = pCR, RCB-I/II\n",
"* NA - 63 patients RD without RCB score (excluded)\n",
"\n",
"#### Drugs ('type_taxane')\n",
"Paclitaxel == Taxol; Docetaxel == Taxotere"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"drug_dict = {\"Taxol\":\"Paclitaxel\",\"Taxotere\":\"Docetaxel\"}\n",
"def RCB2response(row):\n",
" if row[\"pathologic_response_pcr_rd\"] == \"pCR\":\n",
" response = \"S\"\n",
" else:# row[\"pathologic_response_pcr_rd\"] == \"RD\":\n",
" if row[\"pathologic_response_rcb_class\"] == \"RCB-0/I\" or row[\"pathologic_response_rcb_class\"] == \"RCB-II\":\n",
" response = \"S\"\n",
" elif row[\"pathologic_response_rcb_class\"] == \"RCB-III\":\n",
" response = \"R\"\n",
" else: \n",
" #print(row[\"pathologic_response_pcr_rd\"],row[\"pathologic_response_rcb_class\"],file = sys.stderr)\n",
" response = \"NA\"\n",
" #else: \n",
" #print(row[\"pathologic_response_pcr_rd\"],row[\"pathologic_response_rcb_class\"],file = sys.stderr)\n",
" # response = \"NA\"\n",
" return response\n",
"fpath = download_GEO_matrix(\"GSE25065_series_matrix.txt.gz\",\n",
" '/geo/series/GSE25nnn/GSE25065/matrix/',destination=tmp_dir)\n",
"df = read_matrix(fpath)\n",
"os.remove(fpath)\n",
"df.index.name = \"sample_name\"\n",
"cols = [u'age_years', u'chemosensitivity_prediction', u'clinical_ajcc_stage',\n",
" u'clinical_nodal_status', u'clinical_t_stage', u'dlda30_prediction',\n",
" u'drfs_1_event_0_censored', u'drfs_even_time_years', u'er_status_ihc',\n",
" u'erbb2_status', u'esr1_status', u'ggi_class', u'grade', u'her2_status',\n",
" u'pam50_class', u'pr_status_ihc',u'rcb_0_i_prediction', u'sample id', \n",
" u'set_class', u'source', u'tissue', u'title']\n",
"#'type_taxane','pathologic_response_pcr_rd','pathologic_response_rcb_class'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" </tr>\n",
" <tr>\n",
" <th>pathologic_response_pcr_rd</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>NA</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RD</th>\n",
" <td>140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pCR</th>\n",
" <td>42</td>\n",
" </tr>\n",
" </tbody>\n",
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"</div>"
],
"text/plain": [
" title\n",
"pathologic_response_pcr_rd \n",
"NA 16\n",
"RD 140\n",
"pCR 42"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"title\",\"pathologic_response_pcr_rd\"]].groupby(\"pathologic_response_pcr_rd\").count()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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" <tr>\n",
" <th>pathologic_response_rcb_class</th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>NA</th>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RCB-0/I</th>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RCB-II</th>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RCB-III</th>\n",
" <td>31</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title\n",
"pathologic_response_rcb_class \n",
"NA 82\n",
"RCB-0/I 32\n",
"RCB-II 53\n",
"RCB-III 31"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"title\",\"pathologic_response_rcb_class\"]].groupby(\"pathologic_response_rcb_class\").count()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"samples with NA response 63\n",
"Paclitaxel R: 26 S: 58\n",
"Docetaxel R: 5 S: 46\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/olya/miniconda2/lib/python2.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>drug</th>\n",
" <th>response</th>\n",
" <th>pathologic_response_pcr_rd</th>\n",
" <th>pathologic_response_rcb_class</th>\n",
" <th>type_taxane</th>\n",
" <th>age_years</th>\n",
" <th>chemosensitivity_prediction</th>\n",
" <th>clinical_ajcc_stage</th>\n",
" <th>clinical_nodal_status</th>\n",
" <th>clinical_t_stage</th>\n",
" <th>...</th>\n",
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" <th>sample id</th>\n",
" <th>set_class</th>\n",
" <th>source</th>\n",
" <th>tissue</th>\n",
" <th>title</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sample_name</th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GSM615632</th>\n",
" <td>Docetaxel</td>\n",
" <td>S</td>\n",
" <td>pCR</td>\n",
" <td>NA</td>\n",
" <td>Taxotere</td>\n",
" <td>41.9</td>\n",
" <td>Rx Insensitive</td>\n",
" <td>IIB</td>\n",
" <td>N1</td>\n",
" <td>T2</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>P</td>\n",
" <td>Basal</td>\n",
" <td>P</td>\n",
" <td>RCB-0/I</td>\n",
" <td>5</td>\n",
" <td>SET-Low</td>\n",
" <td>USO</td>\n",
" <td>breast cancer tumor</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM615634</th>\n",
" <td>Docetaxel</td>\n",
" <td>S</td>\n",
" <td>pCR</td>\n",
" <td>NA</td>\n",
" <td>Taxotere</td>\n",
" <td>47.1</td>\n",
" <td>Rx Insensitive</td>\n",
" <td>IIA</td>\n",
" <td>N0</td>\n",
" <td>T2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>N</td>\n",
" <td>LumB</td>\n",
" <td>P</td>\n",
" <td>RCB-0/I</td>\n",
" <td>13</td>\n",
" <td>SET-Low</td>\n",
" <td>USO</td>\n",
" <td>breast cancer tumor</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM615635</th>\n",
" <td>Docetaxel</td>\n",
" <td>S</td>\n",
" <td>pCR</td>\n",
" <td>NA</td>\n",
" <td>Taxotere</td>\n",
" <td>43.8</td>\n",
" <td>Rx Insensitive</td>\n",
" <td>IIIC</td>\n",
" <td>N3</td>\n",
" <td>T3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>N</td>\n",
" <td>LumB</td>\n",
" <td>P</td>\n",
" <td>RCB-II/III</td>\n",
" <td>18</td>\n",
" <td>SET-Low</td>\n",
" <td>USO</td>\n",
" <td>breast cancer tumor</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM615636</th>\n",
" <td>Docetaxel</td>\n",
" <td>S</td>\n",
" <td>pCR</td>\n",
" <td>NA</td>\n",
" <td>Taxotere</td>\n",
" <td>50.8</td>\n",
" <td>Rx Sensitive</td>\n",
" <td>IIIA</td>\n",
" <td>N1</td>\n",
" <td>T3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>N</td>\n",
" <td>LumB</td>\n",
" <td>P</td>\n",
" <td>RCB-0/I</td>\n",
" <td>20</td>\n",
" <td>SET-Low</td>\n",
" <td>USO</td>\n",
" <td>breast cancer tumor</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM615637</th>\n",
" <td>Docetaxel</td>\n",
" <td>S</td>\n",
" <td>pCR</td>\n",
" <td>NA</td>\n",
" <td>Taxotere</td>\n",
" <td>34.1</td>\n",
" <td>Rx Insensitive</td>\n",
" <td>IIB</td>\n",
" <td>N0</td>\n",
" <td>T3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>N</td>\n",
" <td>Basal</td>\n",
" <td>N</td>\n",
" <td>RCB-0/I</td>\n",
" <td>23</td>\n",
" <td>SET-Low</td>\n",
" <td>USO</td>\n",
" <td>breast cancer tumor</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" drug response pathologic_response_pcr_rd \\\n",
"sample_name \n",
"GSM615632 Docetaxel S pCR \n",
"GSM615634 Docetaxel S pCR \n",
"GSM615635 Docetaxel S pCR \n",
"GSM615636 Docetaxel S pCR \n",
"GSM615637 Docetaxel S pCR \n",
"\n",
" pathologic_response_rcb_class type_taxane age_years \\\n",
"sample_name \n",
"GSM615632 NA Taxotere 41.9 \n",
"GSM615634 NA Taxotere 47.1 \n",
"GSM615635 NA Taxotere 43.8 \n",
"GSM615636 NA Taxotere 50.8 \n",
"GSM615637 NA Taxotere 34.1 \n",
"\n",
" chemosensitivity_prediction clinical_ajcc_stage \\\n",
"sample_name \n",
"GSM615632 Rx Insensitive IIB \n",
"GSM615634 Rx Insensitive IIA \n",
"GSM615635 Rx Insensitive IIIC \n",
"GSM615636 Rx Sensitive IIIA \n",
"GSM615637 Rx Insensitive IIB \n",
"\n",
" clinical_nodal_status clinical_t_stage ... grade her2_status \\\n",
"sample_name ... \n",
"GSM615632 N1 T2 ... 3 P \n",
"GSM615634 N0 T2 ... 2 N \n",
"GSM615635 N3 T3 ... 3 N \n",
"GSM615636 N1 T3 ... 3 N \n",
"GSM615637 N0 T3 ... 3 N \n",
"\n",
" pam50_class pr_status_ihc rcb_0_i_prediction sample id set_class \\\n",
"sample_name \n",
"GSM615632 Basal P RCB-0/I 5 SET-Low \n",
"GSM615634 LumB P RCB-0/I 13 SET-Low \n",
"GSM615635 LumB P RCB-II/III 18 SET-Low \n",
"GSM615636 LumB P RCB-0/I 20 SET-Low \n",
"GSM615637 Basal N RCB-0/I 23 SET-Low \n",
"\n",
" source tissue title \n",
"sample_name \n",
"GSM615632 USO breast cancer tumor 5 \n",
"GSM615634 USO breast cancer tumor 13 \n",
"GSM615635 USO breast cancer tumor 18 \n",
"GSM615636 USO breast cancer tumor 20 \n",
"GSM615637 USO breast cancer tumor 23 \n",
"\n",
"[5 rows x 27 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"response\"] = df.apply(lambda row : RCB2response(row),axis =1 )\n",
"# drop NA response\n",
"s = df[df[\"response\"] == \"NA\"].index.values\n",
"print(\"samples with NA response\", df[df[\"response\"] == \"NA\"].shape[0])\n",
"df = df[df[\"response\"] != \"NA\"]\n",
"df[\"drug\"] = df[\"type_taxane\"].apply(lambda x : drug_dict[x])\n",
"df = df[[\"drug\",\"response\",'pathologic_response_pcr_rd','pathologic_response_rcb_class','type_taxane'] +\n",
" cols]\n",
"for drug in list(set(df[\"drug\"].values)):\n",
" d = df[df[\"drug\"]==drug]\n",
" d.sort_values(by=\"sample_name\",inplace=True)\n",
" d.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE25065_response.\"+drug+\".tsv\",sep = \"\\t\")\n",
" print(drug,\"R:\",d[d[\"response\"]==\"R\"].shape[0],\"S:\",d[d[\"response\"]==\"S\"].shape[0])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GSE33072 - resistance to erlotinib (25) and PI3K pathway inhibitors (sorafenib, 37) in non-small cell lung cancer\n",
"According to the paper, the main outcome measure is disease control rate (DCR) at 8 weeks.\n",
"DC was assessed by radiologists and defined as a CR, PR or SD according to the RECIST.\n",
"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211116/#SD1\n",
"#### Sorafenib\n",
" - month-to-progression - 'pfsm (month):ch1'\n",
" - whether DC or not '8_week_disease control_1yes_0no:ch1' and '8-week disease control (1=yes, 0=no):ch1'\n",
" - sensitive if DC, resistant otherwise\n",
"\n",
"\n",
"#### Erlotinib\n",
" - whether DC or not - not available neither in GEO phenoData nor in publications\n",
" - month-to-progression - 'progression-free survival time (months):ch1'\n",
" \n",
"Geeleher 2014 et al. calcluate Pearson's $r$ between months-to-progression and predicted responses. \n",
"\n",
"DC at eight weeks was unavailable for erlotinib-treated patients; therefore we used months-to-progression (PFSM) to define resistant and sensitive patients. Since eight weeks is approximately 1.86 months, we assigned patients with months-to-progression < 1.86 as resistant and months-to-progression >= 1.86 as sensitive. This is an uncertain assignment.\n",
"Erlotinib-treated patients had DC annotation and were assigned to sensitive if DC, and resistant otherwise. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/olya/miniconda2/lib/python2.7/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[key] = _infer_fill_value(value)\n",
"/home/olya/miniconda2/lib/python2.7/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sorafenib R: 16 S: 23\n"
]
},
{
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" <th>progression-free survival time (months):ch1</th>\n",
" <th>race:ch1</th>\n",
" <th>randomization_date:ch1</th>\n",
" <th>smoking_status:ch1</th>\n",
" <th>stage_at_diagnosis:ch1</th>\n",
" <th>transition/transversion:ch1</th>\n",
" <th>type kras aa change:ch1</th>\n",
" <th>type of kras mut:ch1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sample_name</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>GSM677317</th>\n",
" <td>Sorafenib</td>\n",
" <td>R</td>\n",
" <td>1.6756</td>\n",
" <td>sorafenib</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>LM118</td>\n",
" <td>GSM677317</td>\n",
" <td>Public on Jun 01 2012</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>White</td>\n",
" <td>2008-02-06</td>\n",
" <td>Former</td>\n",
" <td>IV</td>\n",
" <td>Transversion</td>\n",
" <td>VAL</td>\n",
" <td>GGT12GTT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM677322</th>\n",
" <td>Sorafenib</td>\n",
" <td>S</td>\n",
" <td>9.1663</td>\n",
" <td>sorafenib</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>LM227</td>\n",
" <td>GSM677322</td>\n",
" <td>Public on Jun 01 2012</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>White</td>\n",
" <td>2008-05-29</td>\n",
" <td>Former</td>\n",
" <td>IIIB</td>\n",
" <td>Transition</td>\n",
" <td>ASP</td>\n",
" <td>GGT12GAT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM677333</th>\n",
" <td>Sorafenib</td>\n",
" <td>S</td>\n",
" <td>2.7598</td>\n",
" <td>sorafenib</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>LM450</td>\n",
" <td>GSM677333</td>\n",
" <td>Public on Jun 01 2012</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>White</td>\n",
" <td>2009-05-13</td>\n",
" <td>Former</td>\n",
" <td>IV</td>\n",
" <td>Transversion</td>\n",
" <td>CYS</td>\n",
" <td>GGT12TGT</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 89 columns</p>\n",
"</div>"
],
"text/plain": [
" drug response pfsm (month):ch1 treatment:ch1 \\\n",
"sample_name \n",
"GSM677317 Sorafenib R 1.6756 sorafenib \n",
"GSM677322 Sorafenib S 9.1663 sorafenib \n",
"GSM677333 Sorafenib S 2.7598 sorafenib \n",
"\n",
" 8_week_disease control_1yes_0no:ch1 \\\n",
"sample_name \n",
"GSM677317 NaN \n",
"GSM677322 NaN \n",
"GSM677333 NaN \n",
"\n",
" 8-week disease control (1=yes, 0=no):ch1 \\\n",
"sample_name \n",
"GSM677317 0 \n",
"GSM677322 1 \n",
"GSM677333 1 \n",
"\n",
" pfsc (1=progressed; 0=not progressed):ch1 title geo_accession \\\n",
"sample_name \n",
"GSM677317 1 LM118 GSM677317 \n",
"GSM677322 1 LM227 GSM677322 \n",
"GSM677333 1 LM450 GSM677333 \n",
"\n",
" status ... prior_tx_for_mets:ch1 \\\n",
"sample_name ... \n",
"GSM677317 Public on Jun 01 2012 ... 3 \n",
"GSM677322 Public on Jun 01 2012 ... 2 \n",
"GSM677333 Public on Jun 01 2012 ... 1 \n",
"\n",
" progression-free survival status:ch1 \\\n",
"sample_name \n",
"GSM677317 NaN \n",
"GSM677322 NaN \n",
"GSM677333 NaN \n",
"\n",
" progression-free survival time (months):ch1 race:ch1 \\\n",
"sample_name \n",
"GSM677317 NaN White \n",
"GSM677322 NaN White \n",
"GSM677333 NaN White \n",
"\n",
" randomization_date:ch1 smoking_status:ch1 stage_at_diagnosis:ch1 \\\n",
"sample_name \n",
"GSM677317 2008-02-06 Former IV \n",
"GSM677322 2008-05-29 Former IIIB \n",
"GSM677333 2009-05-13 Former IV \n",
"\n",
" transition/transversion:ch1 type kras aa change:ch1 \\\n",
"sample_name \n",
"GSM677317 Transversion VAL \n",
"GSM677322 Transition ASP \n",
"GSM677333 Transversion CYS \n",
"\n",
" type of kras mut:ch1 \n",
"sample_name \n",
"GSM677317 GGT12GTT \n",
"GSM677322 GGT12GAT \n",
"GSM677333 GGT12TGT \n",
"\n",
"[3 rows x 89 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#fpath = download_GEO_matrix(\"GSE33072_series_matrix.txt.gz\",\n",
"# '/geo/series/GSE33nnn/GSE33072/matrix/',destination=tmp_dir)\n",
"#df = read_matrix(fpath)\n",
"# annotation in GEO is messed up, therefore read phenoData with R GEOquery\n",
"df = pd.read_csv(\"GSE33072_annotation.tsv\",sep=\"\\t\",index_col=0)\n",
"df = df.dropna(subset=[\"treatment:ch1\"])\n",
"df_e = df[df[\"treatment:ch1\"] == \"erlotinib\"]\n",
"df_s = df[df[\"treatment:ch1\"] == \"sorafenib\"]\n",
"#print(\"Erlotinib:\",df_e.shape[0],\"Sorafenib:\",df_s.shape[0])\n",
"df_s.loc[:,\"drug\"] = \"Sorafenib\"\n",
"df_s.loc[:,\"response\"] = \"R\"\n",
"df_s.loc[df_s[\"8-week disease control (1=yes, 0=no):ch1\"] == 1.0, \"response\"] = \"S\"\n",
"cols_order = [\"drug\",\"response\",'pfsm (month):ch1',\"treatment:ch1\",'8_week_disease control_1yes_0no:ch1',\n",
" '8-week disease control (1=yes, 0=no):ch1',\n",
" 'pfsc (1=progressed; 0=not progressed):ch1']\n",
"df_s = df_s[list(cols_order)+list(df_s.columns.values)]\n",
"df_s = df_s.T.drop_duplicates().T\n",
"df_s = df_s.dropna(how=\"all\",axis=1)\n",
"df_s.index.name = \"sample_name\"\n",
"df_s.sort_values(by=\"sample_name\",inplace=True)\n",
"print(\"Sorafenib\",\"R:\",df_s.loc[df_s[\"response\"]==\"R\",:].shape[0],\"S:\",\n",
" df_s.loc[df_s[\"response\"]==\"S\",:].shape[0])\n",
"df_s.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE33072_response.\"+\"Sorafenib\"+\".tsv\",sep = \"\\t\")\n",
"df_s.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Erlotinib R: 12 S: 13\n"
]
},
{
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" }\n",
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" .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>drug</th>\n",
" <th>response</th>\n",
" <th>progression-free survival time (months):ch1</th>\n",
" <th>treatment:ch1</th>\n",
" <th>progression-free survival status:ch1</th>\n",
" <th>title</th>\n",
" <th>geo_accession</th>\n",
" <th>status</th>\n",
" <th>submission_date</th>\n",
" <th>last_update_date</th>\n",
" <th>...</th>\n",
" <th>egfr index:ch1</th>\n",
" <th>egfr mutation:ch1</th>\n",
" <th>glyc_replaced_by_c_d_v_a:ch1</th>\n",
" <th>kras mutation:ch1</th>\n",
" <th>kras_mut_codon:ch1</th>\n",
" <th>kras_mut_iw:ch1</th>\n",
" <th>kras_mut_type:ch1</th>\n",
" <th>randomization date:ch1</th>\n",
" <th>transition_transversion:ch1</th>\n",
" <th>type_kras_aa_change:ch1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sample_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GSM677318</th>\n",
" <td>Erlotinib</td>\n",
" <td>S</td>\n",
" <td>3.2526</td>\n",
" <td>erlotinib</td>\n",
" <td>1</td>\n",
" <td>LM124</td>\n",
" <td>GSM677318</td>\n",
" <td>Public on Jun 01 2012</td>\n",
" <td>Feb 17 2011</td>\n",
" <td>Jun 01 2012</td>\n",
" <td>...</td>\n",
" <td>0.21</td>\n",
" <td>WT</td>\n",
" <td>C</td>\n",
" <td>Mutant</td>\n",
" <td>12</td>\n",
" <td>Yes</td>\n",
" <td>GGT12TGT</td>\n",
" <td>9/27/2007</td>\n",
" <td>Transversion</td>\n",
" <td>CYS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM677321</th>\n",
" <td>Erlotinib</td>\n",
" <td>S</td>\n",
" <td>2.037</td>\n",
" <td>erlotinib</td>\n",
" <td>1</td>\n",
" <td>LM218</td>\n",
" <td>GSM677321</td>\n",
" <td>Public on Jun 01 2012</td>\n",
" <td>Feb 17 2011</td>\n",
" <td>Jun 01 2012</td>\n",
" <td>...</td>\n",
" <td>1.05</td>\n",
" <td>WT</td>\n",
" <td>V</td>\n",
" <td>Mutant</td>\n",
" <td>12</td>\n",
" <td>Yes</td>\n",
" <td>GGT12GTT</td>\n",
" <td>4/17/2008</td>\n",
" <td>Transversion</td>\n",
" <td>VAL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM677326</th>\n",
" <td>Erlotinib</td>\n",
" <td>S</td>\n",
" <td>2.0698</td>\n",
" <td>erlotinib</td>\n",
" <td>1</td>\n",
" <td>LM237</td>\n",
" <td>GSM677326</td>\n",
" <td>Public on Jun 01 2012</td>\n",
" <td>Feb 17 2011</td>\n",
" <td>Jun 01 2012</td>\n",
" <td>...</td>\n",
" <td>-0.26</td>\n",
" <td>WT</td>\n",
" <td>V</td>\n",
" <td>Mutant</td>\n",
" <td>12</td>\n",
" <td>Yes</td>\n",
" <td>GGT12GTT</td>\n",
" <td>7/24/2008</td>\n",
" <td>Transversion</td>\n",
" <td>VAL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 67 columns</p>\n",
"</div>"
],
"text/plain": [
" drug response progression-free survival time (months):ch1 \\\n",
"sample_name \n",
"GSM677318 Erlotinib S 3.2526 \n",
"GSM677321 Erlotinib S 2.037 \n",
"GSM677326 Erlotinib S 2.0698 \n",
"\n",
" treatment:ch1 progression-free survival status:ch1 title \\\n",
"sample_name \n",
"GSM677318 erlotinib 1 LM124 \n",
"GSM677321 erlotinib 1 LM218 \n",
"GSM677326 erlotinib 1 LM237 \n",
"\n",
" geo_accession status submission_date \\\n",
"sample_name \n",
"GSM677318 GSM677318 Public on Jun 01 2012 Feb 17 2011 \n",
"GSM677321 GSM677321 Public on Jun 01 2012 Feb 17 2011 \n",
"GSM677326 GSM677326 Public on Jun 01 2012 Feb 17 2011 \n",
"\n",
" last_update_date ... egfr index:ch1 \\\n",
"sample_name ... \n",
"GSM677318 Jun 01 2012 ... 0.21 \n",
"GSM677321 Jun 01 2012 ... 1.05 \n",
"GSM677326 Jun 01 2012 ... -0.26 \n",
"\n",
" egfr mutation:ch1 glyc_replaced_by_c_d_v_a:ch1 kras mutation:ch1 \\\n",
"sample_name \n",
"GSM677318 WT C Mutant \n",
"GSM677321 WT V Mutant \n",
"GSM677326 WT V Mutant \n",
"\n",
" kras_mut_codon:ch1 kras_mut_iw:ch1 kras_mut_type:ch1 \\\n",
"sample_name \n",
"GSM677318 12 Yes GGT12TGT \n",
"GSM677321 12 Yes GGT12GTT \n",
"GSM677326 12 Yes GGT12GTT \n",
"\n",
" randomization date:ch1 transition_transversion:ch1 \\\n",
"sample_name \n",
"GSM677318 9/27/2007 Transversion \n",
"GSM677321 4/17/2008 Transversion \n",
"GSM677326 7/24/2008 Transversion \n",
"\n",
" type_kras_aa_change:ch1 \n",
"sample_name \n",
"GSM677318 CYS \n",
"GSM677321 VAL \n",
"GSM677326 VAL \n",
"\n",
"[3 rows x 67 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_e.loc[:,\"drug\"] = \"Erlotinib\"\n",
"df_e.loc[:,\"response\"] = \"S\"\n",
"df_e.loc[df_e['progression-free survival time (months):ch1']< 1.86, \"response\"] = \"R\"\n",
"df_e = df_e.dropna(how=\"all\",axis=1)\n",
"cols_order = [\"drug\",\"response\",'progression-free survival time (months):ch1',\"treatment:ch1\",\"treatment:ch1\",\n",
" 'progression-free survival status:ch1']\n",
"df_e = df_e[list(cols_order)+list(df_e.columns.values)]\n",
"df_e = df_e.T.drop_duplicates().T\n",
"print(\"Erlotinib\",\"R:\",df_e.loc[df_e[\"response\"]==\"R\",:].shape[0],\"S:\",\n",
" df_e.loc[df_e[\"response\"]==\"S\",:].shape[0])\n",
"df_e.index.name = \"sample_name\"\n",
"df_e.sort_values(by=\"sample_name\",inplace=True)\n",
"df_e.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE33072_response.\"+\"Erlotinib\"+\".tsv\",sep = \"\\t\")\n",
"df_e.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GSE9782 - response and survival with bortezomib compared to dexamethasone in patients with multiple myeloma\n",
" - Two arrays HG-U133A(GPL96) and HG-U133B(GPL97) for each patient\n",
" - \"characteristics_ch1.1\" - Dex - Dexamethasone (76), PS341 - Bortezomib (188) \n",
" - Response:\n",
" - \"characteristics_ch1.7\" - PGx_Response : complete response (CR), partial response (PR), minimal response (MR), no change (NC), or PD (progressive disease)\n",
"\n",
" [from 10.1182/blood-2006-09-044974]\n",
" - \"characteristics_ch1.8\" - PGx_Responder : R - responder, NR - non-responder\n",
"Samples with PGx_Response = IE and PGx_Responder = IE were excluded, because this group was not explined in the text"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HG-U133A: Dexamethasone - 70 \tBortezomib - 169\n",
"HG-U133B: Dexamethasone - 70 \tBortezomib - 169\n"
]
}
],
"source": [
"df_a = pd.read_csv(\"GSE9782-GPL96_annotation.tsv\",sep=\"\\t\",index_col=0)\n",
"df_a.index.name = \"sample_name\"\n",
"df_a.sort_values(by=\"sample_name\",inplace=True)\n",
"df_a = df_a.loc[df_a[\"characteristics_ch1.8\"] != \"PGx_Responder = IE\",]\n",
"df_a.loc[:,\"response_detailed\"] = df_a[\"characteristics_ch1.7\"].apply(lambda x: x.replace(\"PGx_Response = \",\"\"))\n",
"df_a.loc[df_a[\"characteristics_ch1.8\"]==\"PGx_Responder = NR\",\"response\"] = \"R\"\n",
"df_a.loc[df_a[\"characteristics_ch1.8\"]==\"PGx_Responder = R\",\"response\"] = \"S\"\n",
"df_a_dex = df_a[df_a[\"characteristics_ch1.1\"] == \"treatment = Dex\"]\n",
"df_a_dex .loc[:,\"drug\"] = \"Dexamethasone\"\n",
"df_a_bort = df_a[df_a[\"characteristics_ch1.1\"] == \"treatment = PS341\"]\n",
"df_a_bort.loc[:,\"drug\"] = \"Bortezomib\"\n",
"print(\"HG-U133A:\",\"Dexamethasone - \",df_a_dex.shape[0],\"\\tBortezomib - \",df_a_bort.shape[0])\n",
"\n",
"df_b = pd.read_csv(\"GSE9782-GPL97_annotation.tsv\",sep=\"\\t\",index_col=0)\n",
"df_b.index.name = \"sample_name\"\n",
"df_b.sort_values(by=\"sample_name\",inplace=True)\n",
"df_b = df_b.loc[df_b[\"characteristics_ch1.8\"] != \"PGx_Responder = IE\",]\n",
"df_b.loc[:,\"response_detailed\"] = df_b[\"characteristics_ch1.7\"].apply(lambda x: x.replace(\"PGx_Response = \",\"\"))\n",
"df_b.loc[df_b[\"characteristics_ch1.8\"]==\"PGx_Responder = NR\",\"response\"] = \"R\"\n",
"df_b.loc[df_b[\"characteristics_ch1.8\"]==\"PGx_Responder = R\",\"response\"] = \"S\"\n",
"df_b_dex = df_b[df_b[\"characteristics_ch1.1\"] == \"treatment = Dex\"]\n",
"df_b_dex .loc[:,\"drug\"] = \"Dexamethasone\"\n",
"df_b_bort = df_b[df_b[\"characteristics_ch1.1\"] == \"treatment = PS341\"]\n",
"df_b_bort.loc[:,\"drug\"] = \"Bortezomib\"\n",
"print(\"HG-U133B:\",\"Dexamethasone - \",df_b_dex.shape[0],\"\\tBortezomib - \",df_b_bort.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bortezomib - GPL96 R: 84 S: 85\n",
"Bortezomib - GPL97 R: 84 S: 85\n",
"Dexamethasone - GPL96 R: 42 S: 28\n",
"Dexamethasone - GPL97 R: 42 S: 28\n"
]
}
],
"source": [
"cols_order = [\"drug\",\"response\",\"response_detailed\",'title',u'characteristics_ch1.1',u'characteristics_ch1.7',u'characteristics_ch1.8'] \n",
"df_a_bort = df_a_bort[cols_order+list(df_a_bort.columns.values)].T.drop_duplicates().T\n",
"df_a_bort.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE9782-GPL96_response.\"+\"Bortezomib\"+\".tsv\",sep = \"\\t\")\n",
"print(\"Bortezomib - GPL96\",\"R:\",df_a_bort[df_a_bort[\"response\"]==\"R\"].shape[0],\n",
" \"S:\",df_a_bort[df_a_bort[\"response\"]==\"S\"].shape[0])\n",
"\n",
"df_b_bort = df_b_bort[cols_order+list(df_b_bort.columns.values)].T.drop_duplicates().T\n",
"df_b_bort.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE9782-GPL97_response.\"+\"Bortezomib\"+\".tsv\",sep = \"\\t\")\n",
"print(\"Bortezomib - GPL97\",\"R:\",df_b_bort[df_b_bort[\"response\"]==\"R\"].shape[0],\n",
" \"S:\",df_b_bort[df_b_bort[\"response\"]==\"S\"].shape[0])\n",
"\n",
"df_a_dex = df_a_dex[cols_order+list(df_a_dex.columns.values)].T.drop_duplicates().T\n",
"df_a_dex.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE9782-GPL96_response.\"+\"Dexamethasone\"+\".tsv\",sep = \"\\t\")\n",
"print(\"Dexamethasone - GPL96\",\"R:\",df_a_dex[df_a_dex[\"response\"]==\"R\"].shape[0],\n",
" \"S:\",df_a_dex[df_a_dex[\"response\"]==\"S\"].shape[0])\n",
"\n",
"df_b_dex = df_b_dex[cols_order+list(df_b_dex.columns.values)].T.drop_duplicates().T\n",
"df_b_dex.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GSE9782-GPL97_response.\"+\"Dexamethasone\"+\".tsv\",sep = \"\\t\")\n",
"print(\"Dexamethasone - GPL97\",\"R:\",df_b_dex[df_b_dex[\"response\"]==\"R\"].shape[0],\n",
" \"S:\",df_b_dex[df_b_dex[\"response\"]==\"S\"].shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PDX\n",
"Supplementary file nm.3954-S2.xlsx from https://www.nature.com/articles/nm.3954, tab \"PCT curve metrics\"\n",
"\n",
"- all combinational treatemnts were excluded\n",
"- records containing '-->' or '-->-->' signs in ResponseCategory were excluded; these records correspond non-stable response, e.g. PR --> PD means SD-->-->PD means\n",
"- we focus on 5 drugs: 'Cetuximab', 'Paclitaxel', 'Gemcitabine', '5-Fluorouracil', 'Erlotinib';\n",
" 'Tamoxifen' has no \"S\" xenografts\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4758, 11)\n",
"Combo drugs responses dropped: 1279\n",
"(3479, 11)\n"
]
},
{
"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>Model</th>\n",
" <th>Treatment</th>\n",
" <th>Treatment target</th>\n",
" <th>Treatment type</th>\n",
" <th>BestResponse</th>\n",
" <th>Day_BestResponse</th>\n",
" <th>BestAvgResponse</th>\n",
" <th>Day_BestAvgResponse</th>\n",
" <th>TimeToDouble</th>\n",
" <th>Day_Last</th>\n",
" <th>ResponseCategory</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>X-007</td>\n",
" <td>BGJ398</td>\n",
" <td>FGFR</td>\n",
" <td>single</td>\n",
" <td>396.5</td>\n",
" <td>11</td>\n",
" <td>220.475000</td>\n",
" <td>11</td>\n",
" <td>4.000000</td>\n",
" <td>11</td>\n",
" <td>PD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>X-007</td>\n",
" <td>BKM120</td>\n",
" <td>PIK3CA,PIK3CB,PIK3CG,PIK3CD,panPI3K</td>\n",
" <td>single</td>\n",
" <td>189.1</td>\n",
" <td>14</td>\n",
" <td>77.050000</td>\n",
" <td>11</td>\n",
" <td>6.207547</td>\n",
" <td>14</td>\n",
" <td>PD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>X-007</td>\n",
" <td>BYL719</td>\n",
" <td>PIK3CA</td>\n",
" <td>single</td>\n",
" <td>303.7</td>\n",
" <td>11</td>\n",
" <td>196.175000</td>\n",
" <td>11</td>\n",
" <td>4.000000</td>\n",
" <td>11</td>\n",
" <td>PD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>X-007</td>\n",
" <td>CLR457</td>\n",
" <td>PIK3CA,PIK3CB,PIK3CG,PIK3CD,panPI3K</td>\n",
" <td>single</td>\n",
" <td>25.0</td>\n",
" <td>16</td>\n",
" <td>26.533333</td>\n",
" <td>16</td>\n",
" <td>36.835000</td>\n",
" <td>37</td>\n",
" <td>SD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>X-007</td>\n",
" <td>HDM201</td>\n",
" <td>MDM2</td>\n",
" <td>single</td>\n",
" <td>330.8</td>\n",
" <td>11</td>\n",
" <td>182.750000</td>\n",
" <td>11</td>\n",
" <td>4.000000</td>\n",
" <td>11</td>\n",
" <td>PD</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Treatment Treatment target Treatment type \\\n",
"0 X-007 BGJ398 FGFR single \n",
"1 X-007 BKM120 PIK3CA,PIK3CB,PIK3CG,PIK3CD,panPI3K single \n",
"2 X-007 BYL719 PIK3CA single \n",
"5 X-007 CLR457 PIK3CA,PIK3CB,PIK3CG,PIK3CD,panPI3K single \n",
"6 X-007 HDM201 MDM2 single \n",
"\n",
" BestResponse Day_BestResponse BestAvgResponse Day_BestAvgResponse \\\n",
"0 396.5 11 220.475000 11 \n",
"1 189.1 14 77.050000 11 \n",
"2 303.7 11 196.175000 11 \n",
"5 25.0 16 26.533333 16 \n",
"6 330.8 11 182.750000 11 \n",
"\n",
" TimeToDouble Day_Last ResponseCategory \n",
"0 4.000000 11 PD \n",
"1 6.207547 14 PD \n",
"2 4.000000 11 PD \n",
"5 36.835000 37 SD \n",
"6 4.000000 11 PD "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# add download of annotation file \n",
"df = pd.read_excel(\"/home/olya/SFU/Hossein/PDX/nm.3954-S2.xlsx\",\"PCT curve metrics\")\n",
"print(df.shape)\n",
"df.drop_duplicates(inplace=True)\n",
"print(\"Combo drugs responses dropped:\",df.loc[df[\"Treatment type\"]==\"combo\",:].shape[0])\n",
"df = df.loc[df[\"Treatment type\"]==\"single\",:]\n",
"print(df.shape)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#df[[\"Model\",\"Treatment\"]].groupby(\"Treatment\").size()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[[\"Model\",\"ResponseCategory\"]].groupby(\"ResponseCategory\").size()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"drug_dict = {\"5FU\":\"5-Fluorouracil\",\"erlotinib\":\"Erlotinib\",\"cetuximab\":\"Cetuximab\",\n",
" \"gemcitabine-50mpk\":\"Gemcitabine\",\"paclitaxel\":\"Paclitaxel\"}\n",
"response_dict = {\"CR\":\"S\",\"PR\":\"S\",\"SD\":\"R\",\"PD\":\"R\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = df.loc[df[\"Treatment\"].isin(drug_dict.keys()),:]\n",
"print(\"Records for drugs\",drug_dict.values(),df.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[[\"Model\",\"Treatment\"]].groupby(\"Treatment\").size()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = df.loc[~df[\"ResponseCategory\"].str.contains(\"-->\"),:]\n",
"df.groupby(\"ResponseCategory\").size()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df.loc[:,\"drug\"] = df[\"Treatment\"].apply(lambda x : drug_dict[x])\n",
"df.loc[:,\"response\"] = df[\"ResponseCategory\"].apply(lambda x : response_dict[x])\n",
"df = df[[\"Model\",\"drug\",\"response\",\"ResponseCategory\",\"Treatment\",\"Treatment target\",\n",
" \"Treatment type\",\"BestResponse\",\"Day_BestResponse\",\"BestAvgResponse\",\"Day_BestAvgResponse\",\"TimeToDouble\",\"Day_Last\"]]\n",
"\n",
"for drug in drug_dict.values():\n",
" d = df.loc[df[\"drug\"]==drug,:]\n",
" d.set_index(\"Model\",inplace = True,drop=True)\n",
" d.index.name = \"sample_name\"\n",
" d.sort_values(by=\"sample_name\",inplace=True)\n",
" d.to_csv(root_dir+\"/preprocessed/annotations/\"+\"PDX_response.\"+drug+\".tsv\",sep = \"\\t\")\n",
" print(drug,\"R:\",d[d[\"response\"]==\"R\"].shape[0],\n",
" \"S:\",d[d[\"response\"]==\"S\"].shape[0])\n",
"d.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TCGA \n",
"\n",
"Ding et al. 2016, Supplementary tables , tab \"Table S2\"\n",
"\n",
"\"bcr_patient_barcode\" matches with first 12 symbols in sample barcore. One patient in TCGA may have more than one tumor sample and even one or several normal samples. \n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"response_dict = {'Clinical Progressive Disease':\"R\",'Complete Response':\"S\",\n",
" 'Partial Response':\"S\",'Stable Disease':\"R\"}"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2569, 16)\n"
]
},
{
"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>1</th>\n",
" <th>bcr_patient_barcode</th>\n",
" <th>cohort</th>\n",
" <th>drug</th>\n",
" <th>response</th>\n",
" <th>measure_of_response</th>\n",
" <th>days_to_drug_therapy_start</th>\n",
" <th>days_to_drug_therapy_end</th>\n",
" <th>DrugBank ID</th>\n",
" <th>days_to_initial_pathologic_diagnosis</th>\n",
" <th>method_of_sample_procurement</th>\n",
" <th>days_to_sample_procurement</th>\n",
" <th>days_to_new_tumor_event_after_initial_treatment</th>\n",
" <th>additional_pharmaceutical_therapy</th>\n",
" <th>new_tumor_event_additional_surgery_procedure</th>\n",
" <th>history_of_neoadjuvant_treatment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>TCGA-OR-A5JM</td>\n",
" <td>ACC</td>\n",
" <td>Sunitinib</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>378</td>\n",
" <td>439</td>\n",
" <td>DB01268</td>\n",
" <td>0</td>\n",
" <td>Surgical Resection</td>\n",
" <td>1</td>\n",
" <td>72</td>\n",
" <td>YES</td>\n",
" <td>NO</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>TCGA-OR-A5JM</td>\n",
" <td>ACC</td>\n",
" <td>Ketoconazole</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>378</td>\n",
" <td>439</td>\n",
" <td>DB01026</td>\n",
" <td>0</td>\n",
" <td>Surgical Resection</td>\n",
" <td>1</td>\n",
" <td>72</td>\n",
" <td>YES</td>\n",
" <td>NO</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>TCGA-OU-A5PI</td>\n",
" <td>ACC</td>\n",
" <td>Etoposide</td>\n",
" <td>R</td>\n",
" <td>Stable Disease</td>\n",
" <td>69</td>\n",
" <td>239</td>\n",
" <td>DB00773</td>\n",
" <td>0</td>\n",
" <td>Surgical Resection</td>\n",
" <td>0</td>\n",
" <td>351</td>\n",
" <td>YES</td>\n",
" <td>YES</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>TCGA-OU-A5PI</td>\n",
" <td>ACC</td>\n",
" <td>Doxorubicin</td>\n",
" <td>R</td>\n",
" <td>Stable Disease</td>\n",
" <td>69</td>\n",
" <td>239</td>\n",
" <td>DB00997</td>\n",
" <td>0</td>\n",
" <td>Surgical Resection</td>\n",
" <td>0</td>\n",
" <td>351</td>\n",
" <td>YES</td>\n",
" <td>YES</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>TCGA-OU-A5PI</td>\n",
" <td>ACC</td>\n",
" <td>Cisplatin</td>\n",
" <td>R</td>\n",
" <td>Stable Disease</td>\n",
" <td>55</td>\n",
" <td>239</td>\n",
" <td>DB00515</td>\n",
" <td>0</td>\n",
" <td>Surgical Resection</td>\n",
" <td>0</td>\n",
" <td>351</td>\n",
" <td>YES</td>\n",
" <td>YES</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"1 bcr_patient_barcode cohort drug response \\\n",
"3 TCGA-OR-A5JM ACC Sunitinib R \n",
"4 TCGA-OR-A5JM ACC Ketoconazole R \n",
"5 TCGA-OU-A5PI ACC Etoposide R \n",
"6 TCGA-OU-A5PI ACC Doxorubicin R \n",
"7 TCGA-OU-A5PI ACC Cisplatin R \n",
"\n",
"1 measure_of_response days_to_drug_therapy_start \\\n",
"3 Clinical Progressive Disease 378 \n",
"4 Clinical Progressive Disease 378 \n",
"5 Stable Disease 69 \n",
"6 Stable Disease 69 \n",
"7 Stable Disease 55 \n",
"\n",
"1 days_to_drug_therapy_end DrugBank ID days_to_initial_pathologic_diagnosis \\\n",
"3 439 DB01268 0 \n",
"4 439 DB01026 0 \n",
"5 239 DB00773 0 \n",
"6 239 DB00997 0 \n",
"7 239 DB00515 0 \n",
"\n",
"1 method_of_sample_procurement days_to_sample_procurement \\\n",
"3 Surgical Resection 1 \n",
"4 Surgical Resection 1 \n",
"5 Surgical Resection 0 \n",
"6 Surgical Resection 0 \n",
"7 Surgical Resection 0 \n",
"\n",
"1 days_to_new_tumor_event_after_initial_treatment \\\n",
"3 72 \n",
"4 72 \n",
"5 351 \n",
"6 351 \n",
"7 351 \n",
"\n",
"1 additional_pharmaceutical_therapy \\\n",
"3 YES \n",
"4 YES \n",
"5 YES \n",
"6 YES \n",
"7 YES \n",
"\n",
"1 new_tumor_event_additional_surgery_procedure \\\n",
"3 NO \n",
"4 NO \n",
"5 YES \n",
"6 YES \n",
"7 YES \n",
"\n",
"1 history_of_neoadjuvant_treatment \n",
"3 Yes \n",
"4 Yes \n",
"5 No \n",
"6 No \n",
"7 No "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# add download of annotation file \n",
"df = pd.read_excel(\"/home/olya/SFU/Hossein/TCGA/annotation_Ding2016/bioinfo16_supplementary_tables.xlsx\",\n",
" \"Table S2\")\n",
"df = df.drop_duplicates()\n",
"df.drop([0,2],inplace=True)\n",
"cols = df.loc[1,:]\n",
"df = df.drop(1)\n",
"df.columns = cols\n",
"df.loc[:,\"cohort\"] = df[\"Cancer\"].apply(lambda x: re.search(r'\\((.*?)\\)',x).group(1))\n",
"df.loc[:,\"response\"] = df[\"measure_of_response\"].apply(lambda x: response_dict[x])\n",
"print(df.shape)\n",
"dup_indices = df.loc[df[[\"bcr_patient_barcode\",\n",
" \"days_to_drug_therapy_start\",\"days_to_drug_therapy_end\"]].duplicated(keep=False),:].index.values\n",
"df = df[[\"bcr_patient_barcode\",\"cohort\",\"drug_name\",\"response\",\"measure_of_response\",\n",
" \"days_to_drug_therapy_start\",\"days_to_drug_therapy_end\",\"DrugBank ID\",\n",
" \"days_to_initial_pathologic_diagnosis\",\"method_of_sample_procurement\",\n",
" \"days_to_sample_procurement\",\"days_to_new_tumor_event_after_initial_treatment\",\n",
" \"additional_pharmaceutical_therapy\",\"new_tumor_event_additional_surgery_procedure\",\n",
" \"history_of_neoadjuvant_treatment\"]]\n",
"df.rename({\"drug_name\":\"drug\"},axis=\"columns\",inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"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>1</th>\n",
" <th>bcr_patient_barcode</th>\n",
" <th>cohort</th>\n",
" <th>drug</th>\n",
" <th>response</th>\n",
" <th>measure_of_response</th>\n",
" <th>days_to_drug_therapy_start</th>\n",
" <th>days_to_drug_therapy_end</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>TCGA-OU-A5PI</td>\n",
" <td>ACC</td>\n",
" <td>Carboplatin</td>\n",
" <td>R</td>\n",
" <td>Stable Disease</td>\n",
" <td>725</td>\n",
" <td>817</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"1 bcr_patient_barcode cohort drug response measure_of_response \\\n",
"12 TCGA-OU-A5PI ACC Carboplatin R Stable Disease \n",
"\n",
"1 days_to_drug_therapy_start days_to_drug_therapy_end \n",
"12 725 817 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for group in df.iloc[2:10,0:7].groupby(\"bcr_patient_barcode\"):\n",
" pass\n",
"def exclude_combos(df_group):\n",
" if df_group.shape[0] == 1:\n",
" return df_group\n",
" d = df_group.T.to_dict()\n",
" keys_to_remove = set()\n",
" for key in d.keys():\n",
" start = d[key][\"days_to_drug_therapy_start\"]\n",
" end = d[key][\"days_to_drug_therapy_end\"]\n",
" #print(key,start,end)\n",
" for key2 in d.keys():\n",
" if key2 != key:\n",
" start2 = d[key2][\"days_to_drug_therapy_start\"]\n",
" end2 = d[key2][\"days_to_drug_therapy_end\"]\n",
" if not (end < start2) and not (end2 < start):\n",
" # if not non-overlapping time intervals\n",
" keys_to_remove.add(key)\n",
" keys_to_remove.add(key2)\n",
" #print(list(keys_to_remove))\n",
" return df_group.loc[~df_group.index.isin(keys_to_remove),:]\n",
"exclude_combos(group[1])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(785, 15)\n",
"Records with combo drugs excluded: 1784\n"
]
},
{
"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>1</th>\n",
" <th>bcr_patient_barcode</th>\n",
" <th>cohort</th>\n",
" <th>drug</th>\n",
" <th>response</th>\n",
" <th>measure_of_response</th>\n",
" <th>days_to_drug_therapy_start</th>\n",
" <th>days_to_drug_therapy_end</th>\n",
" <th>DrugBank ID</th>\n",
" <th>days_to_initial_pathologic_diagnosis</th>\n",
" <th>method_of_sample_procurement</th>\n",
" <th>days_to_sample_procurement</th>\n",
" <th>days_to_new_tumor_event_after_initial_treatment</th>\n",
" <th>additional_pharmaceutical_therapy</th>\n",
" <th>new_tumor_event_additional_surgery_procedure</th>\n",
" <th>history_of_neoadjuvant_treatment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1355</th>\n",
" <td>TCGA-05-4402</td>\n",
" <td>LUAD</td>\n",
" <td>Erlotinib</td>\n",
" <td>S</td>\n",
" <td>Complete Response</td>\n",
" <td>122</td>\n",
" <td>122</td>\n",
" <td>DB00530</td>\n",
" <td>0</td>\n",
" <td>Other Method (please specify)</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1360</th>\n",
" <td>TCGA-05-5425</td>\n",
" <td>LUAD</td>\n",
" <td>Gefitinib</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>608</td>\n",
" <td>669</td>\n",
" <td>DB00317</td>\n",
" <td>0</td>\n",
" <td>Other Method (please specify)</td>\n",
" <td>31</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>TCGA-06-1806</td>\n",
" <td>GBM</td>\n",
" <td>veliparib</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>81</td>\n",
" <td>256</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>Subtotal Resection</td>\n",
" <td>0</td>\n",
" <td>256</td>\n",
" <td>YES</td>\n",
" <td>[Not Available]</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>884</th>\n",
" <td>TCGA-06-1806</td>\n",
" <td>GBM</td>\n",
" <td>Cabozantinib</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>293</td>\n",
" <td>455</td>\n",
" <td>DB08875</td>\n",
" <td>0</td>\n",
" <td>Subtotal Resection</td>\n",
" <td>0</td>\n",
" <td>256</td>\n",
" <td>YES</td>\n",
" <td>[Not Available]</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>885</th>\n",
" <td>TCGA-06-A5U0</td>\n",
" <td>GBM</td>\n",
" <td>Temozolomide</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>31</td>\n",
" <td>74</td>\n",
" <td>DB00853</td>\n",
" <td>0</td>\n",
" <td>Subtotal Resection</td>\n",
" <td>0</td>\n",
" <td>100</td>\n",
" <td>YES</td>\n",
" <td>[Not Available]</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"1 bcr_patient_barcode cohort drug response \\\n",
"1355 TCGA-05-4402 LUAD Erlotinib S \n",
"1360 TCGA-05-5425 LUAD Gefitinib R \n",
"883 TCGA-06-1806 GBM veliparib R \n",
"884 TCGA-06-1806 GBM Cabozantinib R \n",
"885 TCGA-06-A5U0 GBM Temozolomide R \n",
"\n",
"1 measure_of_response days_to_drug_therapy_start \\\n",
"1355 Complete Response 122 \n",
"1360 Clinical Progressive Disease 608 \n",
"883 Clinical Progressive Disease 81 \n",
"884 Clinical Progressive Disease 293 \n",
"885 Clinical Progressive Disease 31 \n",
"\n",
"1 days_to_drug_therapy_end DrugBank ID \\\n",
"1355 122 DB00530 \n",
"1360 669 DB00317 \n",
"883 256 NaN \n",
"884 455 DB08875 \n",
"885 74 DB00853 \n",
"\n",
"1 days_to_initial_pathologic_diagnosis method_of_sample_procurement \\\n",
"1355 0 Other Method (please specify) \n",
"1360 0 Other Method (please specify) \n",
"883 0 Subtotal Resection \n",
"884 0 Subtotal Resection \n",
"885 0 Subtotal Resection \n",
"\n",
"1 days_to_sample_procurement \\\n",
"1355 0 \n",
"1360 31 \n",
"883 0 \n",
"884 0 \n",
"885 0 \n",
"\n",
"1 days_to_new_tumor_event_after_initial_treatment \\\n",
"1355 NaN \n",
"1360 NaN \n",
"883 256 \n",
"884 256 \n",
"885 100 \n",
"\n",
"1 additional_pharmaceutical_therapy \\\n",
"1355 NaN \n",
"1360 NaN \n",
"883 YES \n",
"884 YES \n",
"885 YES \n",
"\n",
"1 new_tumor_event_additional_surgery_procedure \\\n",
"1355 NaN \n",
"1360 NaN \n",
"883 [Not Available] \n",
"884 [Not Available] \n",
"885 [Not Available] \n",
"\n",
"1 history_of_neoadjuvant_treatment \n",
"1355 No \n",
"1360 No \n",
"883 No \n",
"884 No \n",
"885 No "
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_single = []\n",
"for group in df.groupby(\"bcr_patient_barcode\"):\n",
" df_single.append(exclude_combos(group[1]))\n",
"df_single = pd.concat(df_single)\n",
"print(df_single.shape)\n",
"print(\"Records with combo drugs excluded:\",df.shape[0] - df_single.shape[0])\n",
"df_single.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cisplatin 113 R: 19 S: 94\n",
"Cetuximab 10 R: 4 S: 6\n",
"Gemcitabine 68 R: 43 S: 25\n",
"Bortezomib 0 R: 0 S: 0\n",
"Tamoxifen 15 R: 3 S: 12\n",
"ZD-6474 0 R: 0 S: 0\n",
"Gefitinib 2 R: 2 S: 0\n",
"5-Fluorouracil 0 R: 0 S: 0\n",
"Afatinib 0 R: 0 S: 0\n",
"Pelitinib 0 R: 0 S: 0\n",
"Panitumumab 0 R: 0 S: 0\n",
"Paclitaxel 49 R: 15 S: 34\n",
"Docetaxel 21 R: 12 S: 9\n",
"Lapatinib 0 R: 0 S: 0\n",
"Erlotinib 6 R: 4 S: 2\n"
]
}
],
"source": [
"drugs = list(set(['Docetaxel', 'Cisplatin', 'Erlotinib', 'Bortezomib','5-Fluorouracil',\n",
" 'Tamoxifen', 'Cetuximab', 'Paclitaxel', 'Gemcitabine'] + EGFRi_drugs))\n",
"\n",
"for drug in drugs:\n",
" d = df_single[df_single[\"drug\"] == drug ]\n",
" print(drug, d.shape[0],\"R:\",d[d[\"response\"] ==\"R\"].shape[0],\"S:\",d[d[\"response\"] ==\"S\"].shape[0] )\n",
" if d.shape[0] > 0 :\n",
" d.set_index(\"bcr_patient_barcode\",drop=True,inplace=True)\n",
" d.to_csv(root_dir+\"/preprocessed/annotations/\"+\"TCGA_response.\"+drug+\".tsv\",sep = \"\\t\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>1</th>\n",
" <th>cohort</th>\n",
" <th>drug</th>\n",
" <th>response</th>\n",
" <th>measure_of_response</th>\n",
" <th>days_to_drug_therapy_start</th>\n",
" <th>days_to_drug_therapy_end</th>\n",
" <th>DrugBank ID</th>\n",
" <th>days_to_initial_pathologic_diagnosis</th>\n",
" <th>method_of_sample_procurement</th>\n",
" <th>days_to_sample_procurement</th>\n",
" <th>days_to_new_tumor_event_after_initial_treatment</th>\n",
" <th>additional_pharmaceutical_therapy</th>\n",
" <th>new_tumor_event_additional_surgery_procedure</th>\n",
" <th>history_of_neoadjuvant_treatment</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bcr_patient_barcode</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>TCGA-05-4402</th>\n",
" <td>LUAD</td>\n",
" <td>Erlotinib</td>\n",
" <td>S</td>\n",
" <td>Complete Response</td>\n",
" <td>122</td>\n",
" <td>122</td>\n",
" <td>DB00530</td>\n",
" <td>0</td>\n",
" <td>Other Method (please specify)</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCGA-53-7624</th>\n",
" <td>LUAD</td>\n",
" <td>Erlotinib</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>880</td>\n",
" <td>922</td>\n",
" <td>DB00530</td>\n",
" <td>0</td>\n",
" <td>Other Method (please specify)</td>\n",
" <td>40</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TCGA-64-5778</th>\n",
" <td>LUAD</td>\n",
" <td>Erlotinib</td>\n",
" <td>R</td>\n",
" <td>Clinical Progressive Disease</td>\n",
" <td>1174</td>\n",
" <td>[Not Available]</td>\n",
" <td>DB00530</td>\n",
" <td>0</td>\n",
" <td>Tumor Resection</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"1 cohort drug response measure_of_response \\\n",
"bcr_patient_barcode \n",
"TCGA-05-4402 LUAD Erlotinib S Complete Response \n",
"TCGA-53-7624 LUAD Erlotinib R Clinical Progressive Disease \n",
"TCGA-64-5778 LUAD Erlotinib R Clinical Progressive Disease \n",
"\n",
"1 days_to_drug_therapy_start days_to_drug_therapy_end \\\n",
"bcr_patient_barcode \n",
"TCGA-05-4402 122 122 \n",
"TCGA-53-7624 880 922 \n",
"TCGA-64-5778 1174 [Not Available] \n",
"\n",
"1 DrugBank ID days_to_initial_pathologic_diagnosis \\\n",
"bcr_patient_barcode \n",
"TCGA-05-4402 DB00530 0 \n",
"TCGA-53-7624 DB00530 0 \n",
"TCGA-64-5778 DB00530 0 \n",
"\n",
"1 method_of_sample_procurement days_to_sample_procurement \\\n",
"bcr_patient_barcode \n",
"TCGA-05-4402 Other Method (please specify) 0 \n",
"TCGA-53-7624 Other Method (please specify) 40 \n",
"TCGA-64-5778 Tumor Resection 0 \n",
"\n",
"1 days_to_new_tumor_event_after_initial_treatment \\\n",
"bcr_patient_barcode \n",
"TCGA-05-4402 NaN \n",
"TCGA-53-7624 NaN \n",
"TCGA-64-5778 NaN \n",
"\n",
"1 additional_pharmaceutical_therapy \\\n",
"bcr_patient_barcode \n",
"TCGA-05-4402 NaN \n",
"TCGA-53-7624 NaN \n",
"TCGA-64-5778 NaN \n",
"\n",
"1 new_tumor_event_additional_surgery_procedure \\\n",
"bcr_patient_barcode \n",
"TCGA-05-4402 NaN \n",
"TCGA-53-7624 NaN \n",
"TCGA-64-5778 NaN \n",
"\n",
"1 history_of_neoadjuvant_treatment \n",
"bcr_patient_barcode \n",
"TCGA-05-4402 No \n",
"TCGA-53-7624 No \n",
"TCGA-64-5778 No "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GDSC\n",
"\n",
"### Continuous response - log(IC50) values \n",
"\n",
"* Supplementary files from \"A landscape of pharmacogenomic interactions in cancer\" by Iorio F et al. Cell. 2016:\n",
"TableS4A.xlsx from https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/suppData/TableS4A.xlsx , tab 'TableS4A-IC50s'\n",
"\n",
"* Also, log(IC50) for are available here ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/v17.3_fitted_dose_response.xlsx\n",
"(ln(IC50), these values seem to be just slightly different)\n",
"\n",
"### Binary response \n",
"\n",
"* Supplementary files from Iorio F et al. 2016\n",
"https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources///Data/suppData/TableS5C.xlsx\n",
"\n",
"Cell line names were replaced with corresponding COSMIC ids from \n",
"https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/suppData/TableS1E.xlsx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GDSC - binarized response "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"COSMIC_ids = pd.read_excel(tmp_dir+\"TableS1E.xlsx\")\n",
"COSMIC_ids = COSMIC_ids.iloc[2:,[1,2]]\n",
"COSMIC_ids = COSMIC_ids.iloc[:-1,]\n",
"COSMIC_ids.columns = [\"name\",'COSMIC']\n",
"# 1002 pair, all IDs are unique\n",
"#print(COSMIC_ids.shape[0],len(set(COSMIC_ids[\"name\"])),len(set(COSMIC_ids[\"COSMIC\"])))\n",
"COSMIC_ids.set_index(\"name\",inplace=True,drop=True)\n",
"names2COSMIC = dict(COSMIC_ids[\"COSMIC\"])\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cisplatin</th>\n",
" <th>Cetuximab</th>\n",
" <th>Gemcitabine</th>\n",
" <th>Bortezomib</th>\n",
" <th>Tamoxifen</th>\n",
" <th>Gefitinib</th>\n",
" <th>5-Fluorouracil</th>\n",
" <th>Afatinib</th>\n",
" <th>Paclitaxel</th>\n",
" <th>Docetaxel</th>\n",
" <th>Lapatinib</th>\n",
" <th>Erlotinib</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cell_line</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>683665</th>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>S</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>683667</th>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684052</th>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684055</th>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684057</th>\n",
" <td>S</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" <td>R</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Cisplatin Cetuximab Gemcitabine Bortezomib Tamoxifen Gefitinib \\\n",
"cell_line \n",
"683665 R R R R R R \n",
"683667 R NaN R NaN R R \n",
"684052 NaN R R NaN R NaN \n",
"684055 NaN S R R R NaN \n",
"684057 S R R R R R \n",
"\n",
" 5-Fluorouracil Afatinib Paclitaxel Docetaxel Lapatinib Erlotinib \n",
"cell_line \n",
"683665 S R R R R R \n",
"683667 R R NaN R NaN NaN \n",
"684052 R NaN NaN NaN NaN NaN \n",
"684055 R NaN R NaN R R \n",
"684057 R R R R R R "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel(tmp_dir+\"TableS5C.xlsx\")\n",
"df.drop([0,1,2],inplace=True)\n",
"df = df.iloc[:,1:]\n",
"df.set_index(\"TableS5C - Binarized Drug IC50s, refers to figure 5\",inplace=True,drop=True)\n",
"df.columns = df.loc[\"Screened Compounds:\",:].values\n",
"df = df.iloc[1:,:]\n",
"df.index.name = \"cell_line\"\n",
"\n",
"IC50_thr = df.iloc[0,:]\n",
"IC50_thr.name = \"logIC50_threshold\"\n",
"df = df.iloc[1:,:]\n",
"\n",
"df.rename(names2COSMIC,axis=\"index\",inplace=True)\n",
"drugs = set(nine_drugs+EGFRi_drugs).intersection(set(df.columns.values))\n",
"df = df.loc[:,drugs]\n",
"df.sort_values(by=\"cell_line\",inplace=True)\n",
"df.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GDSC_response.\"+\"all_drugs\"+\".tsv\",sep = \"\\t\")\n",
"df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cisplatin</th>\n",
" <th>Cetuximab</th>\n",
" <th>Gemcitabine</th>\n",
" <th>Bortezomib</th>\n",
" <th>Tamoxifen</th>\n",
" <th>Gefitinib</th>\n",
" <th>5-Fluorouracil</th>\n",
" <th>Afatinib</th>\n",
" <th>Paclitaxel</th>\n",
" <th>Docetaxel</th>\n",
" <th>Lapatinib</th>\n",
" <th>Erlotinib</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cell_line</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>683665</th>\n",
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" <td>6.29445</td>\n",
" <td>-4.40897</td>\n",
" <td>-3.81791</td>\n",
" <td>2.96832</td>\n",
" <td>1.46485</td>\n",
" <td>0.145949</td>\n",
" <td>1.49002</td>\n",
" <td>-3.64729</td>\n",
" <td>-4.91873</td>\n",
" <td>2.68418</td>\n",
" <td>2.43659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>683667</th>\n",
" <td>1.75756</td>\n",
" <td>NaN</td>\n",
" <td>-0.399711</td>\n",
" <td>NaN</td>\n",
" <td>3.70926</td>\n",
" <td>1.17482</td>\n",
" <td>3.7722</td>\n",
" <td>1.86838</td>\n",
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" <th>684052</th>\n",
" <td>NaN</td>\n",
" <td>6.38732</td>\n",
" <td>-3.70724</td>\n",
" <td>NaN</td>\n",
" <td>3.57455</td>\n",
" <td>NaN</td>\n",
" <td>4.708</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>684055</th>\n",
" <td>NaN</td>\n",
" <td>4.95212</td>\n",
" <td>-2.99645</td>\n",
" <td>-3.84107</td>\n",
" <td>3.6898</td>\n",
" <td>NaN</td>\n",
" <td>3.74045</td>\n",
" <td>NaN</td>\n",
" <td>-0.214086</td>\n",
" <td>NaN</td>\n",
" <td>3.22649</td>\n",
" <td>3.34283</td>\n",
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" <tr>\n",
" <th>684057</th>\n",
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" <td>6.39356</td>\n",
" <td>-2.41002</td>\n",
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" <td>3.80699</td>\n",
" <td>2.15203</td>\n",
" <td>1.93716</td>\n",
" <td>0.463011</td>\n",
" <td>0.0960912</td>\n",
" <td>-6.73713</td>\n",
" <td>3.57179</td>\n",
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],
"text/plain": [
" Cisplatin Cetuximab Gemcitabine Bortezomib Tamoxifen Gefitinib \\\n",
"cell_line \n",
"683665 2.80727 6.29445 -4.40897 -3.81791 2.96832 1.46485 \n",
"683667 1.75756 NaN -0.399711 NaN 3.70926 1.17482 \n",
"684052 NaN 6.38732 -3.70724 NaN 3.57455 NaN \n",
"684055 NaN 4.95212 -2.99645 -3.84107 3.6898 NaN \n",
"684057 1.13197 6.39356 -2.41002 -4.39987 3.80699 2.15203 \n",
"\n",
" 5-Fluorouracil Afatinib Paclitaxel Docetaxel Lapatinib Erlotinib \n",
"cell_line \n",
"683665 0.145949 1.49002 -3.64729 -4.91873 2.68418 2.43659 \n",
"683667 3.7722 1.86838 NaN -6.34303 NaN NaN \n",
"684052 4.708 NaN NaN NaN NaN NaN \n",
"684055 3.74045 NaN -0.214086 NaN 3.22649 3.34283 \n",
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]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ic50 = pd.read_excel(tmp_dir+\"TableS4A.xlsx\",'TableS4A-IC50s')\n",
"df_ic50 = df_ic50.iloc[3:,:]\n",
"df_ic50.drop(['TableS4A - Whole set of log(IC50s) across all the screened compounds and cell lines, related to Figure 4'],axis=1,inplace=True)\n",
"df_ic50.columns = df_ic50.iloc[0,:].values\n",
"df_ic50 = df_ic50.iloc[1:,:]\n",
"df_ic50.index = df_ic50.iloc[:,0].values\n",
"df_ic50.index.name = \"cell_line\"\n",
"df_ic50 = df_ic50.iloc[:,1:]\n",
"df_ic50.sort_values(by=\"cell_line\",inplace=True)\n",
"df_ic50.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GDSC_response.\"+\"logIC50.all_drugs\"+\".tsv\",sep = \"\\t\")\n",
"df_ic50[list(drugs)].head()\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{u'5-Fluorouracil': 1.1236,\n",
" u'Afatinib': -0.22156,\n",
" u'Bortezomib': -7.6275,\n",
" u'Cetuximab': 5.144,\n",
" u'Cisplatin': 1.3801,\n",
" u'Docetaxel': -6.897,\n",
" u'Erlotinib': 1.5671,\n",
" u'Gefitinib': -0.05346,\n",
" u'Gemcitabine': -5.9903,\n",
" u'Lapatinib': 1.6257,\n",
" u'Paclitaxel': -5.6772,\n",
" u'Tamoxifen': 2.7296}"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IC50_thr = IC50_thr[list(drugs)].to_dict()\n",
"IC50_thr"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/olya/miniconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" import sys\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cisplatin total: 850 R: 771 S: 79\n",
"Cetuximab total: 873 R: 749 S: 124\n",
"Gemcitabine total: 870 R: 815 S: 55\n",
"Bortezomib total: 402 R: 371 S: 31\n",
"Tamoxifen total: 928 R: 820 S: 108\n",
"Gefitinib total: 846 R: 727 S: 119\n",
"5-Fluorouracil total: 916 R: 822 S: 94\n",
"Afatinib total: 849 R: 696 S: 153\n",
"Paclitaxel total: 402 R: 376 S: 26\n",
"Docetaxel total: 850 R: 784 S: 66\n",
"Lapatinib total: 398 R: 337 S: 61\n",
"Erlotinib total: 372 R: 308 S: 64\n"
]
}
],
"source": [
"df_long = []\n",
"for drug in drugs:\n",
" d1 = df_ic50.loc[:,[drug]]\n",
" d1.columns = [\"logIC50\"]\n",
" d2 = df.loc[:,[drug]]\n",
" d2.columns = [\"response\"]\n",
" d1.dropna(inplace=True)\n",
" d2.dropna(inplace=True)\n",
" d = pd.concat([d2,d1],axis=1)\n",
" d.loc[:,\"drug\"] = drug\n",
" d.index.name = \"sample_name\"\n",
" df_long.append(d)\n",
" if d.shape[0] >0 :\n",
" d.to_csv(root_dir+\"/preprocessed/annotations/\"+\"GDSC_response.\"+drug+\".tsv\",sep = \"\\t\")\n",
" print(drug,\"total:\",d.shape[0],\"R:\",d.loc[d[\"logIC50\"]>IC50_thr[drug],:].shape[0],\n",
" \"S:\",d.loc[d[\"logIC50\"]<=IC50_thr[drug],:].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>response</th>\n",
" <th>logIC50</th>\n",
" <th>drug</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sample_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>683665</th>\n",
" <td>R</td>\n",
" <td>2.807269</td>\n",
" <td>Cisplatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>683667</th>\n",
" <td>R</td>\n",
" <td>1.757559</td>\n",
" <td>Cisplatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684057</th>\n",
" <td>S</td>\n",
" <td>1.131967</td>\n",
" <td>Cisplatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684059</th>\n",
" <td>S</td>\n",
" <td>0.877124</td>\n",
" <td>Cisplatin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684062</th>\n",
" <td>S</td>\n",
" <td>1.342990</td>\n",
" <td>Cisplatin</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" response logIC50 drug\n",
"sample_name \n",
"683665 R 2.807269 Cisplatin\n",
"683667 R 1.757559 Cisplatin\n",
"684057 S 1.131967 Cisplatin\n",
"684059 S 0.877124 Cisplatin\n",
"684062 S 1.342990 Cisplatin"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_long = pd.concat(df_long)\n",
"df_long.loc[:,\"logIC50\"] = df_long[\"logIC50\"].apply(np.float)\n",
"df_long.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### log(IC50) in R and S groups "
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa08a2c4cd0>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,7))\n",
"sns.violinplot(x=\"drug\", y=\"logIC50\", hue=\"response\",data=df_long,split=True)\n",
"# this curve fitting is imprecize\n",
"#sns.swarmplot(x=\"drug\", y=\"logIC50\", hue=\"response\",data=df_long)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa08abc8b90>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,7))\n",
"sns.swarmplot(x=\"drug\", y=\"logIC50\", hue=\"response\",data=df_long)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CCLE \n",
"\n",
"#### Problems : \n",
"\"GSM886946\":\"COLO-699\" and \"GSM887546\":\"RPMI 6666\" are absent in annotation\n",
"\n",
"drug response data:\n",
"wget https://data.broadinstitute.org/ccle_legacy_data/pharmacological_profiling/CCLE_NP24.2009_Drug_data_2015.02.24.csv\n",
"\n",
"cell line annotation:\n",
"wget https://data.broadinstitute.org/ccle_legacy_data/cell_line_annotations/CCLE_sample_info_file_2012-10-18.txt\n",
"\n",
"expression annotation\n",
"wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE36nnn/GSE36133/matrix/GSE36133_series_matrix.txt.gz\n",
"\n",
"\n",
"#### Drugs \n",
"\"Paclitaxel\",\"Erlotinib\"\n",
"\n",
"Targeted EGFRi : 'Erlotinib', 'Lapatinib', 'ZD-6474'\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>histology</th>\n",
" <th>histology subtype1</th>\n",
" <th>primary site</th>\n",
" <th>source</th>\n",
" <th>title</th>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GSM886835</th>\n",
" <td>glioma</td>\n",
" <td>astrocytoma</td>\n",
" <td>central_nervous_system</td>\n",
" <td>ECACC</td>\n",
" <td>1321N1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886836</th>\n",
" <td>osteosarcoma</td>\n",
" <td></td>\n",
" <td>bone</td>\n",
" <td>ATCC</td>\n",
" <td>143B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886837</th>\n",
" <td>carcinoma</td>\n",
" <td></td>\n",
" <td>prostate</td>\n",
" <td>ATCC</td>\n",
" <td>22Rv1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886838</th>\n",
" <td>carcinoma</td>\n",
" <td>adenocarcinoma</td>\n",
" <td>stomach</td>\n",
" <td>DSMZ</td>\n",
" <td>23132/87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886839</th>\n",
" <td>glioma</td>\n",
" <td>astrocytoma_Grade_IV</td>\n",
" <td>central_nervous_system</td>\n",
" <td>DSMZ</td>\n",
" <td>42-MG-BA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" histology histology subtype1 primary site source \\\n",
"GSM \n",
"GSM886835 glioma astrocytoma central_nervous_system ECACC \n",
"GSM886836 osteosarcoma bone ATCC \n",
"GSM886837 carcinoma prostate ATCC \n",
"GSM886838 carcinoma adenocarcinoma stomach DSMZ \n",
"GSM886839 glioma astrocytoma_Grade_IV central_nervous_system DSMZ \n",
"\n",
" title \n",
"GSM \n",
"GSM886835 1321N1 \n",
"GSM886836 143B \n",
"GSM886837 22Rv1 \n",
"GSM886838 23132/87 \n",
"GSM886839 42-MG-BA "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fpath = download_GEO_matrix(\"GSE36133_series_matrix.txt.gz\",'/geo/series/GSE36nnn/GSE36133/matrix/'\n",
" ,destination=tmp_dir)\n",
"df = read_matrix(fpath)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"anno = pd.read_csv(root_dir+ \"/preprocessed/annotations/CCLE_sample_info_file_2012-10-18.txt\",sep = \"\\t\")\n",
"anno = anno[[\"CCLE name\",\"Cell line primary name\"]]\n",
"anno.set_index(\"Cell line primary name\",drop=True,inplace=True)\n",
"CCLE_names_dict = anno.to_dict()['CCLE name']\n",
"#CCLE_names_dict "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1046, 915, 917, 1046, {'COLO-699', 'RPMI 6666'})"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mapped_CCLE_names = set(df[\"title\"].values).intersection(set(CCLE_names_dict.keys()))\n",
"not_mapped_CCLE_names = set(df[\"title\"].values).difference(set(CCLE_names_dict.keys()))\n",
"not_mapped_GSM = set(df[\"title\"].values).difference(set(CCLE_names_dict.keys()))\n",
"len(CCLE_names_dict.keys()), len(mapped_CCLE_names), len(set(df[\"title\"].values)), len(set(CCLE_names_dict.keys())),not_mapped_CCLE_names"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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>histology</th>\n",
" <th>histology subtype1</th>\n",
" <th>primary site</th>\n",
" <th>source</th>\n",
" <th>title</th>\n",
" <th>CCLE_name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GSM886835</th>\n",
" <td>glioma</td>\n",
" <td>astrocytoma</td>\n",
" <td>central_nervous_system</td>\n",
" <td>ECACC</td>\n",
" <td>1321N1</td>\n",
" <td>1321N1_CENTRAL_NERVOUS_SYSTEM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886836</th>\n",
" <td>osteosarcoma</td>\n",
" <td></td>\n",
" <td>bone</td>\n",
" <td>ATCC</td>\n",
" <td>143B</td>\n",
" <td>143B_BONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886837</th>\n",
" <td>carcinoma</td>\n",
" <td></td>\n",
" <td>prostate</td>\n",
" <td>ATCC</td>\n",
" <td>22Rv1</td>\n",
" <td>22RV1_PROSTATE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886838</th>\n",
" <td>carcinoma</td>\n",
" <td>adenocarcinoma</td>\n",
" <td>stomach</td>\n",
" <td>DSMZ</td>\n",
" <td>23132/87</td>\n",
" <td>2313287_STOMACH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSM886839</th>\n",
" <td>glioma</td>\n",
" <td>astrocytoma_Grade_IV</td>\n",
" <td>central_nervous_system</td>\n",
" <td>DSMZ</td>\n",
" <td>42-MG-BA</td>\n",
" <td>42MGBA_CENTRAL_NERVOUS_SYSTEM</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" histology histology subtype1 primary site source \\\n",
"GSM \n",
"GSM886835 glioma astrocytoma central_nervous_system ECACC \n",
"GSM886836 osteosarcoma bone ATCC \n",
"GSM886837 carcinoma prostate ATCC \n",
"GSM886838 carcinoma adenocarcinoma stomach DSMZ \n",
"GSM886839 glioma astrocytoma_Grade_IV central_nervous_system DSMZ \n",
"\n",
" title CCLE_name \n",
"GSM \n",
"GSM886835 1321N1 1321N1_CENTRAL_NERVOUS_SYSTEM \n",
"GSM886836 143B 143B_BONE \n",
"GSM886837 22Rv1 22RV1_PROSTATE \n",
"GSM886838 23132/87 2313287_STOMACH \n",
"GSM886839 42-MG-BA 42MGBA_CENTRAL_NERVOUS_SYSTEM "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.loc[df[\"title\"].isin(mapped_CCLE_names ),:]\n",
"df[\"CCLE_name\"] = df[\"title\"].apply(lambda x: CCLE_names_dict[x]) #df[\"title\"] +\"_\" + df[\"primary site\"].apply(str.upper)\n",
"df.to_csv(root_dir+ \"/preprocessed/annotations/CCLE_expessions.annotations.tsv\",sep = \"\\t\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\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>1321N1_CENTRAL_NERVOUS_SYSTEM</th>\n",
" <th>143B_BONE</th>\n",
" <th>22RV1_PROSTATE</th>\n",
" <th>2313287_STOMACH</th>\n",
" <th>42MGBA_CENTRAL_NERVOUS_SYSTEM</th>\n",
" <th>5637_URINARY_TRACT</th>\n",
" <th>639V_URINARY_TRACT</th>\n",
" <th>647V_URINARY_TRACT</th>\n",
" <th>697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</th>\n",
" <th>769P_KIDNEY</th>\n",
" <th>...</th>\n",
" <th>YAPC_PANCREAS</th>\n",
" <th>YD10B_UPPER_AERODIGESTIVE_TRACT</th>\n",
" <th>YD15_SALIVARY_GLAND</th>\n",
" <th>YD38_UPPER_AERODIGESTIVE_TRACT</th>\n",
" <th>YD8_UPPER_AERODIGESTIVE_TRACT</th>\n",
" <th>YH13_CENTRAL_NERVOUS_SYSTEM</th>\n",
" <th>YKG1_CENTRAL_NERVOUS_SYSTEM</th>\n",
" <th>YMB1_BREAST</th>\n",
" <th>ZR751_BREAST</th>\n",
" <th>ZR7530_BREAST</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.899963</td>\n",
" <td>5.495630</td>\n",
" <td>4.915154</td>\n",
" <td>4.386444</td>\n",
" <td>8.018313</td>\n",
" <td>4.440586</td>\n",
" <td>7.108326</td>\n",
" <td>4.974334</td>\n",
" <td>7.542390</td>\n",
" <td>4.769888</td>\n",
" <td>...</td>\n",
" <td>4.750698</td>\n",
" <td>4.899243</td>\n",
" <td>4.071518</td>\n",
" <td>4.630944</td>\n",
" <td>4.617563</td>\n",
" <td>7.214694</td>\n",
" <td>5.955572</td>\n",
" <td>8.455509</td>\n",
" <td>7.726960</td>\n",
" <td>6.890627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.151092</td>\n",
" <td>4.867485</td>\n",
" <td>4.683452</td>\n",
" <td>3.770537</td>\n",
" <td>3.992411</td>\n",
" <td>3.668579</td>\n",
" <td>3.643959</td>\n",
" <td>3.799457</td>\n",
" <td>3.985218</td>\n",
" <td>3.708740</td>\n",
" <td>...</td>\n",
" <td>3.532178</td>\n",
" <td>4.184472</td>\n",
" <td>3.778447</td>\n",
" <td>3.898891</td>\n",
" <td>4.298302</td>\n",
" <td>4.583785</td>\n",
" <td>3.854023</td>\n",
" <td>4.372195</td>\n",
" <td>3.680397</td>\n",
" <td>4.555957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7.937206</td>\n",
" <td>7.653808</td>\n",
" <td>10.551317</td>\n",
" <td>9.345134</td>\n",
" <td>8.158181</td>\n",
" <td>7.965484</td>\n",
" <td>7.525776</td>\n",
" <td>8.514274</td>\n",
" <td>8.230191</td>\n",
" <td>8.323116</td>\n",
" <td>...</td>\n",
" <td>7.935024</td>\n",
" <td>7.825549</td>\n",
" <td>8.277187</td>\n",
" <td>8.173469</td>\n",
" <td>7.360998</td>\n",
" <td>7.899128</td>\n",
" <td>7.668647</td>\n",
" <td>9.483785</td>\n",
" <td>11.846274</td>\n",
" <td>9.143848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>4.153359</td>\n",
" <td>4.368691</td>\n",
" <td>4.320320</td>\n",
" <td>4.784488</td>\n",
" <td>4.429411</td>\n",
" <td>4.048702</td>\n",
" <td>4.131885</td>\n",
" <td>4.547273</td>\n",
" <td>4.425889</td>\n",
" <td>4.545985</td>\n",
" <td>...</td>\n",
" <td>4.516905</td>\n",
" <td>4.596316</td>\n",
" <td>4.187752</td>\n",
" <td>4.478969</td>\n",
" <td>4.309859</td>\n",
" <td>4.176227</td>\n",
" <td>4.356414</td>\n",
" <td>5.568482</td>\n",
" <td>5.570161</td>\n",
" <td>4.547440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5.064883</td>\n",
" <td>4.265396</td>\n",
" <td>4.796432</td>\n",
" <td>7.778202</td>\n",
" <td>4.424837</td>\n",
" <td>5.518264</td>\n",
" <td>4.346809</td>\n",
" <td>4.667569</td>\n",
" <td>4.214931</td>\n",
" <td>5.945018</td>\n",
" <td>...</td>\n",
" <td>6.669781</td>\n",
" <td>4.458947</td>\n",
" <td>4.778975</td>\n",
" <td>6.045139</td>\n",
" <td>5.961929</td>\n",
" <td>4.457785</td>\n",
" <td>4.843793</td>\n",
" <td>5.031419</td>\n",
" <td>7.130748</td>\n",
" <td>5.497614</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 915 columns</p>\n",
"</div>"
],
"text/plain": [
" 1321N1_CENTRAL_NERVOUS_SYSTEM 143B_BONE 22RV1_PROSTATE 2313287_STOMACH \\\n",
"1 5.899963 5.495630 4.915154 4.386444 \n",
"2 4.151092 4.867485 4.683452 3.770537 \n",
"9 7.937206 7.653808 10.551317 9.345134 \n",
"10 4.153359 4.368691 4.320320 4.784488 \n",
"12 5.064883 4.265396 4.796432 7.778202 \n",
"\n",
" 42MGBA_CENTRAL_NERVOUS_SYSTEM 5637_URINARY_TRACT 639V_URINARY_TRACT \\\n",
"1 8.018313 4.440586 7.108326 \n",
"2 3.992411 3.668579 3.643959 \n",
"9 8.158181 7.965484 7.525776 \n",
"10 4.429411 4.048702 4.131885 \n",
"12 4.424837 5.518264 4.346809 \n",
"\n",
" 647V_URINARY_TRACT 697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 769P_KIDNEY \\\n",
"1 4.974334 7.542390 4.769888 \n",
"2 3.799457 3.985218 3.708740 \n",
"9 8.514274 8.230191 8.323116 \n",
"10 4.547273 4.425889 4.545985 \n",
"12 4.667569 4.214931 5.945018 \n",
"\n",
" ... YAPC_PANCREAS YD10B_UPPER_AERODIGESTIVE_TRACT \\\n",
"1 ... 4.750698 4.899243 \n",
"2 ... 3.532178 4.184472 \n",
"9 ... 7.935024 7.825549 \n",
"10 ... 4.516905 4.596316 \n",
"12 ... 6.669781 4.458947 \n",
"\n",
" YD15_SALIVARY_GLAND YD38_UPPER_AERODIGESTIVE_TRACT \\\n",
"1 4.071518 4.630944 \n",
"2 3.778447 3.898891 \n",
"9 8.277187 8.173469 \n",
"10 4.187752 4.478969 \n",
"12 4.778975 6.045139 \n",
"\n",
" YD8_UPPER_AERODIGESTIVE_TRACT YH13_CENTRAL_NERVOUS_SYSTEM \\\n",
"1 4.617563 7.214694 \n",
"2 4.298302 4.583785 \n",
"9 7.360998 7.899128 \n",
"10 4.309859 4.176227 \n",
"12 5.961929 4.457785 \n",
"\n",
" YKG1_CENTRAL_NERVOUS_SYSTEM YMB1_BREAST ZR751_BREAST ZR7530_BREAST \n",
"1 5.955572 8.455509 7.726960 6.890627 \n",
"2 3.854023 4.372195 3.680397 4.555957 \n",
"9 7.668647 9.483785 11.846274 9.143848 \n",
"10 4.356414 5.568482 5.570161 4.547440 \n",
"12 4.843793 5.031419 7.130748 5.497614 \n",
"\n",
"[5 rows x 915 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ccle_exprs = pd.read_csv(root_dir+\"/preprocessed/exprs/GSE36133.BrainArray.RMAlog2Average.ENTREZID.Expr.tsv\", sep =\"\\t\")\n",
"samples_annotated = set(ccle_exprs.columns.values).intersection(set(df.index.values))\n",
"#df.loc[samples_annotated ,\"CCLE_name\"].to_dict()\n",
"ccle_exprs = ccle_exprs.loc[:,samples_annotated]\n",
"ccle_exprs.rename(df.loc[samples_annotated ,\"CCLE_name\"].to_dict(),axis=\"columns\",inplace=True)\n",
"ccle_exprs.sort_index(inplace = True)\n",
"ccle_exprs.sort_index(inplace = True,axis=1)\n",
"#ccle_exprs.to_csv(root_dir+\"/preprocessed/exprs/GSE36133.BrainArray.RMAlog2Average.ENTREZID.Expr_renamed.tsv\",sep =\"\\t\")\n",
"ccle_exprs.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"response = pd.read_csv(root_dir+\"/preprocessed/annotations/\"+\"CCLE_NP24.2009_Drug_data_2015.02.24.csv\",sep=\",\")\n",
"set(response[\"Compound\"].values)\n",
"response_9drugs = response.loc[response[\"Compound\"].isin([\"Paclitaxel\",\"Erlotinib\"]),:]\n",
"response_EGFR = response.loc[response[\"Compound\"].isin(['Erlotinib', 'Lapatinib', 'ZD-6474']),:]\n",
"response_9drugs.set_index(\"CCLE Cell Line Name\",inplace=True)\n",
"response_9drugs.index.name = \"CCLE_name\"\n",
"response_9drugs.to_csv(root_dir+\"/preprocessed/annotations/\"+\"CCLE.responses.Paclitaxel_Erlotinib.tsv\", sep = \"\\t\")\n",
"response_EGFR.set_index(\"CCLE Cell Line Name\",inplace=True)\n",
"response_EGFR.index.name = \"CCLE_name\"\n",
"response_EGFR.to_csv(root_dir+\"/preprocessed/annotations/\"+\"CCLE.responses.EGFRi.tsv\", sep = \"\\t\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": []
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{
"cell_type": "code",
"execution_count": null,
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"cell_type": "code",
"execution_count": null,
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"outputs": [],
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}
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Datasets
Models
