TCGA_analysis_BRCA_download

Download data using firehose_get



In [1]:

    
import os
import glob
import subprocess
import pandas

Use run data of 2015_02_04

CNV data

Download data

For this step, one needs the firehose_get binary from the Broad institute to be in the directory of the notebook files. Firehose_get can be downloaded here: https://confluence.broadinstitute.org/display/GDAC/Download



In [2]:

    
!./firehose_get -b -o BRCA.Merge_snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg.Level_3 stddata 2015_02_04 BRCA > BRCA.download.log
!mkdir ./BRCA
!tar xzf stddata__2015_02_04/BRCA/20150204/gdac.broadinstitute.org_BRCA.Merge_snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg.Level_3.2015020400.0.0.tar.gz -C ./BRCA/ 
!mv BRCA/gdac.broadinstitute.org_BRCA.Merge_snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg.Level_3.2015020400.0.0 BRCA/CNV

Split file into one segment file per sample



In [3]:

    
cnv_file = "BRCA/CNV/BRCA.snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg.seg.txt"
CNV_INPUT = open(cnv_file, "r")
header = CNV_INPUT.readline()
body = CNV_INPUT.readlines()
actual_sample = ""
SAMPLE = ""
for line in body:
    info = line.split()
    if (info[0] != actual_sample):
        actual_sample = info[0]
        if SAMPLE != "":
            SAMPLE.close()
        SAMPLE = open("BRCA/CNV/"+actual_sample+".txt","w")
        SAMPLE.write('\t'.join(header.split()[1:])+"\n")
        SAMPLE.write('\t'.join(info[1:])+"\n")
    else:
        SAMPLE.write('\t'.join(info[1:])+"\n")

Perform focal amplification calling

This will take long, since amplifications need to be checked for every of ~1000 samples; On my computer ~3h

Samples where CNV substraction removed an entire chromosome will throw an error here



In [4]:

    
!mkdir BRCA/FocalOutput
file_list = glob.glob("BRCA/CNV/TCGA*.txt")
for input_file in file_list:
  filename = os.path.basename(input_file)
#only use tumor files specified in the Barcode by TCGA-xx-xxxx-0xx-xxx-xxxxx-xx
  if filename[13] == '0':
     !cat FocalAmplifications_fromSNPArray_noChrY.R | R --slave --args $input_file BRCA/FocalOutput/$filename Breast 100 > tmp









    



Warnmeldung:
In scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings,  :
  Anzahl der gelesenen Daten ist kein Vielfaches der Anzahl der Spalten
Fehler in Ops.factor(genes_file$chrom, segments$Chromosome[i]) : 
  Levelmengen für Faktoren unterscheiden sich
Calls: which -> Ops.factor
Ausführung angehalten

Download Expression data



In [5]:

    
!./firehose_get -b -o RSEM_genes_normalized stddata 2015_02_04 BRCA >> BRCA.download.log



In [6]:

    
!tar xzf stddata__2015_02_04/BRCA/20150204/gdac.broadinstitute.org_BRCA.Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.Level_3.2015020400.0.0.tar.gz -C ./BRCA/
!mv BRCA/gdac.broadinstitute.org_BRCA.Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.Level_3.2015020400.0.0 BRCA/RNASeq

Split file into one expression file per sample



In [7]:

    
expression_data=pandas.io.parsers.read_csv("BRCA/RNASeq/BRCA.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.data.txt", header=0, skiprows=[1], sep="\t")
for column in expression_data.columns:
    if column == "Hybridization REF":
        continue
    SAMPLE = open("BRCA/RNASeq/"+column+".txt","w")
    SAMPLE.write("Gene\tRSEM normalized\n")
    column_count = len(expression_data.index)
    for i in range(0,column_count):
        SAMPLE.write(str(expression_data['Hybridization REF'][i])+"\t"+str(expression_data[column][i])+"\n")
    SAMPLE.close()

Download Somatic Mutation data



In [8]:

    
!./firehose_get -b -o Mutation_Packager_Calls stddata 2015_02_04 BRCA >> BRCA.download.log

Extract data



In [9]:

    
!tar xzf stddata__2015_02_04/BRCA/20150204/gdac.broadinstitute.org_BRCA.Mutation_Packager_Calls.Level_3.2015020400.0.0.tar.gz -C ./BRCA/
!mv BRCA/gdac.broadinstitute.org_BRCA.Mutation_Packager_Calls.Level_3.2015020400.0.0 BRCA/SomaticMutations

Download Clinical Data



In [10]:

    
!./firehose_get -b -o Clinical stddata 2015_02_04 BRCA >> BRCA.download.log

Extract data



In [11]:

    
!tar xzf stddata__2015_02_04/BRCA/20150204/gdac.broadinstitute.org_BRCA.Merge_Clinical.Level_1.2015020400.0.0.tar.gz -C ./BRCA/
!tar xzf stddata__2015_02_04/BRCA/20150204/gdac.broadinstitute.org_BRCA.Clinical_Pick_Tier1.Level_4.2015020400.0.0.tar.gz -C ./BRCA/
!mv BRCA/gdac.broadinstitute.org_BRCA.Merge_Clinical.Level_1.2015020400.0.0 BRCA/Clinical
!mv BRCA/gdac.broadinstitute.org_BRCA.Clinical_Pick_Tier1.Level_4.2015020400.0.0 BRCA/Clinical
!mv BRCA/Clinical/gdac.broadinstitute.org_BRCA.Clinical_Pick_Tier1.Level_4.2015020400.0.0/BRCA.clin.merged.picked.txt BRCA/Clinical/BRCA.clin.merged.picked.txt

Split file into one clinical file per sample



In [12]:

    
clinical_expand_data=pandas.io.parsers.read_csv("BRCA/Clinical/BRCA.clin.merged.txt", header=21, index_col=0, sep="\t")
clinical_picked_data=pandas.io.parsers.read_csv("BRCA/Clinical/BRCA.clin.merged.picked.txt", header=0, index_col=0, sep="\t")
for column in clinical_expand_data.columns:
    SAMPLE = open("BRCA/Clinical/"+str(column).upper()+".txt","w")
    for index in clinical_picked_data.index:
        SAMPLE.write(index+"\t"+str(clinical_picked_data.loc[[index],[column]].values[0,0])+"\n")
    for index in clinical_expand_data.index:
        SAMPLE.write(index+"\t"+str(clinical_expand_data.loc[[index],[column]].values[0,0])+"\n")
    SAMPLE.close()









    



/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py:1159: DtypeWarning: Columns (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070) have mixed types. Specify dtype option on import or set low_memory=False.
  data = self._reader.read(nrows)



In [12]: