Style-Sheet
Here we are downloading and processing most of the necissary data to run this analysis pipeline. I have set up a series of scripts to do this in an automated fashon in order to allow for reproduction of this study by others as well as for updating the results obtained here as more TCGA data is collected and reseased.
Downloading this data can take a considerable amount of time (~5 hours) and disk space (~45GB), be prepared.
We use the firehose_get script provided by the Broad to download the data, please see the firehose_get documentation for troubleshooting. As we are making using the Broad's initial processing pipeline and data formats we can not promise this initial code will not break upon future update that they make.
%pylab inline
Populating the interactive namespace from numpy and matplotlib
cd ../src/
/cellar/users/agross/TCGA_Code/TCGA/src
Change OUT_PATH to directory on your machine where you want to store the data
OUT_PATH = '../Data'
RUN_DATE = '2014_01_15'
CANCER = 'HNSC'
VERSION = 'all'
DESCRIPTION = '''Updating analysis for updated dataset.'''
PARAMETERS = {'min_patients' : 12,
'pathway_file' : '../Extra_Data/c2.cp.v3.0.symbols_edit.csv'
}
global_params = 'OUT_PATH\t{}\n'.format(OUT_PATH)
global_params += 'RUN_DATE\t{}\n'.format(RUN_DATE)
global_params += 'VERSION\t{}\n'.format(VERSION)
global_params += 'CANCER\t{}'.format(CANCER)
f = open('../global_params.txt', 'wb')
f.write(global_params)
f.close()
import pickle as pickle
import pandas as pd
import os as os
from Data.Containers import Run
from Data.Containers import get_run
from Data.Containers import Cancer
from Initialization.InitializeCN import initialize_cn
from Initialization.InitializeReal import initialize_real
from Initialization.InitializeMut import initialize_mut
from Initialization.PreprocessMethylation import process_meth
!curl http://gdac.broadinstitute.org/runs/code/firehose_get_latest.zip -o fh_get.zip
!unzip fh_get.zip
% Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 6542 100 6542 0 0 22697 0 --:--:-- --:--:-- --:--:-- 22715 Archive: fh_get.zip inflating: firehose_get
d = 'http://gdac.broadinstitute.org/runs/analyses__{}/ingested_data.tsv'.format(RUN_DATE)
tab = pd.read_table(d, sep='\n', header=None)
skip = tab[0].dropna().apply(lambda s: s.startswith('#'))
skip = list(skip[skip==True].index)
tab = pd.read_table(d, skiprows=skip, index_col=0).dropna()
cancers = tab[tab.Clinical>0].index[:-1]
Takes about 5 min to download data.
cancer_string = ' '.join(cancers)
!firehose_get -b analyses $RUN_DATE $cancer_string > tmp
!firehose_get -b -o miR_gene_expression stddata $RUN_DATE $cancer_string > tmp
!firehose_get -b -o RSEM_genes_normalized stddata $RUN_DATE $cancer_string > tmp
!firehose_get -b -o rppa stddata $RUN_DATE $cancer_string > tmp
!firehose_get -b -o clinical stddata $RUN_DATE $cancer_string > tmp
Takes about 25 min to download methylation data.
!firehose_get -b -o humanmethylation450 stddata $RUN_DATE HNSC
Validating run selection against Broad Institute website ... You've asked to download archives for the following tasks humanmethylation450 run against the disease cohorts HNSC from the stddata__2014_01_15 Firehose run. Attempting to retrieve data for Broad GDAC run stddata__2014_01_15 ... --2014-02-12 19:57:42-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/ 0K 100% 28.2M=0s 2014-02-12 19:57:42 ERROR 404: Not Found. --2014-02-12 19:57:42-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/ 0K 101K=0.4s --2014-02-12 19:57:42-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz' 0K ........ ........ ........ ........ ........ ........ 0% 858K 38m12s 3072K ........ ........ ........ ........ ........ ........ 0% 994K 35m31s 6144K ........ ........ ........ ........ ........ 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755712K ........ ........ ........ ........ ........ ........ 38% 991K 20m10s 758784K ........ ........ ........ ........ ........ ........ 38% 1009K 20m7s 761856K ........ ........ ........ ........ ........ ........ 38% 981K 20m4s 764928K ........ ........ ........ ........ ........ ........ 39% 996K 20m1s 768000K ........ ........ ........ ........ ........ ........ 39% 1011K 19m58s 771072K ........ ........ ........ ........ ........ ........ 39% 1005K 19m55s 774144K ........ ........ ........ ........ ........ ........ 39% 1.00M 19m51s 777216K ........ ........ ........ ........ ........ ........ 39% 970K 19m49s 780288K ........ ........ ........ ........ ........ ........ 39% 1012K 19m45s 783360K ........ ........ ........ ........ ........ ........ 39% 1021K 19m42s 786432K ........ ........ ........ ........ ........ ........ 40% 954K 19m39s 789504K ........ ........ ........ ........ ........ ........ 40% 969K 19m36s 792576K ........ ........ ........ ........ ........ ........ 40% 998K 19m33s 795648K ........ ........ ........ ........ ........ ........ 40% 996K 19m30s 798720K ........ ........ ........ ........ ........ ........ 40% 1019K 19m27s 801792K ........ ........ ........ ........ ........ ........ 40% 978K 19m24s 804864K ........ ........ ........ ........ ........ ........ 41% 1000K 19m21s 807936K ........ ........ ........ ........ ........ ........ 41% 1009K 19m18s 811008K ........ ........ ........ ........ ........ ........ 41% 972K 19m15s 814080K ........ ........ ........ ........ ........ ........ 41% 957K 19m12s 817152K ........ ........ ........ ........ ........ ........ 41% 985K 19m9s 820224K ........ ........ ........ ........ ........ ........ 41% 1008K 19m6s 823296K ........ ........ ........ ........ ........ ........ 41% 989K 19m3s 826368K ........ ........ ........ ........ ........ ........ 42% 998K 19m0s 829440K ........ ........ ........ ........ ........ ........ 42% 1021K 18m57s 832512K ........ ........ ........ ........ ........ ........ 42% 999K 18m54s 835584K ........ ........ ........ ........ ........ ........ 42% 989K 18m51s 838656K ........ ........ ........ ........ ........ ........ 42% 1012K 18m48s 841728K ........ ........ ........ ........ ........ ........ 42% 1006K 18m45s 844800K ........ ........ ........ ........ ........ ........ 43% 979K 18m42s 847872K ........ ........ ........ ........ ........ ........ 43% 1012K 18m38s 850944K ........ ........ ........ ........ ........ ........ 43% 1010K 18m35s 854016K ........ ........ ........ ........ ........ ........ 43% 998K 18m32s 857088K ........ ........ ........ ........ ........ ........ 43% 998K 18m29s 860160K ........ ........ ........ ........ ........ ........ 43% 1008K 18m26s 863232K ........ ........ ........ ........ ........ ........ 43% 1012K 18m23s 866304K ........ ........ ........ ........ ........ ........ 44% 1008K 18m20s 869376K ........ ........ ........ ........ ........ ........ 44% 1.02M 18m17s 872448K ........ ........ ........ ........ ........ ........ 44% 1023K 18m14s 875520K ........ ........ ........ ........ ........ ........ 44% 1006K 18m10s 878592K ........ ........ ........ ........ ........ ........ 44% 1009K 18m7s 881664K ........ ........ ........ ........ ........ ........ 44% 997K 18m4s 884736K ........ ........ ........ ........ ........ ........ 45% 1008K 18m1s 887808K ........ ........ ........ ........ ........ ........ 45% 971K 17m58s 890880K ........ ........ ........ ........ ........ ........ 45% 1021K 17m55s 893952K ........ ........ ........ ........ ........ ........ 45% 1000K 17m52s 897024K ........ ........ ........ ........ ........ ........ 45% 1023K 17m49s 900096K ........ ........ ........ ........ ........ ........ 45% 999K 17m46s 903168K ........ ........ ........ ........ ........ ........ 46% 1023K 17m43s 906240K ........ ........ ........ ........ ........ ........ 46% 960K 17m40s 909312K ........ ........ ........ ........ ........ ........ 46% 997K 17m37s 912384K ........ ........ ........ ........ ........ ........ 46% 1.01M 17m33s 915456K ........ ........ ........ ........ ........ ........ 46% 997K 17m30s 918528K ........ ........ ........ ........ ........ ........ 46% 1009K 17m27s 921600K ........ ........ ........ ........ ........ ........ 46% 999K 17m24s 924672K ........ ........ ........ ........ ........ ........ 47% 1009K 17m21s 927744K ........ ........ ........ ........ ........ ........ 47% 995K 17m18s 930816K ........ ........ ........ ........ ........ ........ 47% 1021K 17m15s 933888K ........ ........ ........ ........ ........ ........ 47% 953K 17m12s 936960K ........ ........ ........ ........ ........ ........ 47% 1000K 17m9s 940032K ........ ........ ........ ........ ........ ........ 47% 1024K 17m6s 943104K ........ ........ ........ ........ ........ ........ 48% 995K 17m3s 946176K ........ ........ ........ ........ ........ ........ 48% 997K 17m0s 949248K ........ ........ ........ ........ ........ ........ 48% 1021K 16m57s 952320K ........ ........ ........ ........ ........ ........ 48% 998K 16m53s 955392K ........ ........ ........ ........ ........ ........ 48% 992K 16m50s 958464K ........ ........ ........ ........ ........ ........ 48% 1022K 16m47s 961536K ........ ........ ........ ........ ........ ........ 48% 1024K 16m44s 964608K ........ ........ ........ ........ ........ ........ 49% 979K 16m41s 967680K ........ ........ ........ ........ ........ ........ 49% 996K 16m38s 970752K ........ ........ ........ ........ ........ ........ 49% 995K 16m35s 973824K ........ ........ ........ ........ ........ ........ 49% 1.00M 16m32s 976896K ........ ........ ........ ........ ........ ........ 49% 1023K 16m29s 979968K ........ ........ ........ ........ ........ ........ 49% 994K 16m26s 983040K ........ ........ ........ ........ ........ ........ 50% 972K 16m23s 986112K ........ ........ ........ ........ ........ ........ 50% 997K 16m20s 989184K ........ ........ ........ ........ ........ ........ 50% 970K 16m17s 992256K ........ ........ ........ ........ ........ ........ 50% 1.03M 16m13s 995328K ........ ........ ........ ........ ........ ........ 50% 997K 16m10s 998400K ........ ........ ........ ........ ........ ........ 50% 996K 16m7s 1001472K ........ ........ ........ ........ ........ ........ 51% 945K 16m4s 1004544K ........ ........ ........ ........ ........ ........ 51% 985K 16m1s 1007616K ........ ........ ........ ........ ........ ........ 51% 996K 15m58s 1010688K ........ ........ ........ ........ ........ ........ 51% 1010K 15m55s 1013760K ........ ........ ........ ........ ........ ........ 51% 1008K 15m52s 1016832K ........ ........ ........ ........ ........ ........ 51% 973K 15m49s 1019904K ........ ........ ........ ........ ........ ........ 51% 1023K 15m46s 1022976K ........ ........ ........ ........ ........ ........ 52% 972K 15m43s 1026048K ........ ........ ........ ........ ........ ........ 52% 1020K 15m40s 1029120K ........ ........ ........ ........ ........ ........ 52% 1000K 15m37s 1032192K ........ ........ ........ ........ ........ ........ 52% 992K 15m34s 1035264K ........ ........ ........ ........ ........ ........ 52% 996K 15m31s 1038336K ........ ........ ........ ........ ........ ........ 52% 1021K 15m28s 1041408K ........ ........ ........ ........ ........ ........ 53% 1018K 15m25s 1044480K ........ ........ ........ ........ ........ ........ 53% 1.00M 15m21s 1047552K ........ ........ ........ ........ ........ ........ 53% 997K 15m18s 1050624K ........ ........ ........ ........ ........ ........ 53% 1.02M 15m15s 1053696K ........ ........ ........ ........ ........ ........ 53% 1.00M 15m12s 1056768K ........ ........ ........ ........ ........ ........ 53% 1013K 15m9s 1059840K ........ ........ ........ ........ ........ ........ 53% 1.01M 15m6s 1062912K ........ ........ ........ ........ ........ ........ 54% 1.00M 15m3s 1065984K ........ ........ ........ ........ ........ ........ 54% 1012K 14m59s 1069056K ........ ........ ........ ........ ........ ........ 54% 1022K 14m56s 1072128K ........ ........ ........ ........ ........ ........ 54% 990K 14m53s 1075200K ........ ........ ........ ........ ........ ........ 54% 1000K 14m50s 1078272K ........ ........ ........ ........ ........ ........ 54% 1017K 14m47s 1081344K ........ ........ ........ ........ ........ ........ 55% 993K 14m44s 1084416K ........ ........ ........ ........ ........ ........ 55% 1022K 14m41s 1087488K ........ ........ ........ ........ ........ ........ 55% 996K 14m38s 1090560K ........ ........ ........ ........ ........ ........ 55% 1.02M 14m35s 1093632K ........ ........ ........ ........ ........ ........ 55% 997K 14m32s 1096704K ........ ........ ........ ........ ........ ........ 55% 996K 14m29s 1099776K ........ ........ ........ ........ ........ ........ 56% 995K 14m26s 1102848K ........ ........ ........ ........ ........ ........ 56% 994K 14m23s 1105920K ........ ........ ........ ........ ........ ........ 56% 1021K 14m19s 1108992K ........ ........ ........ ........ ........ ........ 56% 998K 14m16s 1112064K ........ ........ ........ ........ ........ ........ 56% 1021K 14m13s 1115136K ........ ........ ........ ........ ........ ........ 56% 996K 14m10s 1118208K ........ ........ ........ ........ ........ ........ 56% 1022K 14m7s 1121280K ........ ........ ........ ........ ........ ........ 57% 994K 14m4s 1124352K ........ ........ ........ ........ ........ ........ 57% 1023K 14m1s 1127424K ........ ........ ........ ........ ........ ........ 57% 996K 13m58s 1130496K ........ ........ ........ ........ ........ ........ 57% 991K 13m55s 1133568K ........ ........ ........ ........ ........ ........ 57% 994K 13m52s 1136640K ........ ........ ........ ........ ........ ........ 57% 1022K 13m49s 1139712K ........ ........ ........ ........ ........ ........ 58% 1022K 13m45s 1142784K ........ ........ ........ ........ ........ ........ 58% 1022K 13m42s 1145856K ........ ........ ........ ........ ........ ........ 58% 1006K 13m39s 1148928K ........ ........ ........ ........ ........ ........ 58% 1.02M 13m36s 1152000K ........ ........ ........ ........ ........ ........ 58% 1023K 13m33s 1155072K ........ ........ ........ ........ ........ ........ 58% 1002K 13m30s 1158144K ........ ........ ........ ........ ........ ........ 58% 1018K 13m27s 1161216K ........ ........ ........ ........ ........ ........ 59% 997K 13m24s 1164288K ........ ........ ........ ........ ........ ........ 59% 1004K 13m21s 1167360K ........ ........ ........ ........ ........ ........ 59% 1015K 13m18s 1170432K ........ ........ ........ ........ ........ ........ 59% 983K 13m15s 1173504K ........ ........ ........ ........ ........ ........ 59% 1008K 13m12s 1176576K ........ ........ ........ ........ ........ ........ 59% 1022K 13m8s 1179648K ........ ........ ........ ........ ........ ........ 60% 1023K 13m5s 1182720K ........ ........ ........ ........ ........ ........ 60% 1021K 13m2s 1185792K ........ ........ ........ ........ ........ ........ 60% 1.01M 12m59s 1188864K ........ ........ ........ ........ ........ ........ 60% 1007K 12m56s 1191936K ........ ........ ........ ........ ........ ........ 60% 980K 12m53s 1195008K ........ ........ ........ ........ ........ ........ 60% 990K 12m50s 1198080K ........ ........ ........ ........ ........ ........ 60% 1019K 12m47s 1201152K ........ ........ ........ ........ ........ ........ 61% 1005K 12m44s 1204224K ........ ........ ........ ........ ........ ........ 61% 1.01M 12m41s 1207296K ........ ........ ........ ........ ........ ........ 61% 1009K 12m38s 1210368K ........ ........ ........ ........ ........ ........ 61% 1010K 12m34s 1213440K ........ ........ ........ ........ ........ ........ 61% 994K 12m31s 1216512K ........ ........ ........ ........ ........ ........ 61% 1.00M 12m28s 1219584K ........ ........ ........ ........ ........ ........ 62% 1023K 12m25s 1222656K ........ ........ ........ ........ ........ ........ 62% 1021K 12m22s 1225728K ........ ........ ........ ........ ........ ........ 62% 998K 12m19s 1228800K ........ ........ ........ ........ ........ ........ 62% 1022K 12m16s 1231872K ........ ........ ........ ........ ........ ........ 62% 999K 12m13s 1234944K ........ ........ ........ ........ ........ ........ 62% 968K 12m10s 1238016K ........ ........ ........ ........ ........ ........ 63% 991K 12m7s 1241088K ........ ........ ........ ........ ........ ........ 63% 1011K 12m4s 1244160K ........ ........ ........ ........ ........ ........ 63% 975K 12m1s 1247232K ........ ........ ........ ........ ........ ........ 63% 1023K 11m58s 1250304K ........ ........ ........ ........ ........ ........ 63% 1023K 11m55s 1253376K ........ ........ ........ ........ ........ ........ 63% 1022K 11m51s 1256448K ........ ........ ........ ........ ........ ........ 63% 964K 11m48s 1259520K ........ ........ ........ ........ ........ ........ 64% 995K 11m45s 1262592K ........ ........ ........ ........ ........ ........ 64% 1020K 11m42s 1265664K ........ ........ ........ ........ ........ ........ 64% 993K 11m39s 1268736K ........ ........ ........ ........ ........ ........ 64% 970K 11m36s 1271808K ........ ........ ........ ........ ........ ........ 64% 995K 11m33s 1274880K ........ ........ ........ ........ ........ ........ 64% 1.02M 11m30s 1277952K ........ ........ ........ ........ ........ ........ 65% 977K 11m27s 1281024K ........ ........ ........ ........ ........ ........ 65% 1014K 11m24s 1284096K ........ ........ ........ ........ ........ ........ 65% 1022K 11m21s 1287168K ........ ........ ........ ........ ........ ........ 65% 1007K 11m18s 1290240K ........ ........ ........ ........ ........ ........ 65% 985K 11m15s 1293312K ........ ........ ........ ........ ........ ........ 65% 1.03M 11m12s 1296384K ........ ........ ........ ........ ........ ........ 65% 1008K 11m8s 1299456K ........ ........ ........ ........ ........ ........ 66% 1013K 11m5s 1302528K ........ ........ ........ ........ ........ ........ 66% 977K 11m2s 1305600K ........ ........ ........ ........ ........ ........ 66% 971K 10m59s 1308672K ........ ........ ........ ........ ........ ........ 66% 1013K 10m56s 1311744K ........ ........ ........ ........ ........ ........ 66% 1022K 10m53s 1314816K ........ ........ ........ ........ ........ ........ 66% 1021K 10m50s 1317888K ........ ........ ........ ........ ........ ........ 67% 971K 10m47s 1320960K ........ ........ ........ ........ ........ ........ 67% 1009K 10m44s 1324032K ........ ........ ........ ........ ........ ........ 67% 1024K 10m41s 1327104K ........ ........ ........ ........ ........ ........ 67% 1007K 10m38s 1330176K ........ ........ ........ ........ ........ ........ 67% 1010K 10m35s 1333248K ........ ........ ........ ........ ........ ........ 67% 995K 10m32s 1336320K ........ ........ ........ ........ ........ ........ 68% 968K 10m29s 1339392K ........ ........ ........ ........ ........ ........ 68% 1022K 10m26s 1342464K ........ ........ ........ ........ ........ ........ 68% 1019K 10m22s 1345536K ........ ........ ........ ........ ........ ........ 68% 996K 10m19s 1348608K ........ ........ ........ ........ ........ ........ 68% 1017K 10m16s 1351680K ........ ........ ........ ........ ........ ........ 68% 1020K 10m13s 1354752K ........ ........ ........ ........ ........ ........ 68% 995K 10m10s 1357824K ........ ........ ........ ........ ........ ........ 69% 967K 10m7s 1360896K ........ ........ ........ ........ ........ ........ 69% 1018K 10m4s 1363968K ........ ........ ........ ........ ........ ........ 69% 994K 10m1s 1367040K ........ ........ ........ ........ ........ ........ 69% 972K 9m58s 1370112K ........ ........ ........ ........ ........ ........ 69% 1021K 9m55s 1373184K ........ ........ ........ ........ ........ ........ 69% 964K 9m52s 1376256K ........ ........ ........ ........ ........ ........ 70% 996K 9m49s 1379328K ........ ........ ........ ........ ........ ........ 70% 1022K 9m46s 1382400K ........ ........ ........ ........ ........ ........ 70% 996K 9m43s 1385472K ........ ........ ........ ........ ........ ........ 70% 991K 9m40s 1388544K ........ ........ ........ ........ ........ ........ 70% 997K 9m37s 1391616K ........ ........ ........ ........ ........ ........ 70% 995K 9m33s 1394688K ........ ........ ........ ........ ........ ........ 70% 1022K 9m30s 1397760K ........ ........ ........ ........ ........ ........ 71% 1021K 9m27s 1400832K ........ ........ ........ ........ ........ ........ 71% 994K 9m24s 1403904K ........ ........ ........ ........ ........ ........ 71% 1020K 9m21s 1406976K ........ ........ ........ ........ ........ ........ 71% 970K 9m18s 1410048K ........ ........ ........ ........ ........ ........ 71% 971K 9m15s 1413120K ........ ........ ........ ........ ........ ........ 71% 1009K 9m12s 1416192K ........ ........ ........ ........ ........ ........ 72% 968K 9m9s 1419264K ........ ........ ........ ........ ........ ........ 72% 1021K 9m6s 1422336K ........ ........ ........ ........ ........ ........ 72% 993K 9m3s 1425408K ........ ........ ........ ........ ........ ........ 72% 985K 9m0s 1428480K ........ ........ ........ ........ ........ ........ 72% 984K 8m57s 1431552K ........ ........ ........ ........ ........ ........ 72% 994K 8m54s 1434624K ........ ........ ........ ........ ........ ........ 73% 968K 8m51s 1437696K ........ ........ ........ ........ ........ ........ 73% 997K 8m48s 1440768K ........ ........ ........ ........ ........ ........ 73% 993K 8m45s 1443840K ........ ........ ........ ........ ........ ........ 73% 995K 8m42s 1446912K ........ ........ ........ ........ ........ ........ 73% 1021K 8m38s 1449984K ........ ........ ........ ........ ........ ........ 73% 995K 8m35s 1453056K ........ ........ ........ ........ ........ ........ 73% 972K 8m32s 1456128K ........ ........ ........ ........ ........ ........ 74% 1021K 8m29s 1459200K ........ ........ ........ ........ ........ ........ 74% 1022K 8m26s 1462272K ........ ........ ........ ........ ........ ........ 74% 980K 8m23s 1465344K ........ ........ ........ ........ ........ ........ 74% 1010K 8m20s 1468416K ........ ........ ........ ........ ........ ........ 74% 970K 8m17s 1471488K ........ ........ ........ ........ ........ ........ 74% 997K 8m14s 1474560K ........ ........ ........ ........ ........ ........ 75% 971K 8m11s 1477632K ........ ........ ........ ........ ........ ........ 75% 1010K 8m8s 1480704K ........ ........ ........ ........ ........ ........ 75% 997K 8m5s 1483776K ........ ........ ........ ........ ........ ........ 75% 1007K 8m2s 1486848K ........ ........ ........ ........ ........ ........ 75% 1022K 7m59s 1489920K ........ ........ ........ ........ ........ ........ 75% 971K 7m56s 1492992K ........ ........ ........ ........ ........ ........ 75% 997K 7m52s 1496064K ........ ........ ........ ........ ........ ........ 76% 921K 7m50s 1499136K ........ ........ ........ ........ ........ ........ 76% 972K 7m46s 1502208K ........ ........ ........ ........ ........ ........ 76% 1019K 7m43s 1505280K ........ ........ ........ ........ ........ ........ 76% 997K 7m40s 1508352K ........ ........ ........ ........ ........ ........ 76% 996K 7m37s 1511424K ........ ........ ........ ........ ........ ........ 76% 996K 7m34s 1514496K ........ ........ ........ ........ ........ ........ 77% 969K 7m31s 1517568K ........ ........ ........ ........ ........ ........ 77% 1022K 7m28s 1520640K ........ ........ ........ ........ ........ ........ 77% 969K 7m25s 1523712K ........ ........ ........ ........ ........ ........ 77% 971K 7m22s 1526784K ........ ........ ........ ........ ........ ........ 77% 996K 7m19s 1529856K ........ ........ ........ ........ ........ ........ 77% 997K 7m16s 1532928K ........ ........ ........ ........ ........ ........ 78% 980K 7m13s 1536000K ........ ........ ........ ........ ........ ........ 78% 984K 7m10s 1539072K ........ ........ ........ ........ ........ ........ 78% 1001K 7m7s 1542144K ........ ........ ........ ........ ........ ........ 78% 993K 7m4s 1545216K ........ ........ ........ ........ ........ ........ 78% 998K 7m1s 1548288K ........ ........ ........ ........ ........ ........ 78% 972K 6m58s 1551360K ........ ........ ........ ........ ........ ........ 78% 1.00M 6m54s 1554432K ........ ........ ........ ........ ........ ........ 79% 996K 6m51s 1557504K ........ ........ ........ ........ ........ ........ 79% 998K 6m48s 1560576K ........ ........ ........ ........ ........ ........ 79% 979K 6m45s 1563648K ........ ........ ........ ........ ........ ........ 79% 989K 6m42s 1566720K ........ ........ ........ ........ ........ ........ 79% 708K 6m39s 1569792K ........ ........ ........ ........ ........ ........ 79% 919K 6m36s 1572864K ........ ........ ........ ........ ........ ........ 80% 998K 6m33s 1575936K ........ ........ ........ ........ ........ ........ 80% 994K 6m30s 1579008K ........ ........ ........ ........ ........ ........ 80% 997K 6m27s 1582080K ........ ........ ........ ........ ........ ........ 80% 997K 6m24s 1585152K ........ ........ ........ ........ ........ ........ 80% 1.00M 6m21s 1588224K ........ ........ ........ ........ ........ ........ 80% 1023K 6m18s 1591296K ........ ........ ........ ........ ........ ........ 80% 994K 6m15s 1594368K ........ ........ ........ ........ ........ ........ 81% 995K 6m12s 1597440K ........ ........ ........ ........ ........ ........ 81% 1014K 6m9s 1600512K ........ ........ ........ ........ ........ ........ 81% 971K 6m6s 1603584K ........ ........ ........ ........ ........ ........ 81% 985K 6m3s 1606656K ........ ........ ........ ........ ........ ........ 81% 1000K 6m0s 1609728K ........ ........ ........ ........ ........ ........ 81% 1012K 5m56s 1612800K ........ ........ ........ ........ ........ ........ 82% 994K 5m53s 1615872K ........ ........ ........ ........ ........ ........ 82% 971K 5m50s 1618944K ........ ........ ........ ........ ........ ........ 82% 1013K 5m47s 1622016K ........ ........ ........ ........ ........ ........ 82% 1015K 5m44s 1625088K ........ ........ ........ ........ ........ ........ 82% 1008K 5m41s 1628160K ........ ........ ........ ........ ........ ........ 82% 994K 5m38s 1631232K ........ ........ ........ ........ ........ ........ 82% 1013K 5m35s 1634304K ........ ........ ........ ........ ........ ........ 83% 967K 5m32s 1637376K ........ ........ ........ ........ ........ ........ 83% 1020K 5m29s 1640448K ........ ........ ........ ........ ........ ........ 83% 1024K 5m26s 1643520K ........ ........ ........ ........ ........ ........ 83% 997K 5m23s 1646592K ........ ........ ........ ........ ........ ........ 83% 998K 5m20s 1649664K ........ ........ ........ ........ ........ ........ 83% 982K 5m17s 1652736K ........ ........ ........ ........ ........ ........ 84% 993K 5m13s 1655808K ........ ........ ........ ........ ........ ........ 84% 997K 5m10s 1658880K ........ ........ ........ ........ ........ ........ 84% 1015K 5m7s 1661952K ........ ........ ........ ........ ........ ........ 84% 1006K 5m4s 1665024K ........ ........ ........ ........ ........ ........ 84% 972K 5m1s 1668096K ........ ........ ........ ........ ........ ........ 84% 1016K 4m58s 1671168K ........ ........ ........ ........ ........ ........ 85% 986K 4m55s 1674240K ........ ........ ........ ........ ........ ........ 85% 997K 4m52s 1677312K ........ ........ ........ ........ ........ ........ 85% 997K 4m49s 1680384K ........ ........ ........ ........ ........ ........ 85% 997K 4m46s 1683456K ........ ........ ........ ........ ........ ........ 85% 1015K 4m43s 1686528K ........ ........ ........ ........ ........ ........ 85% 999K 4m40s 1689600K ........ ........ ........ ........ ........ ........ 85% 996K 4m37s 1692672K ........ ........ ........ ........ ........ ........ 86% 1014K 4m34s 1695744K ........ ........ ........ ........ ........ ........ 86% 1004K 4m30s 1698816K ........ ........ ........ ........ ........ ........ 86% 986K 4m27s 1701888K ........ ........ ........ ........ ........ ........ 86% 971K 4m24s 1704960K ........ ........ ........ ........ ........ ........ 86% 995K 4m21s 1708032K ........ ........ ........ ........ ........ ........ 86% 1024K 4m18s 1711104K ........ ........ ........ ........ ........ ........ 87% 1014K 4m15s 1714176K ........ ........ ........ ........ ........ ........ 87% 997K 4m12s 1717248K ........ ........ ........ ........ ........ ........ 87% 1020K 4m9s 1720320K ........ ........ ........ ........ ........ ........ 87% 995K 4m6s 1723392K ........ ........ ........ ........ ........ ........ 87% 1.00M 4m3s 1726464K ........ ........ ........ ........ ........ ........ 87% 997K 4m0s 1729536K ........ ........ ........ ........ ........ ........ 87% 988K 3m57s 1732608K ........ ........ ........ ........ ........ ........ 88% 1018K 3m54s 1735680K ........ ........ ........ ........ ........ ........ 88% 1010K 3m50s 1738752K ........ ........ ........ ........ ........ ........ 88% 992K 3m47s 1741824K ........ ........ ........ ........ ........ ........ 88% 1012K 3m44s 1744896K ........ ........ ........ ........ ........ ........ 88% 990K 3m41s 1747968K ........ ........ ........ ........ ........ ........ 88% 1005K 3m38s 1751040K ........ ........ ........ ........ ........ ........ 89% 999K 3m35s 1754112K ........ ........ ........ ........ ........ ........ 89% 1021K 3m32s 1757184K ........ ........ ........ ........ ........ ........ 89% 994K 3m29s 1760256K ........ ........ ........ ........ ........ ........ 89% 966K 3m26s 1763328K ........ ........ ........ ........ ........ ........ 89% 990K 3m23s 1766400K ........ ........ ........ ........ ........ ........ 89% 996K 3m20s 1769472K ........ ........ ........ ........ ........ ........ 90% 998K 3m17s 1772544K ........ ........ ........ ........ ........ ........ 90% 996K 3m14s 1775616K ........ ........ ........ ........ ........ ........ 90% 971K 3m11s 1778688K ........ ........ ........ ........ ........ ........ 90% 1024K 3m7s 1781760K ........ ........ ........ ........ ........ ........ 90% 972K 3m4s 1784832K ........ ........ ........ ........ ........ ........ 90% 970K 3m1s 1787904K ........ ........ ........ ........ ........ ........ 90% 971K 2m58s 1790976K ........ ........ ........ ........ ........ ........ 91% 966K 2m55s 1794048K ........ ........ ........ ........ ........ ........ 91% 996K 2m52s 1797120K ........ ........ ........ ........ ........ ........ 91% 996K 2m49s 1800192K ........ ........ ........ ........ ........ ........ 91% 995K 2m46s 1803264K ........ ........ ........ ........ ........ ........ 91% 997K 2m43s 1806336K ........ ........ ........ ........ ........ ........ 91% 998K 2m40s 1809408K ........ ........ ........ ........ ........ ........ 92% 997K 2m37s 1812480K ........ ........ ........ ........ ........ ........ 92% 1023K 2m34s 1815552K ........ ........ ........ ........ ........ ........ 92% 998K 2m31s 1818624K ........ ........ ........ ........ ........ ........ 92% 995K 2m28s 1821696K ........ ........ ........ ........ ........ ........ 92% 997K 2m24s 1824768K ........ ........ ........ ........ ........ ........ 92% 975K 2m21s 1827840K ........ ........ ........ ........ ........ ........ 92% 997K 2m18s 1830912K ........ ........ ........ ........ ........ ........ 93% 995K 2m15s 1833984K ........ ........ ........ ........ ........ ........ 93% 996K 2m12s 1837056K ........ ........ ........ ........ ........ ........ 93% 1001K 2m9s 1840128K ........ ........ ........ ........ ........ ........ 93% 1023K 2m6s 1843200K ........ ........ ........ ........ ........ ........ 93% 1.00M 2m3s 1846272K ........ ........ ........ ........ ........ ........ 93% 1020K 2m0s 1849344K ........ ........ ........ ........ ........ ........ 94% 1.00M 1m57s 1852416K ........ ........ ........ ........ ........ ........ 94% 1001K 1m54s 1855488K ........ ........ ........ ........ ........ ........ 94% 964K 1m51s 1858560K ........ ........ ........ ........ ........ ........ 94% 1018K 1m48s 1861632K ........ ........ ........ ........ ........ ........ 94% 1.00M 1m45s 1864704K ........ ........ ........ ........ ........ ........ 94% 1020K 1m41s 1867776K ........ ........ ........ ........ ........ ........ 95% 1023K 98s 1870848K ........ ........ ........ ........ ........ ........ 95% 996K 95s 1873920K ........ ........ ........ ........ ........ ........ 95% 1019K 92s 1876992K ........ ........ ........ ........ ........ ........ 95% 1001K 89s 1880064K ........ ........ ........ ........ ........ ........ 95% 1020K 86s 1883136K ........ ........ ........ ........ ........ ........ 95% 997K 83s 1886208K ........ ........ ........ ........ ........ ........ 95% 974K 80s 1889280K ........ ........ ........ ........ ........ ........ 96% 973K 77s 1892352K ........ ........ ........ ........ ........ ........ 96% 998K 74s 1895424K ........ ........ ........ ........ ........ ........ 96% 987K 71s 1898496K ........ ........ ........ ........ ........ ........ 96% 1021K 68s 1901568K ........ ........ ........ ........ ........ ........ 96% 973K 65s 1904640K ........ ........ ........ ........ ........ ........ 96% 1.00M 61s 1907712K ........ ........ ........ ........ ........ ........ 97% 1.03M 58s 1910784K ........ ........ ........ ........ ........ ........ 97% 947K 55s 1913856K ........ ........ ........ ........ ........ ........ 97% 710K 52s 1916928K ........ ........ ........ ........ ........ ........ 97% 987K 49s 1920000K ........ ........ ........ ........ ........ ........ 97% 1019K 46s 1923072K ........ ........ ........ ........ ........ ........ 97% 969K 43s 1926144K ........ ........ ........ ........ ........ ........ 97% 996K 40s 1929216K ........ ........ ........ ........ ........ ........ 98% 986K 37s 1932288K ........ ........ ........ ........ ........ ........ 98% 996K 34s 1935360K ........ ........ ........ ........ ........ ........ 98% 1.01M 31s 1938432K ........ ........ ........ ........ ........ ........ 98% 997K 28s 1941504K ........ ........ ........ ........ ........ ........ 98% 998K 25s 1944576K ........ ........ ........ ........ ........ ........ 98% 1019K 22s 1947648K ........ ........ ........ ........ ........ ........ 99% 999K 18s 1950720K ........ ........ ........ ........ ........ ........ 99% 971K 15s 1953792K ........ ........ ........ ........ ........ ........ 99% 1004K 12s 1956864K ........ ........ ........ ........ ........ ........ 99% 995K 9s 1959936K ........ ........ ........ ........ ........ ........ 99% 994K 6s 1963008K ........ ........ ........ ........ ........ ........ 99% 997K 3s 1966080K ........ ........ ........ ........ ........ ........ 99% 997K 0s 1969152K 100% 7.69M=32m51s --2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz.md5 Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz.md5' 0K 100% 18.8M=0s --2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz' 0K 100% 131M=0s --2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz.md5 Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz.md5' 0K 100% 28.0M=0s --2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz' 0K 100% 548M=0s --2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz.md5 Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz.md5' 0K 100% 15.5M=0s FINISHED --2014-02-12 20:30:34-- Total wall clock time: 32m 53s Downloaded: 8 files, 1.9G in 32m 52s (999 KB/s) Now performing post-processing on retrieved files ...
No going back from here, so I would check your data to make sure everything got downloaded correctly.
!rm fh_get.zip
!rm firehose_get
if not os.path.isdir(OUT_PATH):
os.makedirs(OUT_PATH)
analyses_folder = 'analyses__' + RUN_DATE
!mv $analyses_folder {OUT_PATH + '/' + analyses_folder}
stddata_folder = 'stddata__' + RUN_DATE
!mv $stddata_folder {OUT_PATH + '/' + stddata_folder}
from Initialization.ProcessFirehose import process_all_cancers
process_all_cancers(OUT_PATH, RUN_DATE)
Get rid of all of the downloaded zip files that we processed
!rm -rf {OUT_PATH + '/' + stddata_folder}
!rm -rf {OUT_PATH + '/' + analyses_folder}
ls
Data/ Figures/ Initialization/ Processing/ Stats/ tmp
data_path = '{}/Firehose__{}/'.format(OUT_PATH, RUN_DATE)
result_path = data_path + 'ucsd_analyses/'
cancer_codes = pd.read_table('../Extra_Data/diseaseStudy.txt',
index_col=0, squeeze=True)
run_dir = 'http://gdac.broadinstitute.org/runs'
f = '{}/analyses__{}/ingested_data.tsv'.format(run_dir, RUN_DATE)
sample_matrix = pd.read_table(f, index_col=0).dropna()
sample_matrix = sample_matrix.ix[[c for c in sample_matrix.index if
c not in ['PANCAN12', 'COADREAD','Totals']]]
run = Run(RUN_DATE, VERSION, data_path, result_path, PARAMETERS,
cancer_codes, sample_matrix, DESCRIPTION)
run.save()
run
Run object for TCGA Analysis Firehose run date: 2014_01_15 Code version: all Comment: Updating analysis for updated dataset.
def init(c, run):
try:
cancer_obj = Cancer(c, run)
cancer_obj.initialize_data(run, save=True)
except:
print c + '\t' + 'all'
try:
initialize_real(c, run.report_path, 'mRNASeq',
create_meta_features=True)
except:
print c + '\t' + 'mRNASeq'
try:
initialize_real(c, run.report_path, 'RPPA',
create_meta_features=True, create_real_features=False)
except:
print c + '\t' + 'RPPA'
try:
initialize_real(c, run.report_path, 'miRNASeq',
create_meta_features=False)
except:
print c + '\t' + 'miRNASeq'
try:
initialize_cn(c, run.report_path, 'CN_broad')
except:
print c + '\t' + 'CN'
try:
initialize_mut(c, run.report_path, create_meta_features=True);
except:
print c + '\t' + 'mut'
for cancer in run.cancers:
init(cancer, run)
ACC mRNASeq ACC RPPA ACC miRNASeq
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)
CESC RPPA DLBC mRNASeq DLBC RPPA DLBC miRNASeq DLBC mut ESCA mRNASeq ESCA RPPA ESCA mut GBM miRNASeq
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388,1390,1391,1393,1394,1396,1397,1399,1400,1402,1403,1405,1406,1408,1409,1411,1412,1414,1415,1417,1418,1420,1421,1423,1424,1426,1427,1429,1430,1432,1433,1435,1436,1438,1439,1441,1442,1444,1445,1447,1448,1450,1451,1453,1454,1456,1457,1459,1460,1462,1463,1465,1466,1468,1469,1471,1472,1474,1475,1477,1478,1480,1481,1483,1484,1486,1487,1489,1490,1492,1493,1495,1496,1498,1499,1501,1502,1504,1505,1507,1508,1510,1511,1513,1514,1516,1517,1519,1520,1522,1523,1525,1526,1528,1529,1531,1532,1534,1535,1537,1538,1540,1541,1543,1544,1546,1547,1549,1550,1552,1553,1555,1556,1558,1559,1561,1562,1564,1565,1567,1568,1570,1571,1573,1574,1576,1577,1579,1580,1582,1583,1585,1586,1588,1589,1591,1592,1594,1595,1597,1598,1600,1601,1603,1604,1606,1607,1609,1610,1612,1613,1615,1616,1618,1619,1621,1622,1624,1625,1627,1628,1630,1631,1633,1634,1636,1637,1639,1640,1642,1643,1645,1646,1648,1649,1651,1652,1654,1655,1657,1658,1660,1661,1663,1664,1666,1667,1669,1670,1672,1673,1675,1676,1678,1679,1681,1682,1684,1685,1687,1688,1690,1691,1693,1694,1696,1697,1699,1700,1702,1703,1705,1706,1708,1709,1711,1712,1714,1715,1717,1718,1720,1721,1723,1724,1726,1727,1729,1730,1732,1733,1735,1736,1738,1739,1741,1742,1744,1745,1747,1748,1750,1751,1753,1754,1756,1757,1759,1760,1762,1763,1765,1766,1768,1769,1771,1772,1774,1775,1777,1778,1780,1781,1783,1784,1786,1787,1789,1790,1792,1793,1795,1796,1798,1799,1801,1802,1804,1805,1807,1808,1810,1811,1813,1814,1816,1817,1819,1820,1822,1823,1825,1826,1828,1829,1831,1832,1834,1835,1837,1838,1840,1841,1843,1844,1846,1847,1849,1850,1852,1853,1855,1856,1858,1859,1861,1862,1864,1865,1867,1868,1870,1871,1873,1874,1876,1877,1879,1880,1882,1883,1885,1886,1888,1889,1891,1892,1894,1895,1897,1898,1900,1901,1903,1904,1906,1907,1909,1910,1912,1913,1915,1916,1918,1919,1921,1922,1924,1925,1927,1928,1930,1931,1933,1934,1936,1937,1939,1940,1942,1943,1945,1946,1948,1949,1951,1952,1954,1955,1957,1958,1960,1961,1963,1964,1966,1967,1969,1970,1972,1973,1975,1976,1978,1979,1981,1982,1984,1985,1987,1988,1990,1991,1993,1994,1996,1997,1999,2000,2002,2003,2005,2006,2008,2009,2011,2012,2014,2015,2017,2018,2020,2021,2023,2024,2026,2027,2029,2030,2032,2033,2035,2036,2038,2039,2041,2042,2044,2045,2047,2048,2050,2051,2053,2054,2056,2057,2059,2060,2062,2063,2065,2066,2068,2069,2071,2072,2074,2075,2077,2078,2080,2081,2083,2084,2086,2087,2089,2090,2092,2093,2095,2096,2098,2099,2101,2102,2104,2105,2107,2108,2110,2111,2113,2114,2116,2117,2119,2120,2122,2123,2125,2126,2128,2129,2131,2132,2134,2135,2137,2138,2140,2141,2143,2144,2146,2147,2149,2150,2152,2153,2155,2156,2158,2159,2161,2162,2164,2165,2167,2168,2170,2171,2173,2174,2176,2177,2179,2180,2182,2183,2185,2186,2188,2189,2191,2192,2194,2195,2197,2198,2200,2201,2203,2204,2206,2207,2209,2210,2212,2213,2215,2216,2218,2219,2221,2222,2224,2225,2227,2228,2230,2231,2233,2234,2236,2237,2239,2240,2242,2243,2245,2246,2248,2249,2251,2252,2254,2255,2257,2258,2260,2261,2263,2264,2266,2267,2269,2270,2272,2273,2275,2276,2278,2279,2281,2282,2284,2285,2287,2288,2290,2291,2293,2294,2296,2297) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)
KICH RPPA
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)
KIRP RPPA LAML mRNASeq LAML RPPA LAML miRNASeq
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)
LIHC RPPA LIHC mut
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388,1390,1391,1393,1394,1396,1397,1399,1400,1402,1403,1405,1406,1408,1409,1411,1412,1414,1415,1417,1418,1420,1421,1423,1424,1426,1427) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)
PAAD RPPA SARC RPPA SARC mut
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)
STAD mRNASeq STAD RPPA
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) /cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388,1390,1391,1393,1394,1396,1397,1399,1400,1402,1403,1405,1406,1408,1409,1411,1412,1414,1415,1417,1418,1420,1421,1423,1424,1426,1427,1429,1430,1432,1433,1435,1436,1438,1439,1441,1442,1444,1445,1447,1448,1450,1451,1453,1454,1456,1457,1459,1460,1462,1463,1465,1466,1468,1469,1471,1472,1474,1475,1477,1478,1480,1481,1483,1484,1486,1487,1489,1490,1492,1493,1495,1496,1498,1499,1501,1502,1504,1505,1507,1508,1510,1511,1513,1514,1516,1517,1519,1520,1522,1523,1525,1526,1528,1529,1531,1532,1534,1535,1537,1538,1540,1541,1543,1544,1546,1547,1549,1550,1552,1553,1555,1556,1558,1559,1561,1562,1564,1565,1567,1568,1570,1571,1573,1574,1576,1577,1579,1580,1582,1583,1585,1586,1588,1589,1591,1592,1594,1595,1597,1598,1600,1601,1603,1604,1606,1607,1609,1610,1612,1613,1615,1616,1618,1619,1621,1622,1624,1625,1627,1628,1630,1631,1633,1634,1636,1637,1639,1640,1642,1643,1645,1646,1648,1649,1651,1652,1654,1655,1657,1658,1660,1661,1663,1664,1666,1667,1669,1670,1672,1673,1675,1676,1678,1679,1681,1682,1684,1685) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows)