Supported by Dr. Osamu Ogasawara and  providing  . |
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Last data update: 2014.03.03 |
Information about cohorts from TCGA projectDescriptionFunction restores codes and counts for each cohort from TCGA project. UsageinfoTCGA() ValueA list with a tabular information from http://gdac.broadinstitute.org/. IssuesIf you have any problems, issues or think that something is missing or is not clear please post an issue on https://github.com/RTCGA/RTCGA/issues. Author(s)Marcin Kosinski, m.p.kosinski@gmail.com See AlsoRTCGA website http://rtcga.github.io/RTCGA/Download.html. Other RTCGA: Examples
infoTCGA()
library(magrittr)
(cohorts <- infoTCGA() %>%
rownames() %>%
sub('-counts', '', x=.))
# in knitr chunk -> results='asis'
knitr::kable(infoTCGA())
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(RTCGA)
Welcome to the RTCGA (version: 1.2.2).
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RTCGA/infoTCGA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: infoTCGA
> ### Title: Information about cohorts from TCGA project
> ### Aliases: infoTCGA
>
> ### ** Examples
>
>
> infoTCGA()
Cohort BCR Clinical CN LowP Methylation mRNA mRNASeq miR
ACC-counts ACC 92 92 90 0 80 0 79 0
BLCA-counts BLCA 412 412 410 112 412 0 408 0
BRCA-counts BRCA 1098 1097 1089 19 1097 526 1093 0
CESC-counts CESC 307 307 295 50 307 0 304 0
CHOL-counts CHOL 51 45 36 0 36 0 36 0
COAD-counts COAD 460 458 451 69 457 153 457 0
COADREAD-counts COADREAD 631 629 616 104 622 222 623 0
DLBC-counts DLBC 58 48 48 0 48 0 48 0
ESCA-counts ESCA 185 185 184 51 185 0 184 0
FPPP-counts FPPP 38 38 0 0 0 0 0 0
GBM-counts GBM 613 595 577 0 420 540 160 565
GBMLGG-counts GBMLGG 1129 1110 1090 52 936 567 676 565
HNSC-counts HNSC 528 528 522 108 528 0 520 0
KICH-counts KICH 113 113 66 0 66 0 66 0
KIPAN-counts KIPAN 973 941 883 0 892 88 889 0
KIRC-counts KIRC 537 537 528 0 535 72 533 0
KIRP-counts KIRP 323 291 289 0 291 16 290 0
LAML-counts LAML 200 200 197 0 194 0 179 0
LGG-counts LGG 516 515 513 52 516 27 516 0
LIHC-counts LIHC 377 377 370 0 377 0 371 0
LUAD-counts LUAD 585 522 516 120 578 32 515 0
LUSC-counts LUSC 504 504 501 0 503 154 501 0
MESO-counts MESO 87 87 87 0 87 0 87 0
OV-counts OV 602 591 586 0 594 574 304 570
PAAD-counts PAAD 185 185 184 0 184 0 178 0
PCPG-counts PCPG 179 179 175 0 179 0 179 0
PRAD-counts PRAD 499 499 492 115 498 0 497 0
READ-counts READ 171 171 165 35 165 69 166 0
SARC-counts SARC 261 261 257 0 261 0 259 0
SKCM-counts SKCM 470 470 469 118 470 0 469 0
STAD-counts STAD 443 443 442 107 443 0 415 0
STES-counts STES 628 628 626 158 628 0 599 0
TGCT-counts TGCT 150 134 150 0 150 0 150 0
THCA-counts THCA 503 503 499 98 503 0 501 0
THYM-counts THYM 124 124 123 0 124 0 120 0
UCEC-counts UCEC 560 548 540 106 547 54 545 0
UCS-counts UCS 57 57 56 0 57 0 57 0
UVM-counts UVM 80 80 80 51 80 0 80 0
miRSeq RPPA MAF rawMAF
ACC-counts 80 46 90 0
BLCA-counts 409 344 130 395
BRCA-counts 1078 887 977 0
CESC-counts 307 173 194 0
CHOL-counts 36 30 35 0
COAD-counts 406 360 154 367
COADREAD-counts 549 491 223 489
DLBC-counts 47 33 48 0
ESCA-counts 184 126 185 0
FPPP-counts 23 0 0 0
GBM-counts 0 238 290 290
GBMLGG-counts 512 668 576 806
HNSC-counts 523 212 279 510
KICH-counts 66 63 66 66
KIPAN-counts 873 756 644 799
KIRC-counts 516 478 417 451
KIRP-counts 291 215 161 282
LAML-counts 188 0 197 0
LGG-counts 512 430 286 516
LIHC-counts 372 63 198 373
LUAD-counts 513 365 230 542
LUSC-counts 478 328 178 0
MESO-counts 87 63 0 0
OV-counts 453 426 316 469
PAAD-counts 178 123 150 184
PCPG-counts 179 80 179 0
PRAD-counts 494 352 332 498
READ-counts 143 131 69 122
SARC-counts 259 223 247 0
SKCM-counts 448 353 343 366
STAD-counts 436 357 289 395
STES-counts 620 483 474 395
TGCT-counts 150 118 149 0
THCA-counts 502 222 402 496
THYM-counts 124 90 123 0
UCEC-counts 538 440 248 0
UCS-counts 56 48 57 0
UVM-counts 80 12 80 0
> library(magrittr)
> (cohorts <- infoTCGA() %>%
+ rownames() %>%
+ sub('-counts', '', x=.))
[1] "ACC" "BLCA" "BRCA" "CESC" "CHOL" "COAD"
[7] "COADREAD" "DLBC" "ESCA" "FPPP" "GBM" "GBMLGG"
[13] "HNSC" "KICH" "KIPAN" "KIRC" "KIRP" "LAML"
[19] "LGG" "LIHC" "LUAD" "LUSC" "MESO" "OV"
[25] "PAAD" "PCPG" "PRAD" "READ" "SARC" "SKCM"
[31] "STAD" "STES" "TGCT" "THCA" "THYM" "UCEC"
[37] "UCS" "UVM"
>
> # in knitr chunk -> results='asis'
> knitr::kable(infoTCGA())
| |Cohort |BCR |Clinical |CN |LowP |Methylation |mRNA |mRNASeq |miR |miRSeq |RPPA |MAF |rawMAF |
|:---------------|:--------|:----|:--------|:----|:----|:-----------|:----|:-------|:---|:------|:----|:---|:------|
|ACC-counts |ACC |92 |92 |90 |0 |80 |0 |79 |0 |80 |46 |90 |0 |
|BLCA-counts |BLCA |412 |412 |410 |112 |412 |0 |408 |0 |409 |344 |130 |395 |
|BRCA-counts |BRCA |1098 |1097 |1089 |19 |1097 |526 |1093 |0 |1078 |887 |977 |0 |
|CESC-counts |CESC |307 |307 |295 |50 |307 |0 |304 |0 |307 |173 |194 |0 |
|CHOL-counts |CHOL |51 |45 |36 |0 |36 |0 |36 |0 |36 |30 |35 |0 |
|COAD-counts |COAD |460 |458 |451 |69 |457 |153 |457 |0 |406 |360 |154 |367 |
|COADREAD-counts |COADREAD |631 |629 |616 |104 |622 |222 |623 |0 |549 |491 |223 |489 |
|DLBC-counts |DLBC |58 |48 |48 |0 |48 |0 |48 |0 |47 |33 |48 |0 |
|ESCA-counts |ESCA |185 |185 |184 |51 |185 |0 |184 |0 |184 |126 |185 |0 |
|FPPP-counts |FPPP |38 |38 |0 |0 |0 |0 |0 |0 |23 |0 |0 |0 |
|GBM-counts |GBM |613 |595 |577 |0 |420 |540 |160 |565 |0 |238 |290 |290 |
|GBMLGG-counts |GBMLGG |1129 |1110 |1090 |52 |936 |567 |676 |565 |512 |668 |576 |806 |
|HNSC-counts |HNSC |528 |528 |522 |108 |528 |0 |520 |0 |523 |212 |279 |510 |
|KICH-counts |KICH |113 |113 |66 |0 |66 |0 |66 |0 |66 |63 |66 |66 |
|KIPAN-counts |KIPAN |973 |941 |883 |0 |892 |88 |889 |0 |873 |756 |644 |799 |
|KIRC-counts |KIRC |537 |537 |528 |0 |535 |72 |533 |0 |516 |478 |417 |451 |
|KIRP-counts |KIRP |323 |291 |289 |0 |291 |16 |290 |0 |291 |215 |161 |282 |
|LAML-counts |LAML |200 |200 |197 |0 |194 |0 |179 |0 |188 |0 |197 |0 |
|LGG-counts |LGG |516 |515 |513 |52 |516 |27 |516 |0 |512 |430 |286 |516 |
|LIHC-counts |LIHC |377 |377 |370 |0 |377 |0 |371 |0 |372 |63 |198 |373 |
|LUAD-counts |LUAD |585 |522 |516 |120 |578 |32 |515 |0 |513 |365 |230 |542 |
|LUSC-counts |LUSC |504 |504 |501 |0 |503 |154 |501 |0 |478 |328 |178 |0 |
|MESO-counts |MESO |87 |87 |87 |0 |87 |0 |87 |0 |87 |63 |0 |0 |
|OV-counts |OV |602 |591 |586 |0 |594 |574 |304 |570 |453 |426 |316 |469 |
|PAAD-counts |PAAD |185 |185 |184 |0 |184 |0 |178 |0 |178 |123 |150 |184 |
|PCPG-counts |PCPG |179 |179 |175 |0 |179 |0 |179 |0 |179 |80 |179 |0 |
|PRAD-counts |PRAD |499 |499 |492 |115 |498 |0 |497 |0 |494 |352 |332 |498 |
|READ-counts |READ |171 |171 |165 |35 |165 |69 |166 |0 |143 |131 |69 |122 |
|SARC-counts |SARC |261 |261 |257 |0 |261 |0 |259 |0 |259 |223 |247 |0 |
|SKCM-counts |SKCM |470 |470 |469 |118 |470 |0 |469 |0 |448 |353 |343 |366 |
|STAD-counts |STAD |443 |443 |442 |107 |443 |0 |415 |0 |436 |357 |289 |395 |
|STES-counts |STES |628 |628 |626 |158 |628 |0 |599 |0 |620 |483 |474 |395 |
|TGCT-counts |TGCT |150 |134 |150 |0 |150 |0 |150 |0 |150 |118 |149 |0 |
|THCA-counts |THCA |503 |503 |499 |98 |503 |0 |501 |0 |502 |222 |402 |496 |
|THYM-counts |THYM |124 |124 |123 |0 |124 |0 |120 |0 |124 |90 |123 |0 |
|UCEC-counts |UCEC |560 |548 |540 |106 |547 |54 |545 |0 |538 |440 |248 |0 |
|UCS-counts |UCS |57 |57 |56 |0 |57 |0 |57 |0 |56 |48 |57 |0 |
|UVM-counts |UVM |80 |80 |80 |51 |80 |0 |80 |0 |80 |12 |80 |0 |
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> dev.off()
null device
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and