knitr::opts_chunk$set(echo = TRUE) knitr::opts_knit$set(progress = FALSE)
library(TCGAbiolinks) library(SummarizedExperiment) library(dplyr) library(DT)
TCGAbiolinks has provided a few functions to search, download and parse clinical data. This section starts by explaining the different sources for clinical information in GDC, followed by the necessary function to access these sources.
Other useful clinical information available are:
In this example we will fetch clinical data from BCR Biotab files.
query <- GDCquery( project = "TCGA-ACC", data.category = "Clinical", data.type = "Clinical Supplement", data.format = "BCR Biotab" ) GDCdownload(query) clinical.BCRtab.all <- GDCprepare(query) names(clinical.BCRtab.all)
clinical.BCRtab.all$clinical_drug_acc %>% head %>% DT::datatable(options = list(scrollX = TRUE, keys = TRUE))
In this example we will fetch all ACC BCR Biotab files, and look for the ER status.
library(TCGAbiolinks) query <- GDCquery( project = "TCGA-ACC", data.category = "Clinical", data.type = "Clinical Supplement", data.format = "BCR Biotab" ) GDCdownload(query) clinical_tab_all <- GDCprepare(query)
# All available tables names(clinical_tab_all) # columns from clinical_patient dplyr::glimpse(clinical_tab_all$clinical_patient_acc)
# Biospecimen BCR Biotab query_biospecimen <- GDCquery( project = "TCGA-ACC", data.category = "Biospecimen", data.type = "Biospecimen Supplement", data.format = "BCR Biotab" ) GDCdownload(query_biospecimen) biospecimen_tab_all <- GDCprepare(query_biospecimen)
# All available tables names(biospecimen_tab_all) biospecimen_tab_all$biospecimen_sample_acc %>% head %>% DT::datatable(options = list(scrollX = TRUE, keys = TRUE))
In this example we will fetch clinical indexed data (same as showed in the data portal).
clinical <- GDCquery_clinic(project = "TCGA-ACC", type = "clinical")
clinical %>% head %>% DT::datatable( filter = 'top', options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), rownames = FALSE )
clinical_beataml <- GDCquery_clinic( project = "BEATAML1.0-COHORT", type = "clinical" ) clinical_cptac2 <- GDCquery_clinic( project = "CPTAC-2", type = "clinical" ) clinical_genie <- GDCquery_clinic( project = "GENIE-MSK", type = "clinical" )
The process to get data directly from the XML are:
1. Use GDCquery
and GDCDownload
functions to search/download either biospecimen or clinical XML files
2. Use GDCprepare_clinic
function to parse the XML files.
The relation between one patient and other clinical information are 1:n,
one patient could have several radiation treatments. For that reason, we only give the option
to parse individual tables (only drug information, only radiation information,...)
The selection of the table is done by the argument clinical.info
.
Below are several examples fetching clinical data directly from the clinical XML files.
query <- GDCquery( project = "TCGA-COAD", data.category = "Clinical", data.format = "bcr xml", barcode = c("TCGA-RU-A8FL","TCGA-AA-3972") ) GDCdownload(query) clinical <- GDCprepare_clinic(query, clinical.info = "patient")
clinical %>% datatable(filter = 'top', options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), rownames = FALSE)
clinical.drug <- GDCprepare_clinic(query, clinical.info = "drug")
clinical.drug %>% datatable(filter = 'top', options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), rownames = FALSE)
clinical.radiation <- GDCprepare_clinic(query, clinical.info = "radiation")
clinical.radiation %>% datatable(filter = 'top', options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), rownames = FALSE)
clinical.admin <- GDCprepare_clinic(query, clinical.info = "admin")
clinical.admin %>% datatable(filter = 'top', options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), rownames = FALSE)
# Pathology report from harmonized portal query_harmonized <- GDCquery( project = "TCGA-COAD", data.category = "Biospecimen", data.type = "Slide Image", experimental.strategy = "Diagnostic Slide", barcode = c("TCGA-RU-A8FL","TCGA-AA-3972") )
query_harmonized %>% getResults %>% head %>% DT::datatable(options = list(scrollX = TRUE, keys = TRUE))
Also, some functions to work with clinical data are provided.
For example the function TCGAquery_SampleTypes
will filter barcodes based on a
type the argument typesample.
| Argument | Description | | |------------ |-------------------------------------------------------------- |----------------------------------------------- | | barcode | is a list of samples as TCGA barcodes | | | typesample | a character vector indicating tissue type to query. Example: | | | | TP | PRIMARY TUMOR | | | TR | RECURRENT TUMOR | | | TB | Primary Blood Derived Cancer-Peripheral Blood | | | TRBM | Recurrent Blood Derived Cancer-Bone Marrow | | | TAP | Additional-New Primary | | | TM | Metastatic | | | TAM | Additional Metastatic | | | THOC | Human Tumor Original Cells | | | TBM | Primary Blood Derived Cancer-Bone Marrow | | | NB | Blood Derived Normal | | | NT | Solid Tissue Normal | | | NBC | Buccal Cell Normal | | | NEBV | EBV Immortalized Normal | | | NBM | Bone Marrow Normal |
The function TCGAquery_MatchedCoupledSampleTypes
will filter the samples that
have all the typesample provided as argument. For example, if TP and TR are set
as typesample, the function will return the barcodes of a patient if it has both types.
So, if it has a TP, but not a TR, no barcode will be returned. If it has a TP and a TR
both barcodes are returned.
An example of the function is below:
bar <- c( "TCGA-G9-6378-02A-11R-1789-07", "TCGA-CH-5767-04A-11R-1789-07", "TCGA-G9-6332-60A-11R-1789-07", "TCGA-G9-6336-01A-11R-1789-07", "TCGA-G9-6336-11A-11R-1789-07", "TCGA-G9-7336-11A-11R-1789-07", "TCGA-G9-7336-04A-11R-1789-07", "TCGA-G9-7336-14A-11R-1789-07", "TCGA-G9-7036-04A-11R-1789-07", "TCGA-G9-7036-02A-11R-1789-07", "TCGA-G9-7036-11A-11R-1789-07", "TCGA-G9-7036-03A-11R-1789-07", "TCGA-G9-7036-10A-11R-1789-07", "TCGA-BH-A1ES-10A-11R-1789-07", "TCGA-BH-A1F0-10A-11R-1789-07", "TCGA-BH-A0BZ-02A-11R-1789-07", "TCGA-B6-A0WY-04A-11R-1789-07", "TCGA-BH-A1FG-04A-11R-1789-08", "TCGA-D8-A1JS-04A-11R-2089-08", "TCGA-AN-A0FN-11A-11R-8789-08", "TCGA-AR-A2LQ-12A-11R-8799-08", "TCGA-AR-A2LH-03A-11R-1789-07", "TCGA-BH-A1F8-04A-11R-5789-07", "TCGA-AR-A24T-04A-55R-1789-07", "TCGA-AO-A0J5-05A-11R-1789-07", "TCGA-BH-A0B4-11A-12R-1789-07", "TCGA-B6-A1KN-60A-13R-1789-07", "TCGA-AO-A0J5-01A-11R-1789-07", "TCGA-AO-A0J5-01A-11R-1789-07", "TCGA-G9-6336-11A-11R-1789-07", "TCGA-G9-6380-11A-11R-1789-07", "TCGA-G9-6380-01A-11R-1789-07", "TCGA-G9-6340-01A-11R-1789-07", "TCGA-G9-6340-11A-11R-1789-07" ) S <- TCGAquery_SampleTypes(bar,"TP") S2 <- TCGAquery_SampleTypes(bar,"NB") # Retrieve multiple tissue types NOT FROM THE SAME PATIENTS SS <- TCGAquery_SampleTypes(bar,c("TP","NB")) # Retrieve multiple tissue types FROM THE SAME PATIENTS SSS <- TCGAquery_MatchedCoupledSampleTypes(bar,c("NT","TP"))
To get all the information for TGCA samples you can use the script below:
# This code will get all clinical indexed data from TCGA library(data.table) library(dplyr) library(regexPipes) clinical <- TCGAbiolinks:::getGDCprojects()$project_id %>% regexPipes::grep("TCGA",value = TRUE) %>% sort %>% plyr::alply(1,GDCquery_clinic, .progress = "text") %>% rbindlist readr::write_csv(clinical,path = paste0("all_clin_indexed.csv")) # This code will get all clinical XML data from TCGA getclinical <- function(proj){ message(proj) while(1){ result = tryCatch({ query <- GDCquery(project = proj, data.category = "Clinical",data.format = "bcr xml") GDCdownload(query) clinical <- GDCprepare_clinic(query, clinical.info = "patient") for(i in c("admin","radiation","follow_up","drug","new_tumor_event")){ message(i) aux <- GDCprepare_clinic(query, clinical.info = i) if(is.null(aux) || nrow(aux) == 0) next # add suffix manually if it already exists replicated <- which(grep("bcr_patient_barcode",colnames(aux), value = T,invert = T) %in% colnames(clinical)) colnames(aux)[replicated] <- paste0(colnames(aux)[replicated],".",i) if(!is.null(aux)) clinical <- merge(clinical,aux,by = "bcr_patient_barcode", all = TRUE) } readr::write_csv(clinical,path = paste0(proj,"_clinical_from_XML.csv")) # Save the clinical data into a csv file return(clinical) }, error = function(e) { message(paste0("Error clinical: ", proj)) }) } } clinical <- TCGAbiolinks:::getGDCprojects()$project_id %>% regexPipes::grep("TCGA",value=T) %>% sort %>% plyr::alply(1,getclinical, .progress = "text") %>% rbindlist(fill = TRUE) %>% setDF %>% subset(!duplicated(clinical)) readr::write_csv(clinical,path = "all_clin_XML.csv") # result: https://drive.google.com/open?id=0B0-8N2fjttG-WWxSVE5MSGpva1U # Obs: this table has multiple lines for each patient, as the patient might have several followups, drug treatments, # new tumor events etc...
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