用TCGAbiolinks从TCGA数据下载到下游分析的学习笔记
2018-11-20 本文已影响3389人
547可是贼帅的547
前言
之前一直在用RTCGA包下载数据,看着永不更新的数据,心里总觉得怪怪的,于是下定决心重新学习一个好用的包——TCGAbiolinks。这个包调用GDC的API,应该是最新的数据。
主要参考:TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data
下载数据
直接上代码
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("TCGAbiolinks")
library(TCGAbiolinks)
library(dplyr)
library(DT)
library(SummarizedExperiment)
#下面填入要下载的癌症种类
request_cancer=c("PRAD","BLCA","KICH","KIRC","KIRP")
for (i in request_cancer) {
cancer_type=paste("TCGA",i,sep="-")
print(cancer_type)
#下载临床数据
clinical <- GDCquery_clinic(project = cancer_type, type = "clinical")
write.csv(clinical,file = paste(cancer_type,"clinical.csv",sep = "-"))
#下载rna-seq的counts数据
query <- GDCquery(project = cancer_type,
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - Counts")
GDCdownload(query, method = "api", files.per.chunk = 100)
expdat <- GDCprepare(query = query)
count_matrix=assay(expdat)
write.csv(count_matrix,file = paste(cancer_type,"Counts.csv",sep = "-"))
#下载miRNA数据
query <- GDCquery(project = cancer_type,
data.category = "Transcriptome Profiling",
data.type = "miRNA Expression Quantification",
workflow.type = "BCGSC miRNA Profiling")
GDCdownload(query, method = "api", files.per.chunk = 50)
expdat <- GDCprepare(query = query)
count_matrix=assay(expdat)
write.csv(count_matrix,file = paste(cancer_type,"miRNA.csv",sep = "-"))
#下载Copy Number Variation数据
query <- GDCquery(project = cancer_type,
data.category = "Copy Number Variation",
data.type = "Copy Number Segment")
GDCdownload(query, method = "api", files.per.chunk = 50)
expdat <- GDCprepare(query = query)
count_matrix=assay(expdat)
write.csv(count_matrix,file = paste(cancer_type,"Copy-Number-Variation.csv",sep = "-"))
#下载甲基化数据
query.met <- GDCquery(project =cancer_type,
legacy = TRUE,
data.category = "DNA methylation")
GDCdownload(query.met, method = "api", files.per.chunk = 300)
expdat <- GDCprepare(query = query)
count_matrix=assay(expdat)
write.csv(count_matrix,file = paste(cancer_type,"methylation.csv",sep = "-"))
}
常用的一些数据基本都下下来了,放在当前目录下。