【TCGA】如何批量下载TCGA公开的转录组、miRNA转录组,蛋白表达谱、SNV、甲基化以及CNV数据

下面这个流程是下载这个网站公开数据的方法,使用到的工具是TCGAbiolinks(https://github.com/BioinformaticsFMRP/TCGAbiolinks),

主要是两种RNA表达谱数据和基因突变maf数据

下载的所有文件获取方法
  1. 站长已经把maf和表达谱文件已经上传到百度云,加入小站vip群里的小伙伴已经获得;
  1. 下面是下载所用到的方法,也可以自己下载,注意下载所有文件需要至少50G空间。
创建R 4.0环境
代码语言:javascript
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conda create -n R4 -c conda-forge -y r-essentials r-base r-devtools
conda activate R4
R
进入R语言环境
下载R包
代码语言:javascript
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install.packages("BiocManager")

BiocManager::install("BioinformaticsFMRP/TCGAbiolinksGUI.data")
BiocManager::install("BioinformaticsFMRP/TCGAbiolinks") ## 致敬开发者

批量下载代码

代码语言:javascript
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library(TCGAbiolinks)
projects <- getGDCprojects()
projects <- projects$project_id
TCGA_dowload<-function(x,dirpath){
#转录组数据
query.exp <-GDCquery(
  project = x, 
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification", 
  workflow.type = "STAR - Counts""
)
GDCdownload(query.exp)
Exp <- GDCprepare(query = query.exp)
#SNV数据
query.maf <- GDCquery(
  project = x, 
  data.category = "Simple Nucleotide Variation", 
  access = "open"
)
Maf <- GDCprepare(query = query.maf)
saveRDS(Maf,file = paste0(dirpath,x,"_maf.rds"))
#甲基化数据
for (i in c("450","27")) {
    query_met.hg38 <- GDCquery(
      project = x, 
      data.category = "DNA Methylation",
      platform = paste0("Illumina Human Methylation ",i),
      data.type = "Methylation Beta Value"
    )
    Met <- GDCprepare(query = query_met.hg38)
    saveRDS(Met,file = paste0(dirpath,x,"_met_Ill",i,".rds"))
  }
#miRNA数据
query.mirna <- GDCquery(
    project = x, 
    experimental.strategy = "miRNA-Seq",
    data.category = "Transcriptome Profiling", 
    data.type = "miRNA Expression Quantification"
  )
GDCdownload(query.mirna)
Mirna <- GDCprepare(query = query.mirna)
saveRDS(Mirna,file = paste0(dirpath,x,"_miRNA.rds"))
#蛋白表达量
query.rppa <- GDCquery(
    project = x, 
    data.category = "Proteome Profiling",
    data.type = "Protein Expression Quantification"
  )
GDCdownload(query.rppa) 
Proteins <- GDCprepare(query.rppa)
saveRDS(Proteins,file = paste0(dirpath,x,"_protein.rds"))
#CNV数据
CNV.type<-c("Allele-specific Copy Number Segment", 
            "Gene Level Copy Number",
            "Masked Copy Number Segment")
for (ii in CNV.type) {
    query.CNV <- GDCquery(
      project = project,
      data.category = "Copy Number Variation",
      data.type = ii
    )
    GDCdownload(query.CNV)
    CNV <- GDCprepare(query = query.CNV)
    saveRDS(CNV,file = paste0(dirpath,project,"_CNV_",
                              stringr::str_replace_all(i," ","_"),
                              ".rds"))
  }
}

批量下载数据

for (j in projects) {
print(j)
try(TCGA_dowload(j,dirpath = "./TCGAbiolinks_data/"),silent = T)
}

下载数据说明

文件使用
  1. 下载文件保存格式是rds,使用下面方法可以加载
代码语言:javascript
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TCGA_ACC_Exp<-readRDA("TCGA-ACC_exp.rds") ##注意文件路径要正确
  1. 表达谱数据 表达谱数据包括:
代码语言:javascript
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TCGA_ACC_Exp_unstrand<-SummarizedExperiment::assay(TCGA_ACC_Exp,1)
  1. 临床信息 表达谱中整合了临床信息可以用下面方法提取
代码语言:javascript
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TCGA_ACC_clinData<-SummarizedExperiment::colData(TCGA_ACC_Exp)
  1. 关于maf 下载的SNV_maf文件没有临床信息需要自己整理一下才能使用maftools