pan-cancer泛癌单基因分析问题之合并TCGA和GTEx

用户1359560

发布于 2021-03-08 04:42:06

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文章被收录于专栏：生信小驿站生信小驿站

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代码可运行

这个学习记录总共分为两个部分。

（1）第一个部分是纯代码分析某个基因在TCGA33类肿瘤中的差异分析。（2）结合TCGA和GTEx数据库，这样做的好处是：因为TCGA中肿瘤样本和正常样本是不均衡的，甚至某些肿瘤是没有癌旁正常组织的。所以结合GTEx数据库，可以大大增加正常样本的数量。

（1）TCGA差异分析

下载TCGA rawcount数据。

#=======================================================


#=======================================================


library(GenomicDataCommons)

setwd('D:\\SCIwork\\F33\\TCGA')

rm(list=ls())


library(dplyr)

library(TCGAbiolinks)

library(dplyr)

library(DT)

library(SummarizedExperiment)

library(stringr)

#=======================================================


#=======================================================

cancer  <- TCGAbiolinks:::getGDCprojects()$project_id

cancer <- str_subset(cancer, "TCGA")

cancer <- sort(cancer)




for (i in 1:33) {
  cancer_select <- cancer[i]
  print(cancer_select)
  #下载rna-seq的counts数据
  suppressMessages({
    query <- GDCquery(
      project = cancer_select,
      data.category = "Transcriptome Profiling",
      data.type = "Gene Expression Quantification",
      workflow.type = "HTSeq - Counts")  })
  
  
  if (is.null(query)){
    print(paste0("No Counts data of solid normal tissue for ", cancer_select ))
  } else{
    
    GDCdownload(query, method = "api", 
                files.per.chunk = 300)
    expdat <- GDCprepare(query = query, save = TRUE,
                         save.filename = paste0(cancer_select,".rda"))
    count_matrix=assay(expdat)
    write.csv(count_matrix,
              file = paste( cancer_select,"Counts.csv",
                            sep = "-"))}}