occdn.limour.top/source/_posts/tcgabiolinks下载maf数据.md

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title: TCGAbiolinks下载maf数据 tags: [] id: '2304' categories:

• - 数据库 date: 2022-09-06 17:04:57 ---

下载数据

• ~/dev/xray/xray -c ~/etc/xui2.json &

  library(TCGAbiolinks)
  Sys.setenv("http_proxy"="http://127.0.0.1:20809")
  Sys.setenv("https_proxy"="http://127.0.0.1:20809")
  PRAD <- GDCquery(project = 'TCGA-PRAD',
              data.category = "Simple Nucleotide Variation",
              access = "open", 
              legacy = FALSE, 
              data.type = "Masked Somatic Mutation", 
              workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking")
  GDCdownload(PRAD)
  maf <- GDCprepare(PRAD)
  saveRDS(maf, 'prad.maf')
  

清洗数据

library(dplyr)
prad <- readRDS('prad.maf')
type <- as.numeric(substr(prad$Tumor_Sample_Barcode, 14, 15))
prad <- subset(prad, type < 10) # tp
group <- readRDS('../idea_2/fig3.2/fig5/tcga.predict.rds')
prad$BarCode <- substr(prad$Tumor_Sample_Barcode,1, 12)
group$BarCode  <- rownames(group)
prad <- subset(prad, prad$BarCode %in% group$BarCode)
prad <- left_join(x = prad, y = group, by = 'BarCode')

分两组导入maftools中

library(maftools)
prad_l <- subset(prad, group=='Low Risk')
prad_h <- subset(prad, group=='High Risk')
maf_l <- read.maf(prad_l)
maf_h <- read.maf(prad_h)

比较并进行可视化

lvsh <- mafCompare(m1=maf_l, m2=maf_h, m1Name="Low Risk", m2Name="High Risk", minMut=5)
saveRDS(lvsh, 'lvsh.rds')

森林图展示突变数量差异

options(repr.plot.width=8, repr.plot.height=6)
forestPlot(mafCompareRes=lvsh, pVal=0.05, color=c("maroon", "royalblue"), geneFontSize=1.2)

瀑布图oncoplot展示突变景观

options(repr.plot.width=12, repr.plot.height=8)
genes <- subset(lvsh$results, pval < 0.05)$Hugo_Symbol
coOncoplot(m1=maf_l, m2=maf_h, m1Name="Low Risk", m2Name="High Risk", genes=genes)

棒棒糖图深入特定基因突变细节

options(repr.plot.width=12, repr.plot.height=6)
lollipopPlot2(m1=maf_l, m2=maf_h, m1_name="Low Risk", m2_name="High Risk", gene="TP53", AACol1 = "HGVSp_Short", AACol2 = "HGVSp_Short")