# TCGAbiolinks下载maf数据

## 下载数据

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

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    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

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    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

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    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

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    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

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    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

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    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

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    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")

数据库
TCGAbiolinks下载maf数据
https://occdn.limour.top/2304.html
Author
Limour
Posted on
September 6, 2022
Licensed under
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