# TCGAbiolinks下载CNV数据

## 下载Gene水平的数据

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    library(TCGAbiolinks)
    query <- GDCquery(
        project = "TCGA-PRAD",
        data.category = "Copy Number Variation",
        data.type = "Gene Level Copy Number",
        access = "open"
    )
    GDCdownload(query)
    data <- GDCprepare(query)
    saveRDS(data, 'prad_cnv.rds')

## 下载Masked数据

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    query <- GDCquery(
        project = "TCGA-PRAD",
        data.category = "Copy Number Variation",
        data.type = "Masked Copy Number Segment",
        access = "open"
    )
    GDCdownload(query)
    data <- GDCprepare(query)
    saveRDS(data, 'prad_cnv_masked.rds')

## 清洗数据

### 初步清洗

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    library(SummarizedExperiment)
    data <- readRDS('prad_cnv.rds')
    cnT <- data@assays@data$copy_number
    cnTcol <- colnames(cnT)
    type <- as.numeric(substr(cnTcol, 14, 15))
    cnT <- cnT[, type<10]
    colnames(cnT) <- substr(cnTcol,1, 12)
    rownames(cnT) <-  data@rowRanges$gene_name
    cnT <- na.omit(cnT)

### 精细清洗

f_dedup_IQR

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    cnT <- f_dedup_IQR(cnT, rownames(cnT))
    cnT <- cnT[,f_rm_duplicated(colnames(cnT))]
    group <- readRDS('../idea_2/fig3.2/fig5/tcga.predict.rds')
    cnT <- cnT[,colnames(cnT) %in% rownames(group)]

### 构造cnTable

#### 慢速

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    df <- NULL
    for (i in 1:ncol(cnT)){
        colnames(cnT)[[i]]
        tmp_df <- data.frame(Hugo_Symbol = rownames(cnT), Tumor_Sample_Barcode = colnames(cnT)[[i]], Variant_Classification=cnT[,i])
        tmp_df <- subset(tmp_df, Variant_Classification != 2)
        df <- rbind(df, tmp_df)
    }

#### 快速

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    library(reshape2)
    df <- melt(cnT)
    colnames(df) = c('Hugo_Symbol', 'Tumor_Sample_Barcode', 'Variant_Classification')
    df <- subset(df, Variant_Classification != 2)
    df

#### 贴标签

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    df$Variant_Classification[df$Variant_Classification > 2]  <- 'Amp'
    df$Variant_Classification[df$Variant_Classification < 2]  <- 'Del'
    table(df$Variant_Classification)

### 分组别

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    df_l <- subset(df, Tumor_Sample_Barcode %in% rownames(group)[group$group == 'Low Risk'])
    df_h <- subset(df, Tumor_Sample_Barcode %in% rownames(group)[group$group == 'High Risk'])
    saveRDS(df_l, 'cnT.l.rds')
    saveRDS(df_h, 'cnT.h.rds')

## 导入maftools

TCGAbiolinks下载maf数据

### 清洗数据

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    cnv_l <- readRDS('cnT.l.rds')
    cnv_h <- readRDS('cnT.h.rds')
    prad_l$Tumor_Sample_Barcode <- prad_l$BarCode
    prad_l <- subset(prad_l, Tumor_Sample_Barcode %in% cnv_l$Tumor_Sample_Barcode)
    cnv_l <- subset(cnv_l, Tumor_Sample_Barcode %in% prad_l$Tumor_Sample_Barcode)
    prad_h$Tumor_Sample_Barcode <- prad_h$BarCode
    prad_h <- subset(prad_h, Tumor_Sample_Barcode %in% cnv_h$Tumor_Sample_Barcode)
    cnv_h <- subset(cnv_h, Tumor_Sample_Barcode %in% prad_h$Tumor_Sample_Barcode)

### 读入maftools

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    maf_l <- read.maf(prad_l, cnTable = cnv_l)
    maf_h <- read.maf(prad_h, cnTable = cnv_h)

### 绘制瀑布图

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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)

数据库
TCGAbiolinks下载CNV数据
https://occdn.limour.top/2307.html
Author
Limour
Posted on
September 8, 2022
Licensed under
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