# TCGAbiolinks下载蛋白质组数据

之前通过tcpa下载过蛋白数据，而TCGAbiolinks也有下载蛋白质组学数据的示例，后者看上去更全面一点。

## 下载数据

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    library(TCGAbiolinks)
    query.rppa <- GDCquery(
        project = "TCGA-PRAD",
        data.category = "Proteome Profiling",
        data.type = "Protein Expression Quantification"
    )
    GDCdownload(query.rppa)
    rppa <- GDCprepare(query.rppa)
    saveRDS(rppa, 'PRAD_rppa.rds')

## 清洗数据

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    pMiss <- function(x){round(sum(is.na(x))/length(x),3)}
    rppa <- rppa[apply(rppa, 1, pMiss) < 0.05, ]
    rppa <- rppa[, apply(rppa, 2, pMiss) < 0.05]
    sum(is.na(rppa))
    rowInfo <- rppa[1:5]
    rppa <- rppa[-(1:5)]
    rppa <- rppa[, substr(colnames(rppa), 14, 16) == "01A"]
    colnames(rppa) <- substr(colnames(rppa),1,12)
    group <- readRDS('../fig5/tcga.predict.rds')
    gRow <- intersect(colnames(rppa), rownames(group))
    group <- group[gRow,]
    rppa <- rppa[, colnames(rppa) %in% gRow]
    Ct1 <- rownames(group)[group$group == 'Low Risk']
    Tt1 <- rownames(group)[group$group == 'High Risk']

## 计算差异蛋白

f_DE_limma

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    r1 <- f_DE_limma(rppa, rowInfo, Ct1, Tt1, trend=F)
    rownames(rppa) <- rowInfo$AGID
    save(r1, rppa, file = 'PRAD_TCPA_DE.rdata')

数据库
TCGAbiolinks下载蛋白质组数据
https://occdn.limour.top/2325.html
Author
Limour
Posted on
September 10, 2022
Licensed under
蛋白质组RPPA抗体与基因信息映射 Previous
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