# CBNplot推断临床变量对通路的影响

## 清洗数据

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    vsted <- readRDS('rininiang.rds')
    group <- readRDS('tcga.predict.rds')
    incSample <- rownames(group)[group$group == 'High Risk']
    pwayGSE  <- readRDS('pwayGSE.rds')
    spath <- read.csv('fig5_selected_pGSE.csv', row.names = 1)
    pwayGSE@result <- pwayGSE@result[rownames(spath),]
    require(org.Hs.eg.db)
    set.seed(123)
    CBNplot::bnpathplot(results = pwayGSE, exp = vsted, expSample = incSample, R = 200,
               nCategory = 100,
               expRow='ENSEMBL', orgDb=org.Hs.eg.db)
    group <- group[colnames(vsted),]

## 推断临床变量对通路的调控

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    bnCov <- CBNplot::bnpathplot(pwayGSE,
                        vsted,
                        nCategory = 1000,
                        adjpCutOff = 0.05,
                        expSample=rownames(group),
                        algo="hc", strType="normal",
                        otherVar=group$group,
                        otherVarName="Risk_Group",
                        R=200, cl=parallel::makeCluster(4),
                        returnNet=T,
                        shadowText=T)
    igraph::is.dag(bnlearn::as.igraph(bnCov$av))
    bnFit <- bnlearn::bn.fit(bnCov$av, bnCov$df)
    bnCov$plot

点此查看官方手册的更进一步的分析

通路富集
CBNplot推断临床变量对通路的影响
https://occdn.limour.top/2313.html
Author
Limour
Posted on
September 9, 2022
Licensed under
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