b.limour.top/source/_posts/cbnplot推断临床变量对通路的影响.md

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title: CBNplot推断临床变量对通路的影响 tags: [] id: '2011' categories:

• - uncategorized date: 2022-09-09 07:40:12 ---

清洗数据

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

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