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


CBNplot推断临床变量对通路的影响
https://b.limour.top/2011.html
Author
Limour
Posted on
September 9, 2022
Licensed under