The R package to infer and plot Bayesian networks. The network are inferred from expression data based on clusterProfiler or ReactomePA results. It makes use of libraries including clusterProfiler, ReactomePA, bnlearn, graphite and depmap. In this vignette, the description of functions and several use cases are depicted using random data. The more detailed use cases including the analysis of the dataset GSE133624, which contains RNA-Seq data of bladder cancer, can be found on the book (https://noriakis.github.io/CBNplot/).
BiocManager::install("CBNplot")library(CBNplot)
library(bnlearn)
library(org.Hs.eg.db)
## Load data
data(gaussian.test)
## Draw genes in the KEGG pathway as DEG
kegg <- org.Hs.egPATH2EG
mapped <- mappedkeys(kegg)
genes <- as.list(kegg[mapped])[["00532"]]
## Random data
counts <- head(gaussian.test, length(genes))
row.names(counts) <- genes
## Perform enrichment analysis
pway <- clusterProfiler::enrichKEGG(gene = genes)
pway <- clusterProfiler::setReadable(pway, org.Hs.eg.db, keyType="ENTREZID")Then use CBNplot. Basically, you need to supply the enrichment analysis result, normalized expression value and samples to be included. For bngeneplot, the pathway number in the result slot of enrichment analysis results must be given.
bngeneplot(results = pway,exp = counts, pathNum = 1, expRow="ENTREZID")