This package provides the visualization of bayesian network inferred from gene expression data. The networks are based on enrichment analysis results inferred from packages including clusterProfiler and ReactomePA. The networks between pathways and genes inside the pathways can be inferred and visualized.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Infer and plot a Bayesian network of genes within a specific enriched pathway using expression data.
library(CBNplot)
library(bnlearn)
library(org.Hs.eg.db)
# 1. Load User Inputs & Prepare Data
data(gaussian.test)
kegg <- org.Hs.egPATH2EG
mapped <- mappedkeys(kegg)
genes <- as.list(kegg[mapped])[["00532"]]
counts <- head(gaussian.test, length(genes))
row.names(counts) <- genes
pway <- clusterProfiler::enrichKEGG(gene = genes)
pway <- clusterProfiler::setReadable(pway, org.Hs.eg.db, keyType="ENTREZID")
# 2. Visualize the gene-level Bayesian network for the first pathway
bngeneplot(results = pway, exp = counts, pathNum = 1, expRow="ENTREZID")
# 3. Extract the underlying network structure, strength, and direction
ret <- bngeneplot(results = pway, exp = counts, pathNum = 1, returnNet=TRUE, expRow="ENTREZID")
head(ret$str)
# 4. Convert the averaged network to an igraph object and perform centrality analysis
g <- bnlearn::as.igraph(ret$av)
igraph::evcent(g)$vector
# 5. Customize the gene network visualization
bngeneplotCustom(results = pway, exp = counts, expRow="ENTREZID", pathNum=1, fontFamily="sans", glowEdgeNum=NULL, hub=1)
Inputs/Outputs: Takes an enrichment analysis result object and a normalized expression data frame as inputs, and outputs Bayesian network plots and network structure data frames.
bngeneplot.bnpathplot.bngeneplotCustom or bnpathplotCustom.igraph object using bnlearn::as.igraph for centrality analysis.minet or GENIE3 instead).pathview instead).clusterProfiler::enrichKEGG or ReactomePA).counts) where row names match the gene identifier type (e.g., ENTREZID) specified in the enrichment results.clusterProfiler::enrichKEGG).bnpathplot.keyType of the enrichment results (e.g., convert using clusterProfiler::setReadable).mappedkeys to map pathway identifiers to genes when preparing test or custom pathway gene lists.returnNet = TRUE in bngeneplot to inspect the underlying network strength and direction before relying solely on the visual plot.clusterProfiler::setReadable to align key types (e.g., to "ENTREZID").pathview for overlaying expression data onto static KEGG pathway diagrams.enrichplot for standard enrichment visualizations (e.g., cnetplot, emapplot) without Bayesian network inference.bnlearn for general Bayesian network inference without direct integration with enrichment results.还没有评价。欢迎分享你的使用感受。
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