Chapter 1 CBNplot: Bayesian network plot for enrichment analysis results

The R package to plot Bayesian network inferred from expression data based on the enrichment analysis results including clusterProfiler or ReactomePA results (Wu et al. 2021; Yu and He 2016). It makes use of libraries including clusterProfiler, ReactomePA, bnlearn, graphite and depmap (Killian and Gatto 2021; Scutari 2010; Sales et al. 2012). The description of functions and several use cases are depicted in this book using GSE133624, which contains RNA-Seq data of bladder cancer and adjacent normal bladder tissues (Chen et al. 2019).

1.1 The preprocessing and DEG identification of GSE133624

In this book, the data from GSE133624 was used for the demonstrative purpose, and the parameters can be changed. First, DESeq2 is used to identify DEGs (Love, Huber, and Anders 2014).

library(DESeq2)

## Load dataset and make metadata
counts = read.table("GSE133624_reads-count-all-sample.txt", header=1, row.names=1)
meta = sapply(colnames(counts), function (x) substring(x,1,1))
meta = data.frame(meta)
colnames(meta) = c("Condition")

dds <- DESeqDataSetFromMatrix(countData = counts,
                              colData = meta,
                              design= ~ Condition)
## Prefiltering
filt <- rowSums(counts(dds) < 10) > dim(meta)[1]*0.9
dds <- dds[!filt,]

## Perform DESeq2()
dds = DESeq(dds)
res = results(dds, pAdjustMethod = "bonferroni")

## apply variance stabilizing transformation
v = vst(dds, blind=FALSE)
vsted = assay(v)
## Plot PCA of VST values
DESeq2::plotPCA(v, intgroup="Condition")+
    theme_bw()

## Define the input genes, and use clusterProfiler::bitr to convert the ID.
sig = subset(res, padj<0.05)
cand.entrez = clusterProfiler::bitr(rownames(sig), fromType="ENSEMBL", toType="ENTREZID", OrgDb=org.Hs.eg.db)$ENTREZID

## Perform enrichment analysis (ORA)
pway = ReactomePA::enrichPathway(gene = cand.entrez)
pwayGO = clusterProfiler::enrichGO(cand.entrez, ont = "MF", OrgDb = org.Hs.eg.db)

## Convert to SYMBOL
pway = setReadable(pway, OrgDb=org.Hs.eg.db)
pwayGO = setReadable(pwayGO, OrgDb=org.Hs.eg.db)

## Store the similarity
pway = enrichplot::pairwise_termsim(pway)

## Define including samples
incSample = rownames(subset(meta, Condition=="T"))

Additionally, for the gene set enrichment analysis, log2 fold changes are obtained.

allEntrez = clusterProfiler::bitr(rownames(res), fromType="ENSEMBL", toType="ENTREZID", OrgDb=org.Hs.eg.db)
res$ENSEMBL <- rownames(res)
lfc <- merge(data.frame(res), allEntrez, by="ENSEMBL")
lfc <- lfc[order(lfc$log2FoldChange, decreasing=TRUE),]
geneList <- lfc$log2FoldChange
names(geneList) <- lfc$ENTREZID

pwayGSE <- ReactomePA::gsePathway(geneList)

The mean gene count in the pathway, which is used in the inference.

sigpway <- subset(pway@result, p.adjust<0.05)
paste(mean(sigpway$Count), sd(sigpway$Count))
FALSE [1] "29.6976744186047 22.0886288379294"

References

Chen, Xin, Ang Li, Bao-Fa Sun, Ying Yang, Ya-Nan Han, Xun Yuan, Ri-Xin Chen, et al. 2019. “5-Methylcytosine Promotes Pathogenesis of Bladder Cancer Through Stabilizing mRNAs.” Nat. Cell Biol. 21 (8): 978–90.
Killian, Theo, and Laurent Gatto. 2021. “Exploiting the DepMap Cancer Dependency Data Using the Depmap R Package.” F1000Res. 10 (416): 416.
Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biol. 15 (12): 550.
Sales, Gabriele, Enrica Calura, Duccio Cavalieri, and Chiara Romualdi. 2012. “Graphite - a Bioconductor Package to Convert Pathway Topology to Gene Network.” BMC Bioinformatics 13 (January): 20.
Scutari, Marco. 2010. “Learning Bayesian Networks with the Bnlearn R Package.” Journal of Statistical Software, Articles 35 (3): 1–22.
Wu, Tianzhi, Erqiang Hu, Shuangbin Xu, Meijun Chen, Pingfan Guo, Zehan Dai, Tingze Feng, et al. 2021. “clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data.” Innov. J., July, 100141.
Yu, Guangchuang, and Qing-Yu He. 2016. “ReactomePA: An R/Bioconductor Package for Reactome Pathway Analysis and Visualization.” Mol. Biosyst. 12 (2): 477–79.