# Cbnplot

> CBNplot

- Skill: `biomate-ai/cbnplot` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/cbnplot`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/cbnplot/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/cbnplot

---


# CBNplot

## Workflows

### Standard Workflow

Infer and plot a Bayesian network of genes within a specific enriched pathway using expression data.

```r
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.

## When to Use
- To infer and visualize Bayesian networks of genes within a specific enriched pathway using expression data via `bngeneplot`.
- To visualize relationships between multiple pathways as a Bayesian network using `bnpathplot`.
- To customize network visualizations with glow edges or hub highlighting using `bngeneplotCustom` or `bnpathplotCustom`.
- To extract network strength and direction or convert the inferred network to an `igraph` object using `bnlearn::as.igraph` for centrality analysis.

## When NOT to Use
- For general non-pathway-based gene regulatory network inference without prior enrichment results (use packages like `minet` or `GENIE3` instead).
- For static pathway diagrams without expression-based network inference (use `pathview` instead).

## Data Requirements
- Enrichment analysis results (e.g., from `clusterProfiler::enrichKEGG` or `ReactomePA`).
- A normalized expression matrix or data frame (e.g., `counts`) where row names match the gene identifier type (e.g., `ENTREZID`) specified in the enrichment results.

## Key Parameters
- **results**: Enrichment analysis result object (e.g., from `clusterProfiler::enrichKEGG`).
- **exp**: Normalized expression data frame or matrix.
- **pathNum** (1): The pathway number in the enrichment results to plot.
- **expRow** ("ENTREZID"): The column/row identifier type for expression data.
- **returnNet** (FALSE): Logical indicating whether to return the network structure, strength, and direction.
- **nCategory** (5): Number of pathways to include in `bnpathplot`.
- **glowEdgeNum** (NULL): Parameter in custom plots to highlight edges of interest.
- **hub** (1): Parameter in custom plots to highlight hub nodes.

## Best Practices
- Ensure gene identifiers in the expression matrix match the `keyType` of the enrichment results (e.g., convert using `clusterProfiler::setReadable`).
- Use `mappedkeys` to map pathway identifiers to genes when preparing test or custom pathway gene lists.
- Set `returnNet = TRUE` in `bngeneplot` to inspect the underlying network strength and direction before relying solely on the visual plot.

## Common Pitfalls
- Mismatched gene identifier types between expression data and enrichment results. Fix: Use `clusterProfiler::setReadable` to align key types (e.g., to "ENTREZID").
- Providing raw, unnormalized count data to network inference. Fix: Supply normalized expression values (e.g., log-transformed or normalized counts).

## Alternatives
- `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.

## Citations
- Scutari M (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software.
- Yu G, Wang LG, Han Y, He QY (2012). clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS.

## References
- Homepage: bioconductor.org/packages/cbnplot
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cbnplot/inst/doc/CBNplot.html