---
title: "CBNplot"
output:
  BiocStyle::html_document:
    toc: true
    toc_float: true
vignette: >
  %\VignetteIndexEntry{CBNplot}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

# CBNplot: Bayesian network plot for clusterProfiler results


## Introduction

The R package to infer and plot Bayesian networks. The network are inferred from expression data based on [clusterProfiler](https://github.com/YuLab-SMU/clusterProfiler) or ReactomePA results. It makes use of libraries including [clusterProfiler](https://github.com/YuLab-SMU/clusterProfiler), [ReactomePA](https://github.com/YuLab-SMU/ReactomePA), [bnlearn](https://www.bnlearn.com/), [graphite](https://bioconductor.org/packages/release/bioc/html/graphite.html) and [depmap](https://bioconductor.org/packages/release/data/experiment/html/depmap.html). 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](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133624), which contains RNA-Seq data of bladder cancer, can be found on the book ([https://noriakis.github.io/CBNplot/](https://noriakis.github.io/CBNplot/)).

## Installation
```{r, eval=FALSE}
BiocManager::install("CBNplot")
```

## Usage
### Generation of data

```{r deg, include=TRUE, echo=TRUE, message=FALSE, cache=FALSE, warning=FALSE, comment=FALSE, fig.height = 10, fig.width = 10}
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")
```


### The use of CBNplot


## bngeneplot

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.

```{r usecase, include=TRUE, echo=TRUE, message=FALSE, cache=FALSE, warning=FALSE, comment=FALSE, fig.height = 10, fig.width = 10}
bngeneplot(results = pway,exp = counts, pathNum = 1, expRow="ENTREZID")
```

Data frame of raw values used in the inference, data frame containing strength and direction, averaged network, and plot can be obtained by specifying `returnNet=TRUE`
```{r usecase2, include=TRUE, echo=TRUE, message=FALSE, cache=FALSE, warning=FALSE, comment=FALSE, fig.height = 10, fig.width = 10}
ret <- bngeneplot(results = pway,exp = counts, pathNum = 1, returnNet=TRUE, , expRow="ENTREZID")
head(ret$str)
```

The resulting network can be converted to `igraph` object using `bnlearn::as.igraph()`.

```{r igraph, include=TRUE, include=TRUE, echo=TRUE, message=FALSE, cache=FALSE}
g <- bnlearn::as.igraph(ret$av)
igraph::evcent(g)$vector
```


## bnpathplot

The relationship between pathways can be drawn by `bnpathplot`. The number to be included in the inference can be specified by `nCategory`.

```{r usecase3, include=TRUE, echo=TRUE, message=FALSE, cache=FALSE, warning=FALSE, comment=FALSE, fig.height = 10, fig.width = 10}
bnpathplot(results = pway,exp = counts, nCategory=5, shadowText = TRUE, expRow="ENTREZID")
```

## bngeneplotCustom and bnpathplotCustom

`bngeneplotCustom` and `bnpathplotCustom` can be used to customize visualization with more flexibility, like highlighting the nodes and edges of interest by `glowEdgeNum` and `hub`.

```{r usecase4, include=TRUE, echo=TRUE, message=FALSE, cache=FALSE, warning=FALSE, comment=FALSE, fig.height = 10, fig.width = 10}
bnpathplotCustom(results = pway, exp = counts, expRow="ENTREZID",
                 fontFamily="serif", glowEdgeNum=1, hub=1)
bngeneplotCustom(results = pway, exp = counts, expRow="ENTREZID",
                 pathNum=1, fontFamily="sans", glowEdgeNum=NULL, hub=1)
```

The detailed usage for the package, like including covariates to the plot and probabilistic reasoning is available in the package documentation ([https://noriakis.github.io/CBNplot/](https://noriakis.github.io/CBNplot/)).

```{r}
sessionInfo()
```