# Chapter 4 `bnpathplot`

## 4.2 The vanilla plot

This function plots inferred relationship between pathways, with the boot-strapped strength between pathways. The normal plot can be plotted by `bnpathplot`

, passing the results of clusterProfiler or ReactomePA, (normalized) expression values, and candidate rows to be included in the inference. `nCategory`

specifies the number of categories (1:nCategory) to be plotted, sorted by the p-value. `expRow`

indicates what identifiers are used in row names of expression matrix.

```
bnpathplot(results = pway,
exp = vsted,
expSample = incSample,
nCategory = 30,
R = 10,
labelSize=5,
expRow = "ENSEMBL")
```

For the messy plot, the label can be modified using `shadowText=TRUE`

.

```
bnpathplot(results = pway,
exp = vsted,
expSample = incSample,
nCategory = 30,
R = 10,
labelSize=3,
shadowText=TRUE,
expRow = "ENSEMBL")
```

It can be KEGG or GO pathway results using `enrichKEGG`

and `enrichGO`

.

```
bnpathplot(results = pwayGO,
exp = vsted,
expSample = incSample,
nCategory = 30,
R = 10,
expRow = "ENSEMBL")
```

Or it can be the GSEA result.

```
bnpathplot(results = pwayGSE,
exp = vsted,
expSample = incSample,
nCategory = 30,
R = 10,
expRow = "ENSEMBL",
shadowText = TRUE,
color = "enrichmentScore")
```

## 4.3 Compare with the reference

For Reactome, the relationship between pathways can also be plotted by specifying `compareRef=TRUE`

. Here, dotted lines indicate relationship not in the reference, and solid lines indicate those in the reference.

```
bnpathplot(results = pway,
exp = vsted,
expSample = incSample,
nCategory = 30,
R = 10,
compareRef=T,
expRow = "ENSEMBL")
```

## 4.4 Reflect DepMap information to pathways

We can reflect overall dependency scores for the genes within the pathway by specifying `sizeDep=TRUE`

. You must provide `depmap::depmap_crispr`

or the other data describing the dependency score to `dep`

variable. The average score of the genes in the pathway is used.

```
bnpathplot(results = pwayGO,
exp = vsted,
expSample = incSample,
nCategory = 30,
R = 50,
sizeDep = T,
dep = dep,
expRow = "ENSEMBL")
```

## 4.5 Aggregating the pathway databases

The multiple GSEA results can be aggregated, by just passing the multiple results like as follows. When choosing the number of categories (`nCategory`

), the order of the results and the numbers should be same.

```
library(DOSE)
<- enrichDO(gene = cand.entrez)
pwayDO <- bnpathplot(results = c(pway, pwayDO), exp = vsted, expSample = incSample,
ReaAndDO nCategory = c(20, 20), R = 50)
ReaAndDO
```