09_Reactome.Rmd

# Reactome enrichment analysis {#reactomepa}



```{r include=FALSE}
library(knitr)
opts_chunk$set(message=FALSE, warning=FALSE, eval=TRUE, echo=TRUE, cache=TRUE)
library(clusterProfiler)
library(ReactomePA)
```

`r Biocpkg("ReactomePA")` is designed for reactome pathway based analysis [@yu_reactomepa_2016]. Reactome is an open-source, open access, manually curated and peer-reviewed pathway database. 



## Supported organisms {#reactomepa-supported-organisms}

Currently `r Biocpkg("ReactomePA")` supports several model organisms, including 'celegans', 'fly', 'human', 'mouse', 'rat', 'yeast' and 'zebrafish'. The input gene ID should be Entrez gene ID. We recommend using [`clusterProfiler::bitr()`](#bitr) to convert biological IDs. 


## Reactome pathway over-representation analysis {#reactomepa-ora}

Enrichment analysis is a widely used approach to identify biological
themes. `r Biocpkg("ReactomePA")` implemented `enrichPathway()` that uses [hypergeometric model](#ora-algorithm) to assess whether the number of selected genes associated with a reactome pathway is larger than expected. 


```{r enrichpathway}
library(ReactomePA)
data(geneList, package="DOSE"
)
de <- names(geneList)[abs(geneList) > 1.5]
head(de)
x <- enrichPathway(gene=de, pvalueCutoff = 0.05, readable=TRUE)
head(x)
``` 


## Reactome pathway gene set enrichment analysis {#reactomepa-gsea}


```{r gsepathway}
y <- gsePathway(geneList, 
                pvalueCutoff = 0.2,
                pAdjustMethod = "BH", 
                verbose = FALSE)
head(y)
```


## Pathway Visualization

`r Biocpkg("ReactomePA")` implemented the `viewPathway()` to visualized selected reactome pathway. More general purpose of visualization methods for ORA and GSEA results are provided in the `r Biocpkg("enrichplot")` package and are documented on [Chapter 14](#enrichplot).


(ref:viewpathwayscap) Visualize reactome pathway.

(ref:viewpathwaycap) **Visualize reactome pathway.**

```{r viewpathway, fig.height=6, fig.width=8, fig.cap="(ref:viewpathwaycap)", fig.scap="(ref:viewpathwayscap)"}
viewPathway("E2F mediated regulation of DNA replication", 
            readable = TRUE, 
            foldChange = geneList)
```

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## Pathway analysis of NGS data

Pathway analysis using NGS data (eg, RNA-Seq and ChIP-Seq) can be performed by linking coding and non-coding regions to coding genes via `r Biocpkg("ChIPseeker")` package, which can annotates genomic regions to their nearest genes, host genes, and flanking genes respectivly. In addtion, it provides a function, __*seq2gene*__, that simultaneously considering host genes, promoter region and flanking gene from intergenic region that may under control via cis-regulation. This function maps genomic regions to genes in a many-to-many manner and facilitate functional analysis. For more details, please refer to `r Biocpkg("ChIPseeker")`[@yu_chipseeker_2015].


## Visualize enrichment result

We implement barplot, dotplot enrichment map and category-gene-network for visualization. It is very common to visualize the enrichment result in bar or pie chart. We believe the pie chart is misleading and only provide bar chart.
```r fig.height=6, fig.width=12}
barplot(x, showCategory=8)
```
```r fig.height=6, fig.width=12}
dotplot(x, showCategory=15)
```

Enrichment map can be viusalized by __*enrichMap*__:
```r fig.height=10, fig.width=10}
emapplot(x)
```



In order to consider the potentially biological complexities in which a gene may belong to multiple annotation categories, we developed __*cnetplot*__ function to extract the complex association between genes and diseases.
```r fig.height=8, fig.width=8}
cnetplot(x, categorySize="pvalue", foldChange=geneList)
```


## Visualize GSEA result

```r fig.height=8, fig.width=8}
emapplot(y, color="pvalue")
```


```r fig.height=7, fig.width=10}
gseaplot(y, geneSetID = "R-HSA-69242")
```


## Comparing enriched reactome pathways among gene clusters with clusterProfiler

We have developed an `R` package `r Biocpkg("clusterProfiler")`[@yu_clusterprofiler:_2012] for comparing biological themes among gene clusters. `r Biocpkg("ReactomePA")` works fine with `r Biocpkg("clusterProfiler")` and can compare biological themes at reactome pathway perspective.

```r fig.height=8, fig.width=13, eval=FALSE}
require(clusterProfiler)
data(gcSample)
res <- compareCluster(gcSample, fun="enrichPathway")
dotplot(res)
```


-->