---
title: "An R package for Reactome Pathway Analysis"
author: "\\

	Guangchuang Yu\\

        School of Public Health, The University of Hong Kong"
date: "`r Sys.Date()`"
bibliography: ReactomePA.bib
biblio-style: apalike
output:
  prettydoc::html_pretty:
    toc: true
    theme: cayman
    highlight: github
  pdf_document:
    toc: true
vignette: >
  %\VignetteIndexEntry{An R package for Reactome Pathway Analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteDepends{org.Hs.eg.db}
  %\usepackage[utf8]{inputenc}
  %\VignetteEncoding{UTF-8}
---

```{r style, echo=FALSE, results="asis", message=FALSE}
knitr::opts_chunk$set(tidy = FALSE,
                      warning = FALSE,
                      message = FALSE)
```

```{r echo=FALSE, results='hide', message=FALSE}
CRANpkg <- function (pkg) {
    cran <- "https://CRAN.R-project.org/package"
    fmt <- "[%s](%s=%s)"
    sprintf(fmt, pkg, cran, pkg)
}

Biocpkg <- function (pkg) {
    sprintf("[%s](http://bioconductor.org/packages/%s)", pkg, pkg)
}

library(org.Hs.eg.db)
library(DOSE)
library(ReactomePA)
```



# Introduction

This package is designed for reactome pathway-based analysis. Reactome
is an open-source, open access, manually curated and peer-reviewed
pathway database.

# Citation

If you use `r Biocpkg("ReactomePA")`[@yu_reactomepa_2016] in published research, please cite:

__*G Yu*__, QY He^\*^. ReactomePA: an R/Bioconductor package for reactome
pathway analysis and visualization. __*Molecular BioSystems*__ 2016,
12(2):477-479. doi: [10.1039/C5MB00663E](http://dx.doi.org/10.1039/C5MB00663E)


# 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` to convert biological IDs. For more detail, please refer to [bitr: Biological Id TranslatoR](http://www.bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html#bitr-biological-id-translator).


# Pathway Enrichment Analysis

Enrichment analysis is a widely used approach to identify biological
themes. Here, we implement hypergeometric model to assess whether the
number of selected genes associated with reactome pathway is larger
than expected. The _p_ values were calculated based the hypergeometric model[@boyle2004].


```{r}
library(ReactomePA)
data(geneList)
de <- names(geneList)[abs(geneList) > 1.5]
head(de)
x <- enrichPathway(gene=de,pvalueCutoff=0.05, readable=T)
head(as.data.frame(x))
```

For calculation/parameter details, please refer to the vignette of `r Biocpkg("DOSE")`[@yu_dose_2015]..

## 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)
```


## 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)
```

![](figures/clusterProfiler.png)


# Gene Set Enrichment Analysis

A common approach in analyzing gene expression profiles was identifying differential expressed genes that are deemed interesting. The __*enrichPathway*__ function we demonstrated previously were based on these differential expressed genes. This approach will find genes where the difference is large, but it will not detect a situation where the difference is small, but evidenced in coordinated way in a set of related genes. Gene Set Enrichment Analysis (GSEA)[@subramanian_gene_2005] directly addressed this limitation. All genes can be used in GSEA; GSEA aggregates the per gene statistics across genes within a gene set, therefore making it possible to detect situations where all genes in a predefined set change in a small but coordinated way. For algorithm details, please refer to the vignette of `r Biocpkg("DOSE")`[@yu_dose_2015].

```{r}
y <- gsePathway(geneList, nPerm=10000,
                pvalueCutoff=0.2,
                pAdjustMethod="BH", verbose=FALSE)
res <- as.data.frame(y)
head(res)
```

## 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")
```

# Pathway Visualization

In `r Biocpkg("ReactomePA")`, we also implemented __*viewPathway*__ to visualized the pathway.
```{r fig.height=8, fig.width=8}
viewPathway("E2F mediated regulation of DNA replication", readable=TRUE, foldChange=geneList)
```


# Need helps?


If you have questions/issues, please visit
[ReactomePA homepage](https://guangchuangyu.github.io/software/ReactomePA/) first.
Your problems are mostly documented. If you think you found a bug, please follow
[the guide](https://guangchuangyu.github.io/2016/07/how-to-bug-author/) and
provide a reproducible example to be posted
on
[github issue tracker](https://github.com/GuangchuangYu/ReactomePA/issues).
For questions, please post
to [Bioconductor support site](https://support.bioconductor.org/) and tag your
post with *ReactomePA*.


For Chinese user, you can follow me on [WeChat (微信)](https://guangchuangyu.github.io/blog_images/biobabble.jpg).



# References
