---
title: "Visualization of Functional Enrichment Result"
author: "\\

	Guangchuang Yu\\

        School of Public Health, The University of Hong Kong"
date: "`r Sys.Date()`"
bibliography: enrichplot.bib
biblio-style: apalike
output:
  prettydoc::html_pretty:
    toc: true
    theme: cayman
    highlight: github
  pdf_document:
    toc: true
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{enrichplot introduction}
  %\VignetteDepends{ggplot2}
  %\VignetteDepends{ggraph}
  %\usepackage[utf8]{inputenc}
---



```{r include=FALSE}
knitr::opts_chunk$set(warning = FALSE,
                      message = TRUE)

library(DOSE)
library(org.Hs.eg.db)
library(clusterProfiler)
library(ggplot2)
library(ggraph)
library(cowplot)
library(UpSetR)
library(enrichplot)

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)
}
```

The `r Biocpkg("enrichplot")` package implements several methods for enrichment
result visualization to help interpretation. It supports both hypergeometric
test and gene set enrichment analysis. Both of them are widely used to
characterize pathway/function relationships to elucidate molecular mechanisms
from high-throughput genomic data.

The `r Biocpkg("enrichplot")` package supports visualizing enrichment results
obtained from `r Biocpkg("DOSE")` [@yu_dose_2015],
`r Biocpkg("clusterProfiler")` [@yu_clusterprofiler_2012],
`r Biocpkg("ReactomePA")` [@yu_reactomepa_2016] and `r Biocpkg("meshes")`.


## Induced GO DAG graph

Gene Ontology (GO) is organized as a directed acyclic graph. An insighful way of
looking at the results of the analysis is to investigate how the significant GO
terms are distributed over the GO graph. The `goplot` function shows subgraph
induced by most significant GO terms.


```{r fig.width=12, fig.height=8}
library(clusterProfiler)
data(geneList, package="DOSE")
de <- names(geneList)[abs(geneList) > 2]
ego <- enrichGO(de, OrgDb = "org.Hs.eg.db", ont="BP", readable=TRUE)

library(enrichplot)
goplot(ego)
```

## Bar plot


Bar plot is the most widely used method to visualize enriched terms. It depicts
the enrichment scores (*e.g.* p values) and gene count or ratio as bar height
and color.

```{r fig.width=12, fig.height=8}
barplot(ego, showCategory=20)
```

## Dot plot


Dot plot is similar to bar plot with the capability to encode another score as
dot size. Both `barplot` and `dotplot` supports facetting to visualize
sub-ontologies simultaneously.

```{r fig.width=12, fig.height=8}
dotplot(ego, showCategory=30)

go <- enrichGO(de, OrgDb = "org.Hs.eg.db", ont="all")
dotplot(go, split="ONTOLOGY") + facet_grid(ONTOLOGY~., scale="free")
```


## Gene-Concept Network

Both the `barplot` and `dotplot` only displayed most significant enriched terms,
while users may want to know which genes are involved in these significant
terms. The `cnetplot` depicts the linkages of genes and biological concepts
(*e.g.* GO terms or KEGG pathways) as a network.

```{r fig.width=12, fig.height=8}
## remove redundent GO terms
ego2 <- simplify(ego)
cnetplot(ego2, foldChange=geneList)
cnetplot(ego2, foldChange=geneList, circular = TRUE, colorEdge = TRUE)
```

## UpSet Plot


The `upsetplot` is an alternative to `cnetplot` for visualizing the complex
association between genes and gene sets. It emphasizes the gene overlapping
among different gene sets.

```{r fig.width=12, fig.height=5}
upsetplot(ego)
```

## Heatmap-like functional classification


The `heatplot` is similar to `cnetplot`, while displaying the relationships as a
heatmap. The gene-concept network may become too complicated if user want to
show a large number significant terms. The `heatplot` can simplify the result
and more easy to identify expression patterns.

```{r fig.width=16, fig.height=4}
heatplot(ego2)
heatplot(ego2, foldChange=geneList)
```


## Enrichment Map


Enrichment map organizes enriched terms into a network with edges connecting
overlapping gene sets. In this way, mutually overlapping gene sets are tend to
cluster together, making it easy to identify functional module.



```{r fig.width=12, fig.height=10}
emapplot(ego2)
```

## ridgeline plot for expression distribution of GSEA result

The `ridgeplot` will visualize expression distributions of core enriched genes
for GSEA enriched categories. It helps users to interpret up/down-regulated pathways.

```{r fig.width=12, fig.height=8, message=FALSE}
kk <- gseKEGG(geneList, nPerm=10000)
ridgeplot(kk)
```


## running score and preranked list of GSEA result

Running score and preranked list are traditional methods for visualizing GSEA
result. The `r Biocpkg("enrichplot")` package supports both of them to visualize
the distribution of the gene set and the enrichment score.


```{r fig.width=12, fig.height=4}
gseaplot(kk, geneSetID = 1, by = "runningScore", title = kk$Description[1])
gseaplot(kk, geneSetID = 1, by = "preranked", title = kk$Description[1])
```

```{r fig.width=12, fig.height=8}
gseaplot(kk, geneSetID = 1, title = kk$Description[1])
```


# References