index.Rmd

---
title: 'clusterProfiler: universal enrichment tool for functional and comparative study'
author: 'Guangchuang Yu'
knit: "bookdown::render_book"
site: bookdown::bookdown_site
output:
  bookdown::gitbook:
  bookdown::pdf_book:
    dev: "cairo_pdf"
    latex_engine: xelatex
bibliography: references.bib
biblio-style: apalike
toc_appendix: yes
fontsize: "12pt"
mainfont: Times New Roman
sansfont: Arial
monofontoptions: "Scale=0.7"
linestretch: 1.5
toc-depth: 2
link-citations: yes
documentclass: book
papersize: A4
classoption: twoside
highlight_bw: yes
geometry: "left=35mm,right=35mm,top=25mm,bottom=25mm"
---


```{r pkgs, message=FALSE, warning=FALSE, echo=FALSE}

library(knitr)
opts_chunk$set(message=FALSE, warning=FALSE, eval=TRUE, echo=TRUE, cache=TRUE)
library(clusterProfiler)

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(enrichplot)
library(ReactomePA)
library(clusterProfiler)

options(width=60)

```

# Preface {-}

hello

![](figures/clusterProfiler-diagram.png)

<!--
In recently years, high-throughput experimental techniques such as
microarray, RNA-Seq and mass spectrometry can detect cellular
molecules at systems-level. These kinds of analyses generate huge
quantitaties of data, which need to be given a biological
interpretation. A commonly used approach is via clustering in the gene
dimension for grouping different genes based on their similarities[@yu2010].

To search for shared functions among genes, a common way is to
incorporate the biological knowledge, such as Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG), for identifying
predominant biological themes of a collection of genes.

After clustering analysis, researchers not only want to determine
whether there is a common theme of a particular gene cluster, but also
to compare the biological themes among gene clusters. The manual step
to choose interesting clusters followed by enrichment analysis on each
selected cluster is slow and tedious. To bridge this gap, we designed
[clusterProfiler](https://www.bioconductor.org/packages/clusterProfiler)[@yu2012], for comparing and visualizing functional
profiles among gene clusters.

-->