% \VignetteEngine{knitr}
% \VignetteIndexEntry{ReactomePA - an R package for Reactome Pathway Analysis}
% \VignettePackage{ReactomePA}

% To compile this document, run the commands within R
% require(knitr); knit2pdf("ReactomePA.Rnw")

\documentclass[12pt]{article}
\usepackage[a4paper,left=3cm,top=2cm,bottom=3cm,right=3cm,ignoreheadfoot]{geometry}
\pagestyle{empty}
\usepackage[usenames,dvipsnames]{xcolor}
\usepackage{helvet}
\usepackage[titletoc]{appendix}
\usepackage{tocloft}

\setlength{\parindent}{0em}
\setlength{\parskip}{.5em}

\renewcommand{\familydefault}{\sfdefault}

\newcommand{\Rcode}[1]{{\texttt{#1}}}
\newcommand{\Robject}[1]{{\small\texttt{#1}}}
\newcommand{\Rfunction}[1]{\Robject{#1}}
\newcommand{\Rpackage}[1]{\textit{#1}}
\newcommand{\Rparameter}[1]{\textit{#1}}
\newcommand{\Rclass}[1]{\textit{#1}}
\newcommand{\R}{\textsf{R}}

\usepackage[
  bookmarks,%
  colorlinks,%
  linktoc=section,%
  linkcolor=RedViolet,%
  citecolor=RedViolet,%
  urlcolor=RedViolet,%
  linkbordercolor={1 1 1},%
  citebordercolor={1 1 1},%
  urlbordercolor={1 1 1},%
  raiselinks,%
  plainpages,%
  pdftex
  ]{hyperref}


\usepackage{cite}
\renewcommand{\floatpagefraction}{0.9}

\usepackage{sectsty}
\sectionfont{\sffamily\bfseries\color{RoyalBlue}\sectionrule{0pt}{0pt}{-1ex}{1pt}}
\subsectionfont{\sffamily\bfseries\color{RoyalBlue}}
\subsubsectionfont{\sffamily\bfseries\color{RoyalBlue}}
\usepackage{fancyhdr}
\pagestyle{fancy}
\fancyhead{}
\fancyfoot{}
\lfoot{}\cfoot{}\rfoot{}
\renewcommand{\headrule}{}

\usepackage{graphicx}
%\usepackage{xstring}



<<include=FALSE>>=
library(DOSE)
library(ReactomePA)
library(clusterProfiler)
library(knitr)
opts_chunk$set(tidy=TRUE,tidy.opts=list(keep.blank.line=FALSE, width.cutoff=50),out.truncate=80,out.lines=6,cache=TRUE,dev='pdf',include=TRUE,fig.width=6,fig.height=6,resolution=150)
@

\newcommand{\fixme}[1]{{\textbf{Fixme:} \textit{\textcolor{blue}{#1}}}}


\title{\textsf{\textbf{Reactome Pathway Analysis}}}
\author{Guangchuang Yu}

\begin{document}
\maketitle

<<options,results='hide',echo=FALSE>>=
options(digits=3, width=80, prompt=" ", continue=" ")
@

\tableofcontents


\section{Introduction}

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

\section{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 \cite{boyle2004},

<<load sample data>>=
require(DOSE)
data(geneList)
de <- names(geneList)[abs(geneList) > 1]
head(de)
require(ReactomePA)
x <- enrichPathway(gene=de,pvalueCutoff=0.05, readable=T)
head(summary(x))
@

\subsection{Visualize enrichment result}
We implement bar plot, 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.
<<barplot, fig.cap="barplot of Reactome Pathway enrichment result.", fig.align="center", fig.height=5, fig.width=8, out.width="0.8\\textwidth", fig.pos="h">>=
barplot(x, showCategory=8)
@

Enrichment map can be viusalized by \Rfunction{enrichMap} :
<<enrichMap, fig.cap="enrichment map of enrichment result", fig.align="center", fig.height=16, fig.width=16, out.width="0.9\\textwidth", fig.pos="h">>=  
enrichMap(x)
@ 

In order to consider the potentially biological complexities in which a gene may belong to multiple annotation categories, we developed \Rfunction{cnetplot} function to extract the complex association between genes and diseases.
<<cnetplot, fig.cap="cnetplot of Reactome Pathway enrichment result.", fig.align="center", fig.height=16, fig.width=16, out.width="0.9\\textwidth", fig.pos="h">>=
cnetplot(x, categorySize="pvalue", foldChange=geneList)
@

\subsection{Comparing enriched reactome pathways among gene clusters with clusterProfiler}
We have developed an \textsf{R} package \Rpackage{clusterProfiler} \cite{yu_clusterprofiler:_2012} for comparing biological themes among gene clusters. \Rpackage{ReactomePA} works fine with \Rpackage{clusterProfiler} and can compare biological themes at reactome pathway perspective.
 
<<clusterProfiler, fig.cap="ReactomePA with clusterProfiler.", fig.align="center", fig.height=6, fig.width=11, out.width="0.9\\textwidth", fig.pos="h">>= 
require(clusterProfiler)
data(gcSample)
res <- compareCluster(gcSample, fun="enrichPathway")
plot(res)
@

\section{Gene Set Enrichment Analysis}
A common approach in analyzing gene expression profiles was identifying differential expressed genes that are deemed interesting. The \Rfunction{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) 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. 

<<GSEA analysis>>=
y <- gsePathway(geneList, nPerm=100, 
                  minGSSize=120, pvalueCutoff=0.05, 
                  pAdjustMethod="BH", verbose=FALSE)
res <- summary(y)
head(res)
@

\subsection{Visualize GSEA result}
<<enrichMap2, fig.cap="enrichment map of gsea result", fig.align="center", fig.height=16, fig.width=16, out.width="0.9\\textwidth", fig.pos="h">>=  
enrichMap(y)
@

<<gseaplot, fig.cap="plotting gsea result", fig.align="center", fig.height=6, fig.width=6, out.width="0.6\\textwidth", fig.pos="h">>=  
gseaplot(y, geneSetID = "1280215")
@

\section{Pathway Visualization}

In \Rpackage{ReactomePA}, we also implemented \Rfunction{viewPathway} to visualized the pathway.
<<viewPathway, fig.cap="Reactome Pathway visualization.", fig.align="center", fig.height=16, fig.width=16, out.width="0.9\\textwidth", fig.pos="h">>=
viewPathway("E2F mediated regulation of DNA replication", readable=TRUE, foldChange=geneList)
@


\section{Session Information}

The version number of R and packages loaded for generating the vignette were:

<<sessInfo, results='asis', echo=FALSE>>=
toLatex(sessionInfo())
@

\bibliographystyle{unsrt}
\bibliography{ReactomePA}
\end{document}