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<<include=FALSE>>=
library(DOSE)
library(GO.db)
library(org.Hs.eg.db)
library(pathview)
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)
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\title{\textsf{\textbf{Using \Rpackage{clusterProfiler} to identify and compare functional profiles of gene lists}}}
\author{Guangchuang Yu \\
School of Biological Sciences \\
The University of Hong Kong, Hong Kong SAR, China \\
email: \texttt{gcyu@connect.hku.hk}}

\begin{document}
\maketitle

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\tableofcontents

\section{Introduction}

In recently years, high-throughput experimental techniques such as
microarray, RNA-Seq and mass spectrometry can detect cellular
moleculars 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 \cite{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
\Rpackage{clusterProfiler} \cite{yu2012}, for comparing and visualizing functional
profiles among gene clusters.

\section{Citation}

Please cite the following articles when using
\Rpackage{clusterProfiler}. \\


G Yu, LG Wang, Y Han, QY He. clusterProfiler: an R package for
comparing biological themes among gene clusters. \textit{OMICS: A
  Journal of Integrative Biology}. 2012, 16(5), 284-287. \\

\section{Gene Ontology Classification}
In \Rpackage{clusterProfiler}, \Rfunction{groupGO} is designed for gene classification based on GO distribution at a specific level.

<<groupGO>>=
require(DOSE)
data(geneList)
gene <- names(geneList)[abs(geneList) > 2]
head(gene)
ggo <- groupGO(gene=gene, organism="human",
				ont="BP", level=3, readable=TRUE)
head(summary(ggo))
@

\section{Enrichment Analysis}
\subsection{Hypergeometric model}
Enrichment analysis \cite{boyle2004} is a widely used approach to identify biological
themes. Here we implement hypergeometric model to assess whether the
number of selected genes associated with disease is larger than
expected. 

To determine whether any terms annotate a specified list of
    genes at frequency greater than that would be expected by chance,
    \Rpackage{clusterProfiler} calculates a p-value using the hypergeometric distribution:

$
p = 1 - \displaystyle\sum_{i = 0}^{k-1}
  \frac{
      {M \choose i}
      {{N-M} \choose {n-i}}
    } {
      {N \choose n}
    }
$

In this equation, \textit{N} is the total number of genes in the
background distribution, \textit{M} is the number of genes within that
distribution that are annotated (either directly or indirectly) to the
node of interest, \textit{n} is the size of the list of genes of
interest and \textit{k} is the number of genes within that list which
are annotated to the node. The background distribution by default is
all the genes that have annotation.

P-values were adjusted for multiple comparison, and q-values were also calculated for FDR control.

\subsection{GO enrichment analysis}

<<enrichGO>>=
ego <- enrichGO(gene=gene,
				universe = names(geneList),
              organism="human",
              ont="CC",
              pvalueCutoff=0.01,
              readable=TRUE)
head(summary(ego))
@

\subsection{KEGG pathway enrichment analysis}

<<enrichKEGG>>=
kk <- enrichKEGG(gene=gene,
                organism="human",
                pvalueCutoff=0.01,
                readable=TRUE)
head(summary(kk))
@

\subsection{DO enrichment analysis}

Disease Ontology (DO) enrichment analysis is implemented in \Rpackage{DOSE}, please refer to the package vignettes. The \Rfunction{enrichDO} function is very useful for identifying disease association of interesting genes.

\subsection{Reactome pathway enrichment analysis}

With the demise of KEGG (at least without subscription), the KEGG pathway data in Bioconductor will not update and we encourage user to analyze pathway using \Rpackage{ReactomePA} which use Reactome as a source of pathway data. The function call of \Rfunction{enrichPathway} in \Rpackage{ReactomePA} is consistent with \Rfunction{enrichKEGG}.

\subsection{Function call}
The function calls of \Rfunction{groupGO}, \Rfunction{enrichGO},
\Rfunction{enrichKEGG}, \Rfunction{enrichDO} and \Rfunction{enrichPathway} are consistent. 
The input parameters of
\textit{gene} is a vector of entrezgene (for human and mouse) or ORF
(for yeast) IDs, and \textit{organism} should be supported species (please refer to the manual of the specific function).

For GO analysis, \textit{ont} must be assigned to one of "BP", "MF",
and "CC" for biological process, molecular function and cellular
component, respectively. In \Rfunction{groupGO}, the \textit{level}
specify the GO level for gene projection.

In enrichment analysis, the \textit{pvalueCutoff} is to restrict the
result based on their pvalues and the adjusted p values. \textit{Q-values} were also calculated for controlling
false discovery rate (FDR). 

The \textit{readable} is a logical parameter to indicate the
input gene IDs will map to gene symbols or not.

\subsection{Visualization}
The output of \Rfunction{groupGO}, \Rfunction{enrichGO} and \Rfunction{enrichKEGG} can be visualized by bar plot and category-gene-network plot. 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.

\subsubsection{barplot}
<<barplot, fig.height=5, fig.width=6>>=
barplot(ggo, drop=TRUE, showCategory=12)
@



<<barplot-enrich, fig.height=5, fig.width=8>>=
barplot(ego, showCategory=8)
@


\subsubsection{cnetplot}
In order to consider the potentially biological complexities in which a gene may belong to multiple annotation categories and provide information of numeric changes if available, we developed \Rfunction{cnetplot} function to extract the complex association.
<<cnetplot, fig.height=14, fig.width=14>>=
cnetplot(ego, categorySize="pvalue", foldChange=geneList)
@


<<cnetplot-KEGG, fig.height=14, fig.width=14>>=
cnetplot(kk, categorySize="geneNum", foldChange=geneList)
@



\subsubsection{pathview from pathview package}
\Rpackage{clusterProfiler} users can also use \Rfunction{pathview} from the \Rpackage{pathview} \cite{luo_pathview} to visualize KEGG pathway.

The following example illustrate how to visualize "hsa04110" pathway, which was enriched in our previous analysis.

<<viewKEGG>>=
require(pathview)
hsa04110 <- pathview(gene.data=geneList, pathway.id="hsa04110", species="hsa", limit=list(gene=max(abs(geneList)), cpd=1))
@

\begin{figure}[ht]
\centering
\includegraphics[width=.9\textwidth,type=png,ext=.png,read=.png]{hsa04110.pathview}
\caption{visualize KEGG pathway using pathview}
\label{viewKEGG}
\end{figure}

For further information, please refer to the vignette of \Rpackage{pathview} \cite{luo_pathview}.


\section{Biological theme comparison}
\Rpackage{clusterProfiler} was developed for biological theme comparison, and it provides a function, \Rfunction{compareCluster}, to automatically calculate enriched functional categories of each gene clusters.

<<compareCluster, fig.height=8, fig.width=8>>=
data(gcSample)
ck <- compareCluster(geneCluster=gcSample, fun="enrichKEGG")
plot(ck)
@


By default, only top 5 (most significant) categories of each cluster
was plotted. User can changes the parameter \textit{showCategory} to
specify how many categories of each cluster to be plotted, and if
\textit{showCategory} was set to \textit{NULL}, the whole result will
be plotted.

The dot sizes were based on their corresponding row percentage by
default, and user can set the parameter \textit{by} to "count" to make
the comparison based on gene counts. We choose "percentage" as default
parameter to represent the size of dots, since some categories may
contain a large number of genes, and make the dot sizes of those small
categories too small to compare. To provide the full information, we
also provide number of identified genes in each category (numbers in
parentheses), as shown in Figure 3. If the dot sizes were based on
"count", the row numbers will not shown.

The p-values indicate that which categories are more likely to have
biological meanings. The dots in the plot are color-coded based on
their corresponding p-values. Color gradient ranging from red to blue
correspond to in order of increasing p-values. That is, red indicate
low p-values (high enrichment), and blue indicate high p-values (low
enrichment). P-values and adjusted p-values were filtered out by the threshold giving by
parameter \textit{pvalueCutoff}, and FDR can be estimated by \textit{qvalue}.

User can refer to the example in \cite{yu2012}; we analyzed the publicly available expression dataset of breast tumour tissues from 200 patients (GSE11121, Gene Expression Omnibus) \cite{schmidt2008}. We
identified 8 gene clusters from differentially expressed genes, and
using \Rfunction{compareCluster} to compare these gene clusters by
their enriched biological process.


Another example was shown in \cite{yu2011}, we calculated functional
similarities among viral miRNAs using method described in
\cite{yu_new_2011}, and compared significant KEGG pathways regulated
by different viruses using \Rfunction{compareCluster}.

The comparison function was designed as a general-package for
comparing gene clusters of any kind of ontology associations, not
only \Rfunction{groupGO}, \Rfunction{enrichGO}, and \Rfunction{enrichKEGG} this package provided, but also other biological and biomedical ontologies, for instance, \Rfunction{enrichDO} from \Rpackage{DOSE} and \Rfunction{enrichPathway} from \Rpackage{ReactomePA} work fine with \Rfunction{compareCluster} for comparing biological themes in disease and reactome pathway perspective. More details can be
  found in the vignettes of \Rpackage{DOSE} and \Rpackage{ReactomePA}.

\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{clusterProfiler}
\end{document}