% \VignetteIndexEntry{Reporting on RNA-seq differential expression}
% \VignetteDepends{}
% \VignetteKeywords{reprise}
% \VignettePackage{ReportingTools}
\documentclass[10pt]{article}

\usepackage{times}
\usepackage{hyperref}
\usepackage{Sweave}

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\title{Using ReportingTools in an Analysis of RNA-seq Data}
\author{Jessica L. Larson and Christina Chaivorapol}
\date{\today}

\begin{document}

\maketitle
\tableofcontents

\newpage

\section{Introduction}
The \tt ReportingTools \rm package can be used with differential gene expression results from RNA-sequencing analysis.  In this vignette we show how to \tt publish \rm output from an \tt edgeR\rm, Gene Ontology (GO) and/or Protein family (PFAM) analysis.  In the final section we \tt publish \rm all our pages onto one, creating a comprehensive output page.


\section{Differential expression analysis with \tt edgeR \rm}
In this section we demonstrate how to use the \tt ReportingTools \rm package to generate a table of differentially expressed genes as determined by the \tt edgeR \rm software.
We begin by loading our library and data set.  The \tt mockRnaSeqData \rm contains random RNA-seq output for random mouse genes.

<<load data, eval=FALSE>>=
library(ReportingTools)
data(mockRnaSeqData)
@

Next, we run \tt edgeR \rm to find differentially expressed genes.
<<run_edgeR, eval=FALSE>>=
library(edgeR)
conditions <- c(rep("case",3), rep("control", 3))
d <- DGEList(counts = mockRnaSeqData, group = conditions)
d <- calcNormFactors(d)
d <- estimateCommonDisp(d)
## Get an edgeR object
edgeR.de <- exactTest(d)
@

Now the results can be written to a report using the \tt DGEExact \rm object. 

<<edgeR_report, eval=FALSE>>=
library(lattice)
rep.theme <- reporting.theme()
## Change symbol colors in plots
rep.theme$superpose.symbol$col <- c("blue", "red")
rep.theme$superpose.symbol$fill <- c("blue", "red")
lattice.options(default.theme = rep.theme)
## Publish a report of the top 10 genes with p-values < 0.05 and log-fold change > 2
## In this case, the plots contain the counts from mockRnaSeqData, which are not normalized.
## The publish function does not normalize counts for the countTable argument to allow for
## flexibility in plotting various units (e.g. RPKM instead of counts).

deReport <- HTMLReport(shortName = 'RNAseq_analysis_with_edgeR',
    title = 'RNA-seq analysis of differential expression using edgeR',
    reportDirectory = "./reports")
publish(edgeR.de, deReport, countTable=mockRnaSeqData,
	conditions=conditions, annotation.db = 'org.Mm.eg', 
	pvalueCutoff = .05, lfc = 2, n = 10, name="edgeR")
finish(deReport)

## If you would like to plot normalized counts, run the following commands instead:
## mockRnaSeqData.norm <- d$pseudo.counts
## publish(edgeR.de, deReport, mockRnaSeqData.norm, 
##        conditions, annotation.db = 'org.Mm.eg', 
## 	  pvalueCutoff = .05, lfc = 2, n = 10)
## finish(deReport)
@ 

\begin{figure}
\centering
\includegraphics[scale = .5]{rnaseq1.png}
\caption{Resulting page created by \tt publish \rm for \tt edgeR.de\rm.}
\end{figure}



We can also ouput results of the LRT test from edgeR.

<<edgeR_report, eval=FALSE>>=
d <- DGEList(counts = mockRnaSeqData, group = conditions)
d <- calcNormFactors(d)
design <- model.matrix(~conditions)
d <- estimateGLMCommonDisp(d, design)
d <- estimateGLMTrendedDisp(d, design)
d <- estimateGLMTagwiseDisp(d, design)
fit <- glmFit(d,design)
edgeR.lrt <- glmLRT(fit, coef=2)

deReport2 <- HTMLReport(shortName = 'RNAseq_analysis_with_edgeR_2',
    title = 'RNA-seq analysis of differential expression using edgeR (LRT)',
    reportDirectory = "./reports")
publish(edgeR.lrt, deReport2, countTable=mockRnaSeqData,
	conditions=conditions, annotation.db = 'org.Mm.eg', 
	pvalueCutoff = .05, lfc = 2, n = 10, name="edgeRlrt")
finish(deReport2)
@ 


\section{Differential expression analysis with \tt DESeq \rm and \tt DESeq2 \rm}
In this section we demonstrate how to use the \tt ReportingTools \rm package to generate a table of differentially expressed genes as determined by the \tt DESeq \rm and \tt DESeq2 \rm packages.


First, we run \tt DESeq \rm to find differentially expressed genes.
<<run_DESeq, eval=FALSE>>=
library(DESeq)
cds<-newCountDataSet(mockRnaSeqData, conditions)
cds<-estimateSizeFactors(cds)
cds<-estimateDispersions(cds)
res<-nbinomTest(cds,"control", "case" )
@

Now the results can be written to a report after converting the \tt DESeq \rm output to a data frame. This is done using the \tt makeDESeqDF \rm command, which is a built-in function to convert DESeq differential expression output to a more meaningful data frame with plots, details about the genes, etc.  With \tt ReportingTools \rm, you can replace the \tt makeDESeqDf \rm with any function you like for more flexibility (see the basic vignette for more details and examples).

<<DESeq_report, eval=FALSE>>=
desReport <- HTMLReport(shortName = 'RNAseq_analysis_with_DESeq',
    title = 'RNA-seq analysis of differential expression using DESeq',
    reportDirectory = "./reports")
publish(res,desReport,name="df",countTable=mockRnaSeqData, pvalueCutoff=0.05,
    conditions=conditions,annotation.db="org.Mm.eg.db", 
    expName="deseq",reportDir="./reports", .modifyDF=makeDESeqDF)
finish(desReport)
@ 

\begin{figure}
\centering
\includegraphics[scale = .5]{rnaseq2.png}
\caption{Resulting page created by \tt makeDESeqDF \rm}
\end{figure}

We can also run \tt DESeq2 \rm to find differentially expressed genes.
<<run_DESeq2, eval=FALSE>>=
library(DESeq2)
conditions <- c(rep("case",3), rep("control", 3))
mockRna.dse <- DESeqDataSetFromMatrix(countData = mockRnaSeqData,
                        colData = as.data.frame(conditions), design = ~ conditions)
colData(mockRna.dse)$conditions <- factor(colData(mockRna.dse)$conditions, levels=c("control", "case"))
## Get a DESeqDataSet object
mockRna.dse <- DESeq(mockRna.dse)
@

Now the results can be written to a report using the \tt DESeqDataSet \rm object.  

<<DESeq2_report, eval=FALSE>>=
des2Report <- HTMLReport(shortName = 'RNAseq_analysis_with_DESeq2',
    title = 'RNA-seq analysis of differential expression using DESeq2',
    reportDirectory = "./reports")
publish(mockRna.dse,des2Report, pvalueCutoff=0.05,
    annotation.db="org.Mm.eg.db", factor = colData(mockRna.dse)$conditions,
    reportDir="./reports")
finish(des2Report)
@ 

\begin{figure}
\centering
\includegraphics[scale = .5]{rnaseq3.png}
\caption{Resulting page created with \tt DESeqDataSet \rm object from \tt DESeq2 \rm analysis}
\end{figure}

\section{GO analysis using GOstats}
This section will demonstrate how to use \tt ReportingTools \rm to write a table of GO analysis results to an html file.  First we select our genes of interest, and then run the \tt hyperGTest\rm.

<<Do GO analysis, eval=FALSE>>=
library(GOstats)
library(org.Mm.eg.db)
tt <- topTags(edgeR.de, n = 1000, adjust.method = 'BH', sort.by = 'p.value')
selectedIDs <- rownames(tt$table)
universeIDs <- rownames(mockRnaSeqData)
goParams <- new("GOHyperGParams", 
    geneIds = selectedIDs, 
    universeGeneIds = universeIDs, 
    annotation ="org.Mm.eg" , 
    ontology = "MF", 
    pvalueCutoff = 0.01,
    conditional = TRUE, 
    testDirection = "over")
goResults <- hyperGTest(goParams)
@

With these results, we can then make the GO report.  

<<make the GO report, eval=FALSE>>=
goReport <- HTMLReport(shortName = 'go_analysis_rnaseq',
	title = "GO analysis of mockRnaSeqData",
	reportDirectory = "./reports")
publish(goResults, goReport, selectedIDs=selectedIDs, annotation.db="org.Mm.eg", 
	pvalueCutoff= 0.05)
finish(goReport)
@


\section{PFAM analysis}
In this section, we show how to use \tt ReportingTools \rm to write a table of PFAM analysis results to an html file.  First we run the \tt hyperGTest \rm using our genes of interest from the previous section.


<<Do PFAM analysis, eval=FALSE>>=
library(Category)
params <- new("PFAMHyperGParams", 
	geneIds= selectedIDs, 
	universeGeneIds=universeIDs, 
	annotation="org.Mm.eg",
	pvalueCutoff= 0.01,
	testDirection="over")
PFAMResults <- hyperGTest(params)
@



Then we make the PFAM report.
<<make the PFAM report, eval=FALSE>>=
PFAMReport <- HTMLReport(shortName = 'pfam_analysis_rnaseq',
	title = "PFAM analysis of mockRnaSeqData",
	reportDirectory = "./reports")
publish(PFAMResults, PFAMReport, selectedIDs=selectedIDs, annotation.db="org.Mm.eg",categorySize=5)
finish(PFAMReport)
@

\begin{figure}
\centering
\includegraphics[scale = .5]{rnaseq4.png}
\caption{Resulting page created by \tt publish \rm for \tt PFAMResults\rm}
\end{figure}


\section{Putting it all together}
Here, we make an index page that puts all three analyses together for easy navigation.
<<make the index page, eval=FALSE>>=
indexPage <- HTMLReport(shortName = "indexRNASeq",
    title = "Analysis of mockRnaSeqData",
    reportDirectory = "./reports")
publish(Link(list(deReport,des2Report, goReport, PFAMReport), report = indexPage),
    indexPage)
finish(indexPage)
@

\begin{figure}
\centering
\includegraphics[scale = .5]{rnaseq5.png}
\caption{Resulting page created by calling \tt publish \rm on all our analysis pages}
\end{figure}

\end{document}