%\VignetteIndexEntry{DEGreport}
%\VignetteKeywords{DifferentialExpression, Visualization, RNASeq, ReportWriting}
%\VignetteEngine{knitr::knitr}

\documentclass[12pt]{article}

\newcommand{\deseqtwo}{\textit{DESeq2}}
\newcommand{\lowtilde}{\raise.17ex\hbox{$\scriptstyle\mathtt{\sim}$}}

<<knitr, echo=FALSE, results="hide">>=
library("knitr")
opts_chunk$set(tidy=FALSE,dev="png",fig.show="hide",
               fig.width=4,fig.height=4.5,
               message=FALSE)
@


<<style, eval=TRUE, echo=FALSE, results="asis">>=
BiocStyle::latex()
@

\title{DEGreport }
\author{Lorena Pantano}
\date{Modified: 23 May, 2014. Compiled: \today}

\begin{document}

\maketitle



<<package-load,message=FALSE>>=
library(DEGreport)
data(humanSexDEedgeR)
library(edgeR)
@


We are going to do a differential expression analysis with edgeR. We have an
object that is comming from the edgeR package. It countains a gene count matrix
for 85 TSI HapMap individuals, and the gender information. With that, we are
going to apply the `glmFit` function to get genes differentially expressed
between males and females.

<<chunk-1>>=
des<-humanSexDEedgeR$design
fit <- glmFit(humanSexDEedgeR,des)
lrt <- glmLRT(fit)
tab<-cbind(lrt$table,p.adjust(lrt$table$PValue,method="BH"))
detags <- rownames(tab[tab[,5]<=0.1,])
plotSmear(humanSexDEedgeR, de.tags=detags)
@

We need to extract the experiment design data.frame where the condition is
Male or Female.

<<chunk-2>>=
counts<-cpm(humanSexDEedgeR,log=FALSE)
g1<-colnames(counts)[1:41]
g2<-colnames(counts)[42:85]
design<-data.frame(condition=sub("1","Male",sub("0","Female",des[,2])))
@

We are getting the chromosome information for each gene. This way we can colour
genes according autosomic,X or Y chromosomes.

<<chunk-3>>=
data(geneInfo)
@

The main parameters are the column names in group1, and
group2. Then, the count matrix, gene names that are DE, p-values, fold changes
and path to create the report. As optional, you can give colours for each gene,
and the number of permutation.

<<chunk-4>>=
detag10<-detags[1:10]
pval<-tab[,4]
fc<-tab[detag10,1]
@
Run the following lines to create the report. You will need Nozzle.R1 package.

<<chunk-5, eval=FALSE>>=
pathreport<-"~/report" #change this to a proper path
createReport(g1,g2,counts,detag10,pval,fc,pathreport,colors,pop=400)
@

Run the fowlling lines if you want to visualize your expression values by
condition:

<<chunk-6, eval=FALSE>>=
degObj(counts,design,"/tmp/degObj.rda")
library(shiny)
runGist(9930881)
@

You can use individual functions, like degRank or degMean. This will create
specific figures and tables that are included in the report.

<<chunk-7>>=
degMean(pval,counts)
degVar(pval,counts)
degMV(g1,g2,pval,counts)
degMB(detags,g1,g2,counts)
degVB(detags,g1,g2,counts)
@

To create a ranked list of genes to validate, you can use
degRank function. It will use the variation of each genes
to give the ones with highest FC not influenced by the
variation.

<<chunk-8, eval=FALSE>>==
library(coda)
library(rjags)
rank<-degRank(g1,g2,counts[detag10,],fc,400,500)
degPR(rank)
@

\end{document}
