%\VignetteIndexEntry{MetaGxOvarian: a package for ovarian cancer gene expression analysis}
%\VignetteDepends{xtable}
%\VignetteSuggests{}
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%\VignettePackage{MetaGxOvarian}

\documentclass[11pt]{article}

\usepackage[utf8]{inputenc}
\usepackage{authblk}
\usepackage{color}


\title{MetaGxOvarian: a package for ovarian cancer gene expression analysis}

\author[1]{Michael Zon}
\author[1,2]{Deena M.A. Gendoo}
\author[1]{Natchar Ratanasirigulchai}
\author[2]{Gregory Chen}
\author[3,4]{Levi Waldron}
\author[1,2]{Benjamin Haibe-Kains\thanks{benjamin.haibe.kains@utoronto.ca }}

\affil[1]{Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada}
\affil[2]{Department of Medical Biophysics, University of Toronto, Toronto, Canada}
\affil[3]{Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA}
\affil[4]{Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA}

\SweaveOpts{highlight=TRUE, tidy=TRUE, keep.space=TRUE, keep.blank.space=FALSE, keep.comment=TRUE}

<<setup,echo=FALSE,results=hide,eval=FALSE>>=
options(keep.source=TRUE, width = 50)
@

\begin{document}
\setkeys{Gin}{width=1.2\textwidth}
\SweaveOpts{concordance=TRUE}

\maketitle
\tableofcontents

%------------------------------------------------------------
\section{Installing the Package}
%------------------------------------------------------------

The MetaGxOvarian package is a compendium of Ovarian Cancer datasets.
The package is publicly available and can be installed from Bioconductor into R version 3.5.0 or higher.

To install the MetaGxOvarian package from Bioconductor:
<<install-pkg,eval=FALSE>>=
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("MetaGxOvarian")
@

%------------------------------------------------------------
\section{Loading Datasets}
%------------------------------------------------------------
First we load the MetaGxOvarian package into the workspace.

To load the packages into R, please use the following commands:
<<loadlib>>=
library(MetaGxOvarian)
esets = MetaGxOvarian::loadOvarianEsets()[[1]]
@

This will load 26 expression datasets. Users can modify the parameters of the function to restrict datasets that do not meet certain criteria for loading. Some example parameters are shown below:

\begin{description}
\item Datasets:  Retain only genes that are common across all platforms loaded (default = FALSE)
\item Datasets:  Retain studies with a minimum sample size (default = 0)
\item Datasets:  Retain studies with a minimum umber of genes (default = 0)
\item Datasets:  Retain studies with a minimum number of survival events (default = 0)
\item Datasets:  Remove duplicate samples (default = TRUE)
\end{description}


%------------------------------------------------------------
\section{Obtaining Sample Counts in Datasets}
%------------------------------------------------------------

To obtain the number of samples per dataset, run the following:

<<sampleNumber-summary, results=tex>>=
numSamples <- vapply(seq_along(esets), FUN=function(i, esets){
  length(sampleNames(esets[[i]]))
  }, numeric(1), esets=esets)


SampleNumberSummaryAll <- data.frame(NumberOfSamples = numSamples,
                                     row.names = names(esets))
total <- sum(SampleNumberSummaryAll[,"NumberOfSamples"])
SampleNumberSummaryAll <- rbind(SampleNumberSummaryAll, total)
rownames(SampleNumberSummaryAll)[nrow(SampleNumberSummaryAll)] <- "Total"

require(xtable)
print(xtable(SampleNumberSummaryAll,digits = 2), floating = FALSE)
@
%------------------------------------------------------------
\section{Assess Phenotype Data}
%------------------------------------------------------------

We can also obtain a summary of the phenotype data (pData) for each expression dataset.
Here, we assess the proportion of samples in every datasets that contain a specific pData variable.
<<sampleNumberSummariesPdata, fig=TRUE>>=
#pData Variables
pDataID <- c("sample_type", "histological_type", "primarysite", "summarygrade",
             "summarystage", "tumorstage", "grade",
             "age_at_initial_pathologic_diagnosis", "pltx", "tax",
             "neo", "days_to_tumor_recurrence", "recurrence_status",
             "days_to_death", "vital_status")


pDataPercentSummaryTable <- NULL
pDataSummaryNumbersTable <- NULL

pDataSummaryNumbersList = lapply(esets, function(x)
  vapply(pDataID, function(y) sum(!is.na(pData(x)[,y])), numeric(1)))

pDataPercentSummaryList = lapply(esets, function(x)
  vapply(pDataID, function(y)
    sum(!is.na(pData(x)[,y]))/nrow(pData(x)), numeric(1))*100)

pDataSummaryNumbersTable = sapply(pDataSummaryNumbersList, function(x) x)
pDataPercentSummaryTable = sapply(pDataPercentSummaryList, function(x) x)

rownames(pDataSummaryNumbersTable) <- pDataID
rownames(pDataPercentSummaryTable) <- pDataID
colnames(pDataSummaryNumbersTable) <- names(esets)
colnames(pDataPercentSummaryTable) <- names(esets)

pDataSummaryNumbersTable <- rbind(pDataSummaryNumbersTable, total)
rownames(pDataSummaryNumbersTable)[nrow(pDataSummaryNumbersTable)] <- "Total"


# Generate a heatmap representation of the pData
pDataPercentSummaryTable<-t(pDataPercentSummaryTable)
pDataPercentSummaryTable<-cbind(Name=(rownames(pDataPercentSummaryTable))
                                ,pDataPercentSummaryTable)

nba<-pDataPercentSummaryTable
gradient_colors = c("#ffffff","#ffffd9","#edf8b1","#c7e9b4","#7fcdbb",
                    "#41b6c4","#1d91c0","#225ea8","#253494","#081d58")

library(lattice)
nbamat<-as.matrix(nba)
rownames(nbamat)<-nbamat[,1]
nbamat<-nbamat[,-1]
Interval<-as.numeric(c(10,20,30,40,50,60,70,80,90,100))

levelplot(nbamat,col.regions=gradient_colors,
          main="Available Clinical Annotation",
          scales=list(x=list(rot=90, cex=0.5),
                      y= list(cex=0.5),key=list(cex=0.2)),
          at=seq(from=0,to=100,length=10),
          cex=0.2, ylab="", xlab="", lattice.options=list(),
          colorkey=list(at=as.numeric(factor(c(seq(from=0, to=100, by=10)))),
                  labels=as.character(c( "0","10%","20%","30%", "40%","50%",
                                         "60%", "70%", "80%","90%", "100%"),
                                      cex=0.2,font=1,col="brown",height=1,
                                      width=1.4), col=(gradient_colors)))

@



%------------------------------------------------------------
\section{Session Info}
%------------------------------------------------------------
<<sessionInfo,echo=FALSE,results=tex,eval=TRUE>>=
toLatex(sessionInfo())
@

\end{document}