setClass("MRexperiment", contains=c("eSet"), representation=representation(expSummary = "environment"),prototype = prototype( new( "VersionedBiobase",versions = c(classVersion("eSet"),MRexperiment = "1.0.0" ))))
setMethod("[", "MRexperiment", function (x, i, j, ..., drop = FALSE) {
obj= callNextMethod()
if(!missing(j)){
obj@expSummary = new("environment",expSummary=as(expSummary(x)[j,1:2,...,drop=drop],"AnnotatedDataFrame"),cumNormStat=x@expSummary$cumNormStat)
if(length(pData(obj))>0){
for(i in 1:length(pData(obj))){
if(is.factor(pData(obj)[,i])){
pData(obj)[,i] = factor(pData(obj)[,i])
} else {
pData(obj)[,i] = pData(obj)[,i]
}
}
}
}
obj
})
setMethod("colSums", signature ="MRexperiment", function (x, ...) {
callNextMethod(MRcounts(x),...)
})
setMethod("rowSums", signature="MRexperiment", function (x, ...) {
callNextMethod(MRcounts(x),...)
})
setMethod("rowMeans", signature="MRexperiment", function (x, ...) {
callNextMethod(MRcounts(x),...)
})
setMethod("colMeans", signature="MRexperiment", function (x, ...) {
callNextMethod(MRcounts(x),...)
})
#' Create a MRexperiment object
#'
#' This function creates a MRexperiment object from a matrix or data frame of
#' count data.
#'
#' See \code{\link{MRexperiment-class}} and \code{eSet} (from the Biobase
#' package) for the meaning of the various slots.
#'
#' @param counts A matrix or data frame of count data. The count data is
#' representative of the number of reads annotated for a feature (be it gene,
#' OTU, species, etc). Rows should correspond to features and columns to
#' samples.
#' @param phenoData An AnnotatedDataFrame with pertinent sample information.
#' @param featureData An AnnotatedDataFrame with pertinent feature information.
#' @param libSize libSize, library size, is the total number of reads for a
#' particular sample.
#' @param normFactors normFactors, the normalization factors used in either the
#' model or as scaling factors of sample counts for each particular sample.
#' @return an object of class MRexperiment
#' @author Joseph N Paulson, jpaulson@@umiacs.umd.edu
#' @examples
#'
#' cnts = matrix(abs(rnorm(1000)),nc=10)
#' obj <- newMRexperiment(cnts)
#'
newMRexperiment <- function(counts, phenoData=NULL, featureData=NULL,libSize=NULL, normFactors=NULL) {
counts= as.matrix(counts)
if( is.null( featureData ) ){
featureData <- annotatedDataFrameFrom(counts, byrow=TRUE)
}
if( is.null( phenoData ) ){
phenoData <- annotatedDataFrameFrom(counts, byrow=FALSE)
}
if( is.null( libSize ) ){
libSize <- as.matrix(colSums(counts))
rownames(libSize) = colnames(counts)
}
if( is.null( normFactors ) ){
normFactors <- as.matrix(rep( NA_real_, length(libSize) ))
rownames(normFactors) = rownames(libSize)
}
obj <-new("MRexperiment", assayData = assayDataNew("environment",counts=counts),phenoData = phenoData,featureData = featureData ,expSummary = new("environment",expSummary=annotatedDataFrameFrom(counts,byrow=FALSE),cumNormStat=NULL))
obj@expSummary$expSummary$libSize = libSize;
obj@expSummary$expSummary$normFactors=normFactors;
validObject(obj)
obj
}
setValidity( "MRexperiment", function( object ) {
if( is.null(assayData(object)$counts))
return( "There are no counts!" )
# if( ncol(MRcounts(object)) != length(normFactors(object)))
# return( "Experiment summary got hacked!" )
# if( ncol(MRcounts(object)) != length(libSize(object)))
# return( "Experiment summary got hacked!" )
TRUE
} )
#' Accessor for the counts slot of a MRexperiment object
#'
#' The counts slot holds the raw count data representing (along the rows) the
#' number of reads annotated for a particular feature and (along the columns)
#' the sample.
#'
#'
#' @name MRcounts
#' @aliases MRcounts,MRexperiment-method MRcounts
#' @docType methods
#' @param obj a \code{MRexperiment} object.
#' @param norm logical indicating whether or not to return normalized counts.
#' @param log TRUE/FALSE whether or not to log2 transform scale.
#' @param sl The value to scale by (default=1000).
#' @return Normalized or raw counts
#' @author Joseph N. Paulson, jpaulson@@umiacs.umd.edu
#' @examples
#'
#' data(lungData)
#' head(MRcounts(lungData))
#'
MRcounts <- function(obj,norm=FALSE,log=FALSE,sl=1000) {
stopifnot( is( obj, "MRexperiment" ) )
if(!norm){
x=assayData(obj)[["counts"]]
}
else{
if(any(is.na(normFactors(obj)))){
x=cumNormMat(obj,sl=sl)
} else{
x=sweep(assayData(obj)[["counts"]],2,as.vector(unlist(normFactors(obj)))/sl,"/")
}
}
if(!log){
return(x)
} else{
return(log2(x+1))
}
}
#' Access the posterior probabilities that results from analysis
#'
#' Accessing the posterior probabilities following a run through
#' \code{\link{fitZig}}
#'
#'
#' @name posteriorProbs
#' @aliases posteriorProbs,MRexperiment-method posteriorProbs
#' @docType methods
#' @param obj a \code{MRexperiment} object.
#' @return Matrix of posterior probabilities
#' @author Joseph N. Paulson, jpaulson@@umiacs.umd.edu
#' @examples
#'
#' # see vignette
#'
posteriorProbs <- function( obj ) {
stopifnot( is( obj, "MRexperiment" ) )
assayData(obj)[["z"]]
}
#' Access the normalization factors in a MRexperiment object
#'
#' Function to access the scaling factors, aka the normalization factors, of
#' samples in a MRexperiment object.
#'
#'
#' @name normFactors
#' @aliases normFactors,MRexperiment-method normFactors
#' @docType methods
#' @param obj a \code{MRexperiment} object.
#' @return Normalization scaling factors
#' @author Joseph N. Paulson, jpaulson@@umiacs.umd.edu
#' @examples
#'
#' data(lungData)
#' head(normFactors(lungData))
#'
normFactors <- function( obj ) {
stopifnot( is( obj, "MRexperiment" ) )
nf <- pData(obj@expSummary$expSummary)[["normFactors"]]
nf <- unlist(nf)
names(nf) <- sampleNames(obj)
nf
}
#' Access sample depth of coverage from MRexperiment object
#'
#' The libSize vector represents the column (sample specific) sums of features,
#' i.e. the total number of reads for a sample or depth of coverage. It is used by
#' \code{\link{fitZig}}.
#'
#'
#' @name libSize
#' @aliases libSize,MRexperiment-method libSize
#' @docType methods
#' @param obj a \code{MRexperiment} object.
#' @return Library sizes
#' @author Joseph N. Paulson, jpaulson@@umiacs.umd.edu
#' @examples
#'
#' data(lungData)
#' head(libSize(lungData))
#'
libSize<-function(obj){
stopifnot( is( obj, "MRexperiment" ) )
ls <- pData(obj@expSummary$expSummary)[["libSize"]]
ls <- unlist(ls)
names(ls) <- sampleNames(obj)
ls
}
#' Access MRexperiment object experiment data
#'
#' The expSummary vectors represent the column (sample specific) sums of
#' features, i.e. the total number of reads for a sample, libSize and also the
#' normalization factors, normFactor.
#'
#'
#' @name expSummary
#' @aliases expSummary,MRexperiment-method expSummary
#' @docType methods
#' @param obj a \code{MRexperiment} object.
#' @return Experiment summary table
#' @author Joseph N. Paulson, jpaulson@@umiacs.umd.edu
#' @examples
#'
#' data(mouseData)
#' expSummary(mouseData)
#'
expSummary<-function(obj){
stopifnot( is( obj, "MRexperiment" ) )
pData(obj@expSummary$expSummary)
}
#' Check if MRexperiment or matrix and return matrix
#'
#' Function to check if object is a MRexperiment
#' class or matrix
#'
#' @name returnAppropriateObj
#' @param obj a \code{MRexperiment} or \code{matrix} object
#' @param norm return a normalized \code{MRexperiment} matrix
#' @param log return a log transformed \code{MRexperiment} matrix
#' @param sl scaling value
#' @return Matrix
#' @examples
#'
#' data(lungData)
#' head(returnAppropriateObj(lungData,norm=FALSE,log=FALSE))
#'
returnAppropriateObj <- function(obj,norm,log,sl=1000) {
if(class(obj)=="MRexperiment"){
mat = MRcounts(obj,norm=norm,log=log,sl=sl)
} else if(class(obj) == "matrix") {
mat = obj
} else {
stop("Object needs to be either a MRexperiment object or matrix")
}
mat
}
