Nothing
R/methodsAccess.R
In dexus: DEXUS - Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions or without Replicates
# Copyright (C) 2013 Guenter Klambauer and Thomas Unterthiner
# <klambauer@bioinf.jku.at>
#' @title Accessors for a "DEXUSResult".
#' @name accessors
#'
#' @aliases transcriptNames sampleNames inputData normalizedData
#' sizeFactors INIValues INIThreshold INICalls pvals responsibilities
#' posteriorProbs logFC conditionSizes sizeParameters means dispersions
#' params
#'
#' @rdname accessors
#'
#' @description These generic functions return the slots
#' of an RNA-Seq analysis performed by DEXUS.
#' The results of DEXUS are stored as an instance of
#' \code{\link{DEXUSResult-class}}.
#'
#' @param object An instance of "DEXUSResult".
#' @examples
#' data(dexus)
#' result <- dexus(countsBottomly[1:20,1:10])
#' transcriptNames(result)
#' sampleNames(result)
#' inputData(result)
#' normalizedData(result)
#' sizeFactors(result)
#' INIValues(result)
#' INIThreshold(result)
#' INICalls(result)
#' pvals(result)
#' responsibilities(result)
#' posteriorProbs(result)
#' logFC(result)
#' conditionSizes(result)
#' sizeParameters(result)
#' means(result)
#' dispersions(result)
#' params(result)
#'
#' @export transcriptNames
#' @export sampleNames
#' @export inputData
#' @export normalizedData
#' @export sizeFactors
#' @export INIValues
#' @export INIThreshold
#' @export INICalls
#' @export pvals
#' @export responsibilities
#' @export posteriorProbs
#' @export logFC
#' @export conditionSizes
#' @export sizeParameters
#' @export means
#' @export dispersions
#' @export params
#' @return The accessor functions return a the matrices or vectors contained
#' in the corresponding slot of the "DEXUSResult".
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
setMethod("transcriptNames", signature = "DEXUSResult",
definition = function(object) object@transcriptNames)
setMethod("sampleNames", signature = "DEXUSResult",
definition = function(object) object@sampleNames)
setMethod("inputData", signature = "DEXUSResult",
definition = function(object) object@inputData)
setMethod("normalizedData", signature = "DEXUSResult",
definition = function(object) object@normalizedData)
setMethod("sizeFactors", signature = "DEXUSResult",
definition = function(object) object@sizeFactors)
setMethod("INIValues", signature = "DEXUSResult",
definition = function(object) object@INIValues)
setMethod("INIThreshold", signature = "DEXUSResult",
definition = function(object) object@INIThreshold)
setMethod("INICalls", signature = "DEXUSResult",
definition = function(object) object@INICalls)
setMethod("pvals", signature = "DEXUSResult",
definition = function(object) object@pvals)
setMethod("responsibilities", signature = "DEXUSResult",
definition = function(object) object@responsibilities)
setMethod("posteriorProbs", signature = "DEXUSResult",
definition = function(object) object@posteriorProbs)
setMethod("logFC", signature = "DEXUSResult",
definition = function(object) object@logFC)
setMethod("conditionSizes", signature = "DEXUSResult",
definition = function(object) object@conditionSizes)
setMethod("sizeParameters", signature = "DEXUSResult",
definition = function(object) object@sizeParameters)
setMethod("means", signature = "DEXUSResult",
definition = function(object) object@means)
setMethod("dispersions", signature = "DEXUSResult",
definition = function(object) object@dispersions)
setMethod("params", signature = "DEXUSResult",
definition = function(object) object@params)
#' @title Set the I/NI threshold.
#' @aliases INIThreshold<-
#' @aliases INIThreshold-set
#'
#' @description This generic function sets the
#' threshold of the I/NI value. Transcripts with I/NI values above the
#' I/NI threshold are considered as differentially expressed.
#' The results of DEXUS are stored as an instance of
#' \code{\link{DEXUSResult-class}}.
#'
#' @param object An instance of "DEXUSResult".
#' @param value A numeric to be used for thresholding the I/NI values.
#' @examples
#' data(dexus)
#' result <- dexus(countsBottomly[1:20,1:10])
#' INIThreshold(result) <- 0.1
#' @return \code{INIThreshold<-} returns an instance of "DEXUSResult".
#' @name INIThreshold<-
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @export
setReplaceMethod("INIThreshold", signature="DEXUSResult",
definition=function(object,value)
{
slot(object,"INIThreshold") <- value
slot(object,"INICalls") <- object@INIValues > value
y <- length(object@INICalls)
x <- length(which(object@INICalls))
cat("Total number of transcripts: ",y,"\n")
cat("Number of differentially expressed transcripts: ",x,"\n")
cat("Percentage of differentially expressed transcripts: ",
round(x/y,3)*100,"%","\n")
object
}
)
#' @title Obtaining the result data frame.
#'
#' @description This generic function extracts the result of the analysis
#' from the "DEXUSResult" class as a data frame.
#'
#' The results of DEXUS are stored as an instance of
#' \code{\link{DEXUSResult-class}}.
#'
#' @param object An instance of "DEXUSResult".
#' @examples
#' data(dexus)
#' result <- dexus(countsBottomly[1:20,1:10])
#' as.data.frame(result)
#' @return \code{as.data.frame} returns a data frame.
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @noRd
#' @exportMethod as.data.frame
setMethod("as.data.frame", signature = c(x="DEXUSResult"),
definition = function(x){
n <- nrow(x@normalizedData)
idx <- 1:n
df <- data.frame(
"Transcript"=x@transcriptNames[idx],
"INIcall"=x@INICalls[idx],
"INI"=x@INIValues[idx],
"pval"=x@pvals[idx],
"Mean"=x@means[idx, ,drop=FALSE],
"logFC"=x@logFC[idx, ,drop=FALSE],
"conditionSize"=x@conditionSizes[idx, ,drop=FALSE],
"dispersion"=x@dispersions[idx, ,drop=FALSE],
"responsibilities"=x@responsibilities[idx, ,drop=FALSE]
)
rownames(df) <- NULL
return(df)
})
#' @title Subsetting a "DEXUSResult".
#'
#' @description Information about specific transcripts can be accessed in
#' the "DEXUSResult" object by using the standard brackets "[idx]" for
#' subsetting. Either transcript names or transcript indices can be used.
#'
#' @name `[`
#' @aliases `[`,DEXUSResult,numeric-method
#' @aliases `[`,DEXUSResult,character-method
#' @aliases `[`,DEXUSResult,logical-method
#' @param x "DEXUSResult"
#' @param i Either a numeric vector of indices or a character vector containing
#' the transcript names.
#' @return An instance of "DEXUSResult".
#'
#' @examples
#' data(dexus)
#' res <- dexus(countsBottomly[1:100, ])
#' res["ENSMUSG00000000486"]
#' res[50:55]
#'
#' @rdname DEXUSResult-subset
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @export
setMethod("[", signature = c(x="DEXUSResult",i="numeric"),
definition = function(x,i){
n <- length(x@transcriptNames)
y <- new("DEXUSResult")
if (!all(i %in% 1:n)){
stop(paste("Given Indices outside the range of the",
"transcripts' indices."))
} else {
y@transcriptNames = x@transcriptNames[i]
y@sampleNames = x@sampleNames
y@inputData = x@inputData[i, ,drop=FALSE]
y@normalizedData = x@normalizedData[i, ,drop=FALSE]
y@sizeFactors = x@sizeFactors
y@INIValues = x@INIValues[i]
y@INIThreshold = x@INIThreshold
y@INICalls = x@INICalls[i]
y@pvals = x@pvals[i]
y@responsibilities = x@responsibilities[i, ,drop=FALSE]
y@posteriorProbs = x@posteriorProbs[, ,i,drop=FALSE]
y@logFC = x@logFC[i, ,drop=FALSE]
y@conditionSizes = x@conditionSizes[i, ,drop=FALSE]
y@sizeParameters = x@sizeParameters[i, ,drop=FALSE]
y@means = x@means[i, ,drop=FALSE]
y@dispersions = x@dispersions[i, ,drop=FALSE]
y@params = x@params
}
return(y)
})
setMethod("[", signature = c(x="DEXUSResult",i="logical"),
definition = function(x,i){
n <- length(x@transcriptNames)
y <- new("DEXUSResult")
if (length(i)!=n){
stop(paste("Given logical vector must have as many elements",
"as transcripts in the result object."))
}
i <- which(i)
y@transcriptNames = x@transcriptNames[i]
y@sampleNames = x@sampleNames
y@inputData = x@inputData[i, ,drop=FALSE]
y@normalizedData = x@normalizedData[i, ,drop=FALSE]
y@sizeFactors = x@sizeFactors
y@INIValues = x@INIValues[i]
y@INIThreshold = x@INIThreshold
y@INICalls = x@INICalls[i]
y@pvals = x@pvals[i]
y@responsibilities = x@responsibilities[i, ,drop=FALSE]
y@posteriorProbs = x@posteriorProbs[, ,i,drop=FALSE]
y@logFC = x@logFC[i, ,drop=FALSE]
y@conditionSizes = x@conditionSizes[i, ,drop=FALSE]
y@sizeParameters = x@sizeParameters[i, ,drop=FALSE]
y@means = x@means[i, ,drop=FALSE]
y@dispersions = x@dispersions[i, ,drop=FALSE]
y@params = x@params
return(y)
})
setMethod("[", signature = c(x="DEXUSResult",i="character"),
definition = function(x,i){
y <- new("DEXUSResult")
if (!all(i %in% x@transcriptNames)){
stop("Given transcript name not in result object.")
} else {
ii <- match(i,x@transcriptNames)
y@transcriptNames = x@transcriptNames[ii]
y@sampleNames = x@sampleNames
y@inputData = x@inputData[i, ,drop=FALSE]
y@normalizedData = x@normalizedData[i, ,drop=FALSE]
y@sizeFactors = x@sizeFactors
y@INIValues = x@INIValues[i]
y@INIThreshold = x@INIThreshold
y@INICalls = x@INICalls[i]
y@pvals = x@pvals[i]
y@responsibilities = x@responsibilities[i, ,drop=FALSE]
y@posteriorProbs = x@posteriorProbs[, ,ii,drop=FALSE]
y@logFC = x@logFC[i, ,drop=FALSE]
y@conditionSizes = x@conditionSizes[i, ,drop=FALSE]
y@sizeParameters = x@sizeParameters[i, ,drop=FALSE]
y@means = x@means[i, ,drop=FALSE]
y@dispersions = x@dispersions[i, ,drop=FALSE]
y@params = x@params
}
return(y)
})
#' @title Sorting a DEXUS result.
#' @description This function sorts the result object by I/NI values or
#' p-values such that the transcripts with the highest I/NI value or the lowest
#' p-value are ranked first.
#'
#' @name sort
#'
#' @param object An instance of "DEXUSResult".
#' @return An instance of "DEXUSResult".
#'
#' @examples
#' data(dexus)
#' res <- dexus(countsBottomly[1:100, ])
#' sort(res)
#'
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @exportMethod sort
setMethod("sort",
signature= c(x="DEXUSResult"),
definition=function(x){
n <- nrow(x@normalizedData)
if (!is.null(x@params$mode)){
if (x@params$mode=="unsupervised" | x@params$mode=="semi-supervised"){
idx <- order(x@INIValues,decreasing=TRUE)[1:n]
} else if (x@params$mode %in% c("two-class","multi-class")){
idx <- order(x@pvals)[1:n]
} else {
if (!all(is.na(x@pvals))){
idx <- order(x@pvals)[1:n]
} else {
if (!all(is.na(x@pvals))){
idx <- order(x@INIValues,decreasing=TRUE)[1:n]
} else {
stop(paste("\"DEXUSResult\" contains neither",
"p-values nor I/NI values."))
}
}
}
} else {
if (!all(is.na(x@pvals))){
idx <- order(x@pvals)[1:n]
} else {
if (!all(is.na(x@INIValues))){
idx <- order(x@INIValues,decreasing=TRUE)[1:n]
} else {
stop(paste("\"DEXUSResult\" contains neither",
"p-values nor I/NI values."))
}
}
}
return(x[idx])
})
#' @title I/NI filtering of a DEXUS result.
#' @description This function filters the result object for informative
#' transcripts. Transcripts with an I/NI value below the given threshold
#' are filtered out.
#'
#' @name INI
#'
#' @param object An instance of "DEXUSResult".
#' @param threshold A numeric determining the threshold for the I/NI values.
#' @return An instance of "DEXUSResult".
#'
#' @examples
#' data(dexus)
#' res <- dexus(countsBottomly[1:100, ])
#' INI(res)
#'
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @export
setMethod("INI", signature="DEXUSResult",
definition=function(object,threshold=0.1)
{
if (all(is.na(object@INIValues)))
stop(paste("No I/NI values found in the result object.",
"INI filter can only be applied if DEXUS",
"was run in unsupervised mode."))
idx <- object@INIValues > threshold
y <- length(idx)
x <- length(which(idx))
cat("Total number of transcripts: ",y,"\n")
cat("Number of differentially expressed transcripts: ",x,"\n")
cat("Percentage of differentially expressed transcripts: ",
round(x/y,3)*100,"%","\n")
cat("\n")
return(sort(object[which(idx)]))
}
)
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# Copyright (C) 2013 Guenter Klambauer and Thomas Unterthiner
# <klambauer@bioinf.jku.at>
#' @title Accessors for a "DEXUSResult".
#' @name accessors
#'
#' @aliases transcriptNames sampleNames inputData normalizedData
#' sizeFactors INIValues INIThreshold INICalls pvals responsibilities
#' posteriorProbs logFC conditionSizes sizeParameters means dispersions
#' params
#'
#' @rdname accessors
#'
#' @description These generic functions return the slots
#' of an RNA-Seq analysis performed by DEXUS.
#' The results of DEXUS are stored as an instance of
#' \code{\link{DEXUSResult-class}}.
#'
#' @param object An instance of "DEXUSResult".
#' @examples
#' data(dexus)
#' result <- dexus(countsBottomly[1:20,1:10])
#' transcriptNames(result)
#' sampleNames(result)
#' inputData(result)
#' normalizedData(result)
#' sizeFactors(result)
#' INIValues(result)
#' INIThreshold(result)
#' INICalls(result)
#' pvals(result)
#' responsibilities(result)
#' posteriorProbs(result)
#' logFC(result)
#' conditionSizes(result)
#' sizeParameters(result)
#' means(result)
#' dispersions(result)
#' params(result)
#'
#' @export transcriptNames
#' @export sampleNames
#' @export inputData
#' @export normalizedData
#' @export sizeFactors
#' @export INIValues
#' @export INIThreshold
#' @export INICalls
#' @export pvals
#' @export responsibilities
#' @export posteriorProbs
#' @export logFC
#' @export conditionSizes
#' @export sizeParameters
#' @export means
#' @export dispersions
#' @export params
#' @return The accessor functions return a the matrices or vectors contained
#' in the corresponding slot of the "DEXUSResult".
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
setMethod("transcriptNames", signature = "DEXUSResult",
definition = function(object) object@transcriptNames)
setMethod("sampleNames", signature = "DEXUSResult",
definition = function(object) object@sampleNames)
setMethod("inputData", signature = "DEXUSResult",
definition = function(object) object@inputData)
setMethod("normalizedData", signature = "DEXUSResult",
definition = function(object) object@normalizedData)
setMethod("sizeFactors", signature = "DEXUSResult",
definition = function(object) object@sizeFactors)
setMethod("INIValues", signature = "DEXUSResult",
definition = function(object) object@INIValues)
setMethod("INIThreshold", signature = "DEXUSResult",
definition = function(object) object@INIThreshold)
setMethod("INICalls", signature = "DEXUSResult",
definition = function(object) object@INICalls)
setMethod("pvals", signature = "DEXUSResult",
definition = function(object) object@pvals)
setMethod("responsibilities", signature = "DEXUSResult",
definition = function(object) object@responsibilities)
setMethod("posteriorProbs", signature = "DEXUSResult",
definition = function(object) object@posteriorProbs)
setMethod("logFC", signature = "DEXUSResult",
definition = function(object) object@logFC)
setMethod("conditionSizes", signature = "DEXUSResult",
definition = function(object) object@conditionSizes)
setMethod("sizeParameters", signature = "DEXUSResult",
definition = function(object) object@sizeParameters)
setMethod("means", signature = "DEXUSResult",
definition = function(object) object@means)
setMethod("dispersions", signature = "DEXUSResult",
definition = function(object) object@dispersions)
setMethod("params", signature = "DEXUSResult",
definition = function(object) object@params)
#' @title Set the I/NI threshold.
#' @aliases INIThreshold<-
#' @aliases INIThreshold-set
#'
#' @description This generic function sets the
#' threshold of the I/NI value. Transcripts with I/NI values above the
#' I/NI threshold are considered as differentially expressed.
#' The results of DEXUS are stored as an instance of
#' \code{\link{DEXUSResult-class}}.
#'
#' @param object An instance of "DEXUSResult".
#' @param value A numeric to be used for thresholding the I/NI values.
#' @examples
#' data(dexus)
#' result <- dexus(countsBottomly[1:20,1:10])
#' INIThreshold(result) <- 0.1
#' @return \code{INIThreshold<-} returns an instance of "DEXUSResult".
#' @name INIThreshold<-
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @export
setReplaceMethod("INIThreshold", signature="DEXUSResult",
definition=function(object,value)
{
slot(object,"INIThreshold") <- value
slot(object,"INICalls") <- object@INIValues > value
y <- length(object@INICalls)
x <- length(which(object@INICalls))
cat("Total number of transcripts: ",y,"\n")
cat("Number of differentially expressed transcripts: ",x,"\n")
cat("Percentage of differentially expressed transcripts: ",
round(x/y,3)*100,"%","\n")
object
}
)
#' @title Obtaining the result data frame.
#'
#' @description This generic function extracts the result of the analysis
#' from the "DEXUSResult" class as a data frame.
#'
#' The results of DEXUS are stored as an instance of
#' \code{\link{DEXUSResult-class}}.
#'
#' @param object An instance of "DEXUSResult".
#' @examples
#' data(dexus)
#' result <- dexus(countsBottomly[1:20,1:10])
#' as.data.frame(result)
#' @return \code{as.data.frame} returns a data frame.
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @noRd
#' @exportMethod as.data.frame
setMethod("as.data.frame", signature = c(x="DEXUSResult"),
definition = function(x){
n <- nrow(x@normalizedData)
idx <- 1:n
df <- data.frame(
"Transcript"=x@transcriptNames[idx],
"INIcall"=x@INICalls[idx],
"INI"=x@INIValues[idx],
"pval"=x@pvals[idx],
"Mean"=x@means[idx, ,drop=FALSE],
"logFC"=x@logFC[idx, ,drop=FALSE],
"conditionSize"=x@conditionSizes[idx, ,drop=FALSE],
"dispersion"=x@dispersions[idx, ,drop=FALSE],
"responsibilities"=x@responsibilities[idx, ,drop=FALSE]
)
rownames(df) <- NULL
return(df)
})
#' @title Subsetting a "DEXUSResult".
#'
#' @description Information about specific transcripts can be accessed in
#' the "DEXUSResult" object by using the standard brackets "[idx]" for
#' subsetting. Either transcript names or transcript indices can be used.
#'
#' @name `[`
#' @aliases `[`,DEXUSResult,numeric-method
#' @aliases `[`,DEXUSResult,character-method
#' @aliases `[`,DEXUSResult,logical-method
#' @param x "DEXUSResult"
#' @param i Either a numeric vector of indices or a character vector containing
#' the transcript names.
#' @return An instance of "DEXUSResult".
#'
#' @examples
#' data(dexus)
#' res <- dexus(countsBottomly[1:100, ])
#' res["ENSMUSG00000000486"]
#' res[50:55]
#'
#' @rdname DEXUSResult-subset
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @export
setMethod("[", signature = c(x="DEXUSResult",i="numeric"),
definition = function(x,i){
n <- length(x@transcriptNames)
y <- new("DEXUSResult")
if (!all(i %in% 1:n)){
stop(paste("Given Indices outside the range of the",
"transcripts' indices."))
} else {
y@transcriptNames = x@transcriptNames[i]
y@sampleNames = x@sampleNames
y@inputData = x@inputData[i, ,drop=FALSE]
y@normalizedData = x@normalizedData[i, ,drop=FALSE]
y@sizeFactors = x@sizeFactors
y@INIValues = x@INIValues[i]
y@INIThreshold = x@INIThreshold
y@INICalls = x@INICalls[i]
y@pvals = x@pvals[i]
y@responsibilities = x@responsibilities[i, ,drop=FALSE]
y@posteriorProbs = x@posteriorProbs[, ,i,drop=FALSE]
y@logFC = x@logFC[i, ,drop=FALSE]
y@conditionSizes = x@conditionSizes[i, ,drop=FALSE]
y@sizeParameters = x@sizeParameters[i, ,drop=FALSE]
y@means = x@means[i, ,drop=FALSE]
y@dispersions = x@dispersions[i, ,drop=FALSE]
y@params = x@params
}
return(y)
})
setMethod("[", signature = c(x="DEXUSResult",i="logical"),
definition = function(x,i){
n <- length(x@transcriptNames)
y <- new("DEXUSResult")
if (length(i)!=n){
stop(paste("Given logical vector must have as many elements",
"as transcripts in the result object."))
}
i <- which(i)
y@transcriptNames = x@transcriptNames[i]
y@sampleNames = x@sampleNames
y@inputData = x@inputData[i, ,drop=FALSE]
y@normalizedData = x@normalizedData[i, ,drop=FALSE]
y@sizeFactors = x@sizeFactors
y@INIValues = x@INIValues[i]
y@INIThreshold = x@INIThreshold
y@INICalls = x@INICalls[i]
y@pvals = x@pvals[i]
y@responsibilities = x@responsibilities[i, ,drop=FALSE]
y@posteriorProbs = x@posteriorProbs[, ,i,drop=FALSE]
y@logFC = x@logFC[i, ,drop=FALSE]
y@conditionSizes = x@conditionSizes[i, ,drop=FALSE]
y@sizeParameters = x@sizeParameters[i, ,drop=FALSE]
y@means = x@means[i, ,drop=FALSE]
y@dispersions = x@dispersions[i, ,drop=FALSE]
y@params = x@params
return(y)
})
setMethod("[", signature = c(x="DEXUSResult",i="character"),
definition = function(x,i){
y <- new("DEXUSResult")
if (!all(i %in% x@transcriptNames)){
stop("Given transcript name not in result object.")
} else {
ii <- match(i,x@transcriptNames)
y@transcriptNames = x@transcriptNames[ii]
y@sampleNames = x@sampleNames
y@inputData = x@inputData[i, ,drop=FALSE]
y@normalizedData = x@normalizedData[i, ,drop=FALSE]
y@sizeFactors = x@sizeFactors
y@INIValues = x@INIValues[i]
y@INIThreshold = x@INIThreshold
y@INICalls = x@INICalls[i]
y@pvals = x@pvals[i]
y@responsibilities = x@responsibilities[i, ,drop=FALSE]
y@posteriorProbs = x@posteriorProbs[, ,ii,drop=FALSE]
y@logFC = x@logFC[i, ,drop=FALSE]
y@conditionSizes = x@conditionSizes[i, ,drop=FALSE]
y@sizeParameters = x@sizeParameters[i, ,drop=FALSE]
y@means = x@means[i, ,drop=FALSE]
y@dispersions = x@dispersions[i, ,drop=FALSE]
y@params = x@params
}
return(y)
})
#' @title Sorting a DEXUS result.
#' @description This function sorts the result object by I/NI values or
#' p-values such that the transcripts with the highest I/NI value or the lowest
#' p-value are ranked first.
#'
#' @name sort
#'
#' @param object An instance of "DEXUSResult".
#' @return An instance of "DEXUSResult".
#'
#' @examples
#' data(dexus)
#' res <- dexus(countsBottomly[1:100, ])
#' sort(res)
#'
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @exportMethod sort
setMethod("sort",
signature= c(x="DEXUSResult"),
definition=function(x){
n <- nrow(x@normalizedData)
if (!is.null(x@params$mode)){
if (x@params$mode=="unsupervised" | x@params$mode=="semi-supervised"){
idx <- order(x@INIValues,decreasing=TRUE)[1:n]
} else if (x@params$mode %in% c("two-class","multi-class")){
idx <- order(x@pvals)[1:n]
} else {
if (!all(is.na(x@pvals))){
idx <- order(x@pvals)[1:n]
} else {
if (!all(is.na(x@pvals))){
idx <- order(x@INIValues,decreasing=TRUE)[1:n]
} else {
stop(paste("\"DEXUSResult\" contains neither",
"p-values nor I/NI values."))
}
}
}
} else {
if (!all(is.na(x@pvals))){
idx <- order(x@pvals)[1:n]
} else {
if (!all(is.na(x@INIValues))){
idx <- order(x@INIValues,decreasing=TRUE)[1:n]
} else {
stop(paste("\"DEXUSResult\" contains neither",
"p-values nor I/NI values."))
}
}
}
return(x[idx])
})
#' @title I/NI filtering of a DEXUS result.
#' @description This function filters the result object for informative
#' transcripts. Transcripts with an I/NI value below the given threshold
#' are filtered out.
#'
#' @name INI
#'
#' @param object An instance of "DEXUSResult".
#' @param threshold A numeric determining the threshold for the I/NI values.
#' @return An instance of "DEXUSResult".
#'
#' @examples
#' data(dexus)
#' res <- dexus(countsBottomly[1:100, ])
#' INI(res)
#'
#' @author Guenter Klambauer \email{klambauer@@bioinf.jku.at} and
#' Thomas Unterthiner \email{unterthiner@@bioinf.jku.at}
#' @export
setMethod("INI", signature="DEXUSResult",
definition=function(object,threshold=0.1)
{
if (all(is.na(object@INIValues)))
stop(paste("No I/NI values found in the result object.",
"INI filter can only be applied if DEXUS",
"was run in unsupervised mode."))
idx <- object@INIValues > threshold
y <- length(idx)
x <- length(which(idx))
cat("Total number of transcripts: ",y,"\n")
cat("Number of differentially expressed transcripts: ",x,"\n")
cat("Percentage of differentially expressed transcripts: ",
round(x/y,3)*100,"%","\n")
cat("\n")
return(sort(object[which(idx)]))
}
)
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