R/empNegativeControls.R

Defines functions empNegativeControls.simulateGEdata empNegativeControls.default empNegativeControls

Documented in empNegativeControls empNegativeControls.default empNegativeControls.simulateGEdata

#' Empirically choose negative control genes. 
#' 
#' \code{empNegativeControls} finds suitable negative controls in real or simulated data.
#'
#' @param Y A matrix of gene expression values or an object of the class \code{simulateGEdata}.
#' @param exclude A vector of indices to be excluded from being chosen as negative controls.
#' @param smoothing A numerical scalar determining the amount of smoothing to be applied.
#' @param nc An integer setting the number of negative controls.
#' @return \code{empNegativeControls} returns a vector of indicies of empirically chosen negative controls.
#'
#' @details
#' First the mean of all genes (except the excluded genes) is calculated and genes are accordingly assigned to bins. The bins have the size
#' of the smoothing parameter. In each bin the function picks a number of negative control genes proportional to the total number of genes in the bin. 
#' The picked genes in each bin have the lowest inter-quantile ranges of all genes in the respective bin.
#' @examples
#' Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 250, 100, check.input=TRUE)
#' empNegativeControls(Y, exclude=1:100, nc=100)
#' @section Warning:
#' For simulated data it is advisable to use the known negative controls or restrict the empirical
#' choice to the known negative controls by excluding all other genes.
#' @author Saskia Freytag
#' @export
empNegativeControls<-function(Y, #matrix of expression data
								exclude, #index of genes to be excluded from negative controls
								smoothing=0.1, # smoothing factor
								nc #number of negative controls
								) UseMethod("empNegativeControls")
								
#' \code{empNegativeControls.default} empirically chooses negative control genes for matrix input.
#'
#' @rdname empNegativeControls
#' @export 
empNegativeControls.default<-function(
										Y, #matrix of expression data
										exclude, #index of genes to be excluded from negative controls
										smoothing=0.1, # smoothing factor
										nc #approx number of negative controls
										){
	if(is.null(colnames(Y))) colnames(Y)<-1:dim(Y)[2]
	tmpY<-Y[,-exclude]									
	prop<-nc/dim(tmpY)[2]									
	# find the proportion of negative controls to all genes
	
	tmpMeans<-colMeans(tmpY)
	# find means for every gene

	tmpIQR<-apply(tmpY, 2, function(x) findIQR(x))
	# find IQR for every gene

	empNC<-vector(length=0)
	# initialise vector of empirical negative controls
	
	partition<-seq(floor(min(tmpMeans)), ceiling(max(tmpMeans)), smoothing)
	# set partition of data
	
	for(i in 1:(length(partition)-1)){
		start<-partition[i]
		end<-partition[i+1]
		index<-which(tmpMeans<end & tmpMeans>start)
		# find genes in partition
		if(length(index)==0) next
		# if no genes are in the partition go to next
		size<-ceiling(length(index)*prop)
		# find how many genes to pick
		tmpIQRsorted<-sort(tmpIQR[index], decreasing=FALSE)
		empNC<-c(empNC, names(tmpIQRsorted)[1:size])
	}

	empNC<-empNC[-sample(1:length(empNC),(length(empNC)-nc))]
	# randomly remove too many controls
	
	return(which(is.element(colnames(Y), empNC)))
}

#' \code{empNegativeControls.simulateGEdata} empircially chooses negative control genes for \code{simulateGEdata} object. 
#'
#' @rdname empNegativeControls
#' @export
empNegativeControls.simulateGEdata<-function(
												Y, #object of class simualteGEdata
												exclude, #index of genes to be excluded from negative controls
												smoothing=0.1, # smoothing factor
												nc #approx number of negative controls
												){
	
	if(is.simulateGEdata(Y)==FALSE) stop("Y needs to be of class simulateGEdata.")	
	Y<-Y$Y
	colnames(Y)<-1:dim(Y)[2]
	
	empNegativeControls.default(Y, exclude, smoothing, nc)
											
}	
PeteHaitch/RUVcorr documentation built on May 9, 2017, 5:43 p.m.