R/RUVNaiveRidge.R

Defines functions RUVNaiveRidge.simulateGEdata RUVNaiveRidge.default RUVNaiveRidge

Documented in RUVNaiveRidge RUVNaiveRidge.default RUVNaiveRidge.simulateGEdata

#' Removal of unwanted variation for gene correlations.
#' 
#' \code{RUVNaiveRidge} applies the ridged version of global removal of unwanted variation 
#' 	to simulated or real gene expression data. 
#'
#' @param Y A matrix of gene expression values or an object of class \code{simulateGEdata}. 
#' @param center A logical scalar; if \code{TRUE} the data is centered, 
#'	if \code{FALSE} data is assumed to be already centered.
#' @param nc_index A vector of indices of negative controls.
#' @param nu A numeric scalar value of \code{nu} \eqn{\geq 0}.
#' @param kW An integer setting the number of dimensions for the estimated noise.
#' @param check.input A logical scalar; if \code{TRUE} all input is checked (not advisable for large simulations).
#' @return \code{RUVNaiveRidge} returns a matrix of the cleaned (RUV-treated) centered gene expression values.
#' @details 
#' The parameter \code{kW} controls how much noise is cleaned, whereas the parameter \code{nu}
#' 	controls the amount of ridging to deal with possible dependence of the noise and the factor of 
#'	interest.
#' @examples 
#' Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 250, 100, check.input=FALSE)
#' Y
#' Y.hat<-RUVNaiveRidge(Y, center=TRUE, nc_index=251:500, 0, 10, check.input=TRUE)
#' cor(Y.hat[,1:5])
#' Y$Sigma[1:5,1:5]
#' Y.hat<-RUVNaiveRidge(Y, center=TRUE, nc_index=251:500, 1000, 10, check.input=TRUE)
#' cor(Y.hat[,1:5])
#' Y$Sigma[1:5,1:5]
#' @author Saskia Freytag
#' @exportMethod RUVNaiveRidge
#' @export
RUVNaiveRidge<-function(Y, 
						center=TRUE, #set equal to FALSE in case of centered data
						nc_index, #column index for negative controls 
						nu, # Ridge factor
						kW, # number of noise dimensions
						check.input=FALSE) UseMethod("RUVNaiveRidge")

#' \code{RUVNaiveRidge.default} applies the ridged version of global removal of unwanted variation to matrices.
#'
#' @rdname RUVNaiveRidge
#' @export
RUVNaiveRidge.default<-function(
								Y, #matrix of gene expression data 
								center=TRUE, #set equal to FALSE in case of centered data
								nc_index, #column index for negative controls 
								nu, # Ridge factor
								kW, # number of noise dimensions
								check.input=FALSE){
								
	if(check.input){
		if(is.matrix(Y)==FALSE){stop("Y needs to be a matrix.")}
		if(nu<0){stop("nu has to be positive or 0.")}
		if(kW>dim(Y)[1]){stop("kW is too big.")}
	}							
	
	if(center){
			Y<-scale(Y,center=T,scale=F)
			# center data
	}
	
	Yc<-Y[,nc_index]
	# subset negative controls
	
	tmp<-svd(Yc, nu=kW, nv=kW)
	S.d<-diag(tmp$d[1:kW],nrow=kW,ncol=kW)
	# SVD of negative controls
	
	W.hat<-tmp$u%*%S.d
	# estimate W.hat
	
	alpha.hat<-solve(t(W.hat)%*%W.hat+nu*diag(dim(W.hat)[2]))%*%t(W.hat)%*%Y
	# estimate alpha.hat
	
	return(Y-W.hat%*%alpha.hat)
	# calculate Y.hat (note that this is the mean centered Y)
}

#' \code{RUVNaiveRidge.simulateGEdata} applies the ridged version of removal of unwanted variation to objects of class \code{simulateGEdata}.
#'
#' @rdname RUVNaiveRidge 
#' @export
RUVNaiveRidge.simulateGEdata<-function(
								Y, #object of the class simulateGEdata
								center=TRUE, #set equal to FALSE in case of centered data
								nc_index, #column index for negative controls 
								nu, # Ridge factor
								kW, # number of noise dimensions
								check.input=FALSE){
								
	Y<-Y$Y 
	# extract right expression values
	
	RUVNaiveRidge.default(Y,center,nc_index, nu, kW, check.input)
}
PeteHaitch/RUVcorr documentation built on May 9, 2017, 5:43 p.m.