ber (version 4.0)

ber_bg: Batch Effects Removal using Bagging

Description

Batch effects are removed using a two-stage regression approach and bagging.

Usage

ber_bg(Y, b, covariates = NULL,partial=TRUE,nSim=150)

Arguments

Y

A matrix with $n$ rows and $g$ columns, where $n$ is the number of objects and $g$ is the number of variables. In the case of gene expression data, columns correspond to genes (probe sets) and rows to samples.

b

A vector of class factor with the element in position $i$ ($i=1,\ldots,n$) representing the batch from which observation $i$ belongs to.

covariates

An object of class data.frame where each column corresponds to a quantitative variable (of class numeric) or a qualitative variable (of class factor).

partial

A logical value indicating if partial bagging or full bugging have to be performed. See reference below.

nSim

Number of bootstrap samples.

Value

$n$ rows and $g$ columns.

Details

In this implementation NA values are not allowed.

References

M. Giordan. February 2013. A Two-Stage Procedure for the Removal of Batch Effects in Microarray Studies. Statistics in Biosciences.

See Also

ber, combat_np, combat_p, mean_centering, standardization

Examples

Y<-matrix(rnorm(6000),nrow=12)
class<-gl(2,6,labels=c("Control","Treat"))
class<-data.frame(class)
batch<-rep(gl(2,3,labels=c("Batch1","Batch2")),2)
YEadj<-ber_bg(Y,batch,class)