ber_bg: Batch Effects Removal using Bagging
In ber: Batch Effects Removal

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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,…,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.

In this implementation NA values are not allowed.

A matrix of adjusted data with n rows and g columns.

Marco Giordan

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

ber, combat_np, combat_p, mean_centering, standardization

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)