ComBat: Adjust for batch effects using an empirical Bayesian or...

In xinchoubiology/Rcppsva: Rcpp Integration Surrogate Variable Analysis

Description

ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodology described in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for batch effects. Users are returned an expression matrix that has been corrected for batch effects. The input data are assumed to be cleaned and normalized before batch effect removal.[[Cpp Rewrite and we finally get 100x speedup]] (For 485512 x 6 expression array matrix = 9622.983 secs = 2 hrs 40 mins) and origin source code of pure R version is from minif package

Usage

ComBat(dat, batch, mod = NULL, par.prior = TRUE, prior.plots = FALSE)

Arguments

dat	Genomic measure matrix (dimensions probe x sample) - for example, expression matrix
batch	Batch covariate (only one batch allowed)
mod	Model matrix for outcome of interest and other covariates besides batch
par.prior	(Optional) TRUE indicates parametric adjustments will be used, FALSE indicates non-parametric adjustments will be used
prior.plots	(Optional)TRUE give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric

Value

data : A probe x sample genomic measure matrix, adjusted for batch effects.

Author(s)

Xin Zhou [e-mail]

xinchoubiology/Rcppsva documentation built on May 4, 2019, 1:06 p.m.