ComBat

Genomic measure matrix (dimensions probe x sample) - for example, expression matrix

Batch covariate (multiple batches are not allowed)

batch

Model matrix for outcome of interest and other covariates besides batch

(Optional) TRUE indicates parametric adjustments will be used, FALSE indicates non-parametric adjustments will be used

par.prior

(Optional)TRUE give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric

prior.plots

(Optional)FALSE If TRUE ComBat only corrects the mean of the batch effect (no scale adjustment)

mean.only


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.


The sva package contains functions for removing batch
effects and other unwanted variation in high-throughput
experiment. Specifically, the sva package contains functions
for the identifying and building surrogate variables for
high-dimensional data sets. Surrogate variables are covariates
constructed directly from high-dimensional data (like gene
expression/RNA sequencing/methylation/brain imaging data) that
can be used in subsequent analyses to adjust for unknown,
unmodeled, or latent sources of noise. The sva package can be
used to remove artifacts in three ways: (1) identifying and
estimating surrogate variables for unknown sources of variation
in high-throughput experiments (Leek and Storey 2007 PLoS
Genetics,2008 PNAS), (2) directly removing known batch
effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing
batch effects with known control probes (Leek 2014 biorXiv).
Removing batch effects and using surrogate variables in
differential expression analysis have been shown to reduce
dependence, stabilize error rate estimates, and improve
reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008
PNAS or Leek et al. 2011 Nat. Reviews Genetics).

Jeffrey T Leek

Surrogate Variable Analysis

ComBat function

{Batch covariate (multiple batches are not allowed)}

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.

ComBat: Adjust for batch effects using an empirical Bayes framework

Description

Usage

Arguments

Value