Adjust for batch effects using an empirical Bayes framework
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(dat, batch, mod=NULL, par.prior = TRUE, prior.plots = FALSE)
- Genomic measure matrix (dimensions probe x sample) - for example, expression matrix
- Batch covariate (multiple batches are not allowed)
- 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
- (Optional)TRUE give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric
- (Optional)FALSE If TRUE ComBat only corrects the mean of the batch effect (no scale adjustment)
data A probe x sample genomic measure matrix, adjusted for batch effects.