ComBat_seq: Adjust for batch effects using an empirical Bayes framework...

In madhulika-EBI/Batchevaluation: Toolkit for batch correction

Adjust for batch effects using an empirical Bayes framework in RNA-seq raw counts

Description

ComBat_seq is an extension to the ComBat method using Negative Binomial model.

Usage

ComBat_seq(
  counts,
  batch,
  group = NULL,
  covar_mod = NULL,
  full_mod = TRUE,
  shrink = FALSE,
  shrink.disp = FALSE,
  gene.subset.n = NULL
)

Arguments

counts	Raw count matrix from genomic studies (dimensions gene x sample)
batch	Batch covariate (only one batch allowed)
group	Vector / factor for condition of interest
covar_mod	Model matrix for other covariates to include in linear model besides batch and condition of interest
full_mod	Boolean, if TRUE include condition of interest in model
shrink	Boolean, whether to apply empirical Bayes estimation on parameters
shrink.disp	Boolean, whether to apply empirical Bayes estimation on dispersion
gene.subset.n	Number of genes to use in empirical Bayes estimation, only useful when shrink = TRUE

Value

data A probe x sample count matrix, adjusted for batch effects.

madhulika-EBI/Batchevaluation documentation built on Jan. 27, 2023, 5:27 p.m.