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

In jtleek/sva-devel: Surrogate Variable Analysis

Description

ComBat_seq is an improved model from ComBat using negative binomial regression, which specifically targets RNA-Seq count data.

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	Vector / factor for batch
group	Vector / factor for biological condition of interest
covar_mod	Model matrix for multiple covariates to include in linear model (signals from these variables are kept in data after adjustment)
full_mod	Boolean, if TRUE include condition of interest in model
shrink	Boolean, whether to apply shrinkage on parameter estimation
shrink.disp	Boolean, whether to apply shrinkage on dispersion
gene.subset.n	Number of genes to use in empirical Bayes estimation, only useful when shrink = TRUE

Value

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

Examples

count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8)
batch <- c(rep(1, 4), rep(2, 4))
group <- rep(c(0,1), 4)

# include condition (group variable)
adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=group, full_mod=TRUE)

# do not include condition
adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=NULL, full_mod=FALSE)

jtleek/sva-devel documentation built on March 25, 2020, 4:13 a.m.