sva: ComBat_seq – R documentation

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ComBat_seq

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

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)

sva

Surrogate Variable Analysis

v3.38.0

Artistic-2.0

Authors

Jeffrey T. Leek <jtleek@gmail.com>, W. Evan Johnson <wej@bu.edu>, Hilary S. Parker <hiparker@jhsph.edu>, Elana J. Fertig <ejfertig@jhmi.edu>, Andrew E. Jaffe <ajaffe@jhsph.edu>, Yuqing Zhang <zhangyuqing.pkusms@gmail.com>, John D. Storey <jstorey@princeton.edu>, Leonardo Collado Torres <lcolladotor@gmail.com>

Initial release