ComBat_seq {sva} | R Documentation |
ComBat_seq is an improved model from ComBat using negative binomial regression, which specifically targets RNA-Seq count data.
ComBat_seq( counts, batch, group = NULL, covar_mod = NULL, full_mod = TRUE, shrink = FALSE, shrink.disp = FALSE, gene.subset.n = NULL )
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 |
data A gene x sample count matrix, adjusted for batch effects.
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)