batchSEQ: a pipeline for batch correction and mean-variance function...

In kokrah/cbcbSEQ: RNAseq analysis pipeline for UMD CBCB collaborators with batch effect assessment and correction

Description

The pipeline includes 1) quantile normalization 2) log-transformation of counts 3) combat batch correction 4) voom calculation of weights for testing from mean-variance relationship

Usage

batchSEQ(counts, design, batch, condition, lib.size = NULL, verbose = FALSE,
  plot = FALSE, filter = FALSE, ...)

Arguments

counts	Count matrix
design	model.matrix for differential expression testing
batch	factor indicating batch
condition	factor indicating biological condition
lib.size	library sizes, if NULL or missing, uses the sum of quantile-normalized counts (default=NULL)
verbose	print extra information
plot	plot the mean-variance fit
filter	filter genes with low expression
...	pass arguments to internal functions

Value

list with components elist (result of calling voom) and combatEstimates (batch effect estimates from combat)

kokrah/cbcbSEQ documentation built on May 20, 2019, 12:54 p.m.