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predFC <- function(y,design,prior.count=0.125,offset=NULL,dispersion=NULL,weights=NULL,...)
UseMethod("predFC")
predFC.DGEList <- function(y,design,prior.count=0.125,offset=NULL,dispersion=NULL,weights=NULL,...)
{
if(is.null(offset)) offset <- getOffset(y)
if(is.null(dispersion)) dispersion <- getDispersion(y)
if(is.null(dispersion)) {
dispersion <- 0
message("dispersion set to zero")
}
predFC.default(y=y$counts,design=design,prior.count=prior.count,offset=offset,dispersion=dispersion,weights=weights,...)
}
predFC.SummarizedExperiment <- function(y,design,prior.count=0.125,offset=NULL,dispersion=NULL,weights=NULL,...)
# Created 03 April 2020. Last modified 03 April 2020.
{
y <- SE2DGEList(y)
predFC.DGEList(y, design=design, prior.count=prior.count, offset=offset, dispersion=dispersion, weights=weights, ...)
}
predFC.default <- function(y,design,prior.count=0.125,offset=NULL,dispersion=0,weights=NULL,...)
# Shrink log-fold-changes towards zero by augmenting data counts
# Gordon Smyth and Belinda Phipson
# 17 Aug 2011. Last modified 9 July 2017.
{
# Add prior counts in proportion to library sizes
out <- addPriorCount(y, offset=offset, prior.count=prior.count)
# Check design
design <- as.matrix(design)
# Return matrix of coefficients on log2 scale
g <- glmFit(out$y,design,offset=out$offset,dispersion=dispersion,prior.count=0,weights=weights,...)
g$coefficients/log(2)
}
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