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cpmByGroup <- function(y, ...)
UseMethod("cpmByGroup")
cpmByGroup.DGEList <- function(y, group=NULL, dispersion=NULL, ...)
# Counts per million averaged by group
# Gordon Smyth
# Created 10 July 2017. Last modified 4 Nov 2018.
{
if(is.null(group)) group <- y$samples$group
group <- as.factor(group)
if(is.null(dispersion)) dispersion <- getDispersion(y)
if(is.null(dispersion)) dispersion <- 0.05
offset <- getOffset(y)
cpmByGroup(y$counts,group=group,dispersion=dispersion,offset=offset,weights=y$weights,...)
}
cpmByGroup.SummarizedExperiment <- function(y, group=NULL, dispersion=NULL, ...)
# Created 03 April 2020. Last modified 03 April 2020.
{
y <- SE2DGEList(y)
cpmByGroup.DGEList(y, group=group, dispersion=dispersion, ...)
}
cpmByGroup.default <- function(y, group=NULL, dispersion=0.05, offset=NULL, weights=NULL, log=FALSE, prior.count=2, ...)
# Counts per million averaged by group
# Gordon Smyth
# Created 10 July 2017. Last modified 4 Nov 2018.
{
y <- as.matrix(y)
if(is.null(group)) {
group <- factor(rep_len(1,ncol(y)))
levels(group) <- "AveCPM"
}
if(is.null(offset)) offset <- log(colSums(y))
if(log) {
YP <- addPriorCount(y,offset=offset,prior.count=prior.count)
fit <- mglmOneWay(YP$y,group=group,dispersion=dispersion,offset=YP$offset,weights=weights)
fit$coefficients / log(2) + log2(1e6)
} else {
fit <- mglmOneWay(y,group=group,dispersion=dispersion,offset=offset,weights=weights)
exp(fit$coefficients) * 1e6
}
}
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