Average Log Counts Per Million
Compute average log2 counts-per-million for each row of counts.
## S3 method for class 'DGEList'
aveLogCPM(y, normalized.lib.sizes=TRUE, prior.count=2, dispersion=NULL, ...)
## S3 method for class 'SummarizedExperiment'
aveLogCPM(y, normalized.lib.sizes=TRUE, prior.count=2,
dispersion=NULL, ...)
## Default S3 method:
aveLogCPM(y, lib.size=NULL, offset=NULL, prior.count=2, dispersion=NULL,
weights=NULL, ...)y |
numeric matrix containing counts. Rows for genes and columns for libraries. |
normalized.lib.sizes |
logical, use normalized library sizes? |
prior.count |
numeric scalar or vector of length |
dispersion |
numeric scalar or vector of negative-binomial dispersions. Defaults to 0.05. |
lib.size |
numeric vector of library sizes. Defaults to |
offset |
numeric matrix of offsets for the log-linear models. |
weights |
optional numeric matrix of observation weights. |
... |
other arguments are not currently used. |
This function uses mglmOneGroup to compute average counts-per-million (AveCPM) for each row of counts, and returns log2(AveCPM).
An average value of prior.count is added to the counts before running mglmOneGroup.
If prior.count is a vector, each entry will be added to all counts in the corresponding row of y, as described in addPriorCount.
This function is similar to
log2(rowMeans(cpm(y, ...))),
but with the refinement that larger library sizes are given more weight in the average. The two versions will agree for large values of the dispersion.
Numeric vector giving log2(AveCPM) for each row of y.
Gordon Smyth
See cpm for individual logCPM values, rather than genewise averages.
Addition of the prior count is performed using the strategy described in addPriorCount.
The computations for aveLogCPM are done by mglmOneGroup.
y <- matrix(c(0,100,30,40),2,2) lib.size <- c(1000,10000) # With disp large, the function is equivalent to row-wise averages of individual cpms: aveLogCPM(y, dispersion=1e4) cpm(y, log=TRUE, prior.count=2) # With disp=0, the function is equivalent to pooling the counts before dividing by lib.size: aveLogCPM(y,prior.count=0,dispersion=0) cpms <- rowSums(y)/sum(lib.size)*1e6 log2(cpms) # The function works perfectly with prior.count or dispersion vectors: aveLogCPM(y, prior.count=runif(nrow(y), 1, 5)) aveLogCPM(y, dispersion=runif(nrow(y), 0, 0.2))
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