aveLogCPM {edgeR} | R Documentation

## Average Log Counts Per Million

### Description

Compute average log2 counts-per-million for each row of counts.

### Usage

    ## S3 method for class 'DGEList'
    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, ...)

### Arguments

`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 `nrow(y)`, containing the average value(s) to be added to each count to avoid infinite values on the log-scale.
`dispersion` | numeric scalar or vector of negative-binomial dispersions. Defaults to 0.05.
`lib.size` | numeric vector of library sizes. Defaults to `colSums(y)`. Ignored if `offset` is not `NULL`.
`offset` | numeric matrix of offsets for the log-linear models.
`weights` | optional numeric matrix of observation weights.
`...` | other arguments are not currently used.

### Details

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.

### Value

Numeric vector giving log2(AveCPM) for each row of `y`.

### Author(s)

Gordon Smyth

### See Also

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`.

### Examples

    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))

[Package edgeR version 3.26.8 Index]