estimateCommonDisp {edgeR} | R Documentation

## Estimate Common Negative Binomial Dispersion by Conditional Maximum Likelihood

### Description

Maximizes the negative binomial conditional common likelihood to estimate a common dispersion value across all genes.

### Usage

    ## S3 method for class 'DGEList'
    estimateCommonDisp(y, tol=1e-06, rowsum.filter=5, verbose=FALSE, ...)
    ## Default S3 method:
    estimateCommonDisp(y, group=NULL, lib.size=NULL, tol=1e-06,
              rowsum.filter=5, verbose=FALSE, ...)

### Arguments

`y` | matrix of counts or a `DGEList` object.
`tol` | the desired accuracy, passed to `optimize`.
`rowsum.filter` | genes with total count (across all samples) below this value will be filtered out before estimating the dispersion.
`verbose` | logical, if `TRUE` then the estimated dispersion and BCV will be printed to standard output.
`group` | vector or factor giving the experimental group/condition for each library.
`lib.size` | numeric vector giving the total count (sequence depth) for each library.
`...` | other arguments that are not currently used.

### Details

Implements the conditional maximum likelihood (CML) method proposed by Robinson and Smyth (2008) for estimating a common dispersion parameter. This method proves to be accurate and nearly unbiased even for small counts and small numbers of replicates.

The CML method involves computing a matrix of quantile-quantile normalized counts, called pseudo-counts. The pseudo-counts are adjusted in such a way that the library sizes are equal for all samples, while preserving differences between groups and variability within each group. The pseudo-counts are included in the output of the function, but are intended mainly for internal edgeR use.

### Value

`estimateCommonDisp.DGEList` adds the following components to the input `DGEList` object:

`common.dispersion` | estimate of the common dispersion.
`pseudo.counts` | numeric matrix of pseudo-counts.
`pseudo.lib.size` | the common library size to which the pseudo-counts have been adjusted.
`AveLogCPM` | numeric vector giving log2(AveCPM) for each row of `y`.

`estimateCommonDisp.default` returns a numeric scalar of the common dispersion estimate.

### Author(s)

Mark Robinson, Davis McCarthy, Gordon Smyth

### References

Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332. http://biostatistics.oxfordjournals.org/content/9/2/321

### See Also

`equalizeLibSizes`, `estimateTrendedDisp`, `estimateTagwiseDisp`

### Examples

    # True dispersion is 1/5=0.2
    y <- matrix(rnbinom(250*4,mu=20,size=5),nrow=250,ncol=4)
    dge <- DGEList(counts=y,group=c(1,1,2,2))
    dge <- estimateCommonDisp(dge, verbose=TRUE)

[Package edgeR version 3.26.8 Index]