estimateCommonDisp: Estimate Common Negative Binomial Dispersion by Conditional...

In OliverVoogd/edgeR: Empirical Analysis of Digital Gene Expression Data in R

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)

OliverVoogd/edgeR documentation built on July 28, 2022, 10:13 p.m.