Estimate Common Negative Binomial Dispersion by Conditional Maximum Likelihood
Maximizes the negative binomial conditional common likelihood to estimate a common dispersion value across all genes.
## 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, ...)y |
matrix of counts or a |
tol |
the desired accuracy, passed to |
rowsum.filter |
genes with total count (across all samples) below this value will be filtered out before estimating the dispersion. |
verbose |
logical, if |
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. |
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.
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 |
estimateCommonDisp.default returns a numeric scalar of the common dispersion estimate.
Mark Robinson, Davis McCarthy, Gordon Smyth
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
# 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)
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