This function obtains dispersion estimates for Negative Binomial distributed data.
1 2 3 4 5 6
either "parametric", "local", or "mean" for the type of fitting of dispersions to the mean intensity.
control parameter: maximum number of iterations to allow for convergence
whether to print messages at each step
Typically the function is called with the idiom:
dds <- estimateDispersions(dds)
The fitting proceeds as follows: for each gene, an estimate of the dispersion
is found which maximizes the Cox Reid-adjusted profile likelihood
(the methods of Cox Reid-adjusted profile likelihood maximization for
estimation of dispersion in RNA-Seq data were developed by McCarthy,
et al. (2012), first implemented in the edgeR package in 2010);
a trend line capturing the dispersion-mean relationship is fit to the maximum likelihood estimates;
a normal prior is determined for the log dispersion estimates centered
on the predicted value from the trended fit
with variance equal to the difference between the observed variance of the
log dispersion estimates and the expected sampling variance;
finally maximum a posteriori dispersion estimates are returned.
This final dispersion parameter is used in subsequent tests.
The final dispersion estimates can be accessed from an object using
The fitted dispersion-mean relationship is also used in
All of the intermediate values (gene-wise dispersion estimates, fitted dispersion
estimates from the trended fit, etc.) are stored in
information about these columns in
The log normal prior on the dispersion parameter has been proposed by Wu, et al. (2012) and is also implemented in the DSS package.
In DESeq2, the dispersion estimation procedure described above replaces the different methods of dispersion from the previous version of the DESeq package.
estimateDispersions checks for the case of an analysis
with as many samples as the number of coefficients to fit,
and will temporarily substitute a design formula
~ 1 for the
purposes of dispersion estimation. This treats the samples as
replicates for the purpose of dispersion estimation. As mentioned in the DESeq paper:
"While one may not want to draw strong conclusions from such an analysis,
it may still be useful for exploration and hypothesis generation."
The lower-level functions called by
The DESeqDataSet passed as parameters, with the dispersion information
filled in as metadata columns, accessible via
mcols, or the final dispersions
Simon Anders, Wolfgang Huber: Differential expression analysis for sequence count data. Genome Biology 11 (2010) R106, http://dx.doi.org/10.1186/gb-2010-11-10-r106
McCarthy, DJ, Chen, Y, Smyth, GK: Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40 (2012), 4288-4297, http://dx.doi.org/10.1093/nar/gks042
Wu, H., Wang, C. & Wu, Z. A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics (2012). http://dx.doi.org/10.1093/biostatistics/kxs033
1 2 3 4
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.