Description Usage Arguments Details Value References Examples
This function obtains dispersion estimates for Negative Binomial distributed data.
1 2 3 4 5 6  ## S4 method for signature 'DESeqDataSet'
estimateDispersions(object,fitType=c("parametric","local","mean"),maxit=100, quiet=FALSE)
## S4 method for signature 'DESeqDataSet'
estimateDispersions(object, fitType = c("parametric",
"local", "mean"), maxit = 100, quiet = FALSE)

object 
a DESeqDataSet 
fitType 
either "parametric", "local", or "mean" for the type of fitting of dispersions to the mean intensity.

maxit 
control parameter: maximum number of iterations to allow for convergence 
quiet 
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 Reidadjusted profile likelihood
(the methods of Cox Reidadjusted profile likelihood maximization for
estimation of dispersion in RNASeq data were developed by McCarthy,
et al. (2012), first implemented in the edgeR package in 2010);
a trend line capturing the dispersionmean 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 dispersions
.
The fitted dispersionmean relationship is also used in
varianceStabilizingTransformation
.
All of the intermediate values (genewise dispersion estimates, fitted dispersion
estimates from the trended fit, etc.) are stored in mcols(dds)
, with
information about these columns in mcols(mcols(dds))
.
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 lowerlevel functions called by estimateDispersions
are:
estimateDispersionsGeneEst
,
estimateDispersionsFit
, and
estimateDispersionsMAP
.
The DESeqDataSet passed as parameters, with the dispersion information
filled in as metadata columns, accessible via mcols
, or the final dispersions
accessible via dispersions
.
Simon Anders, Wolfgang Huber: Differential expression analysis for sequence count data. Genome Biology 11 (2010) R106, http://dx.doi.org/10.1186/gb20101110r106
McCarthy, DJ, Chen, Y, Smyth, GK: Differential expression analysis of multifactor RNASeq experiments with respect to biological variation. Nucleic Acids Research 40 (2012), 42884297, 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 RNAseq data. Biostatistics (2012). http://dx.doi.org/10.1093/biostatistics/kxs033
1 2 3 4  dds < makeExampleDESeqDataSet()
dds < estimateSizeFactors(dds)
dds < estimateDispersions(dds)
head(dispersions(dds))

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