[BioC] DESeq2

[Ryan C. Thompson]

Hello,

I noticed the addition of the DESeq2 package a few days ago and had a 
look at the new additions. Overall, it looks like an excellent package 
(and it runs a lot faster than DESeq 1, too). I have a few questions for 
clarification of exactly what methods DESeq2 is using. Specifically, I 
notice that the fold change shrinkage is performed in nbinomWaldTest but 
not in nbinomChisqTest. Is this just for reasons of 
backward-compatibility of results, or is the Chi-squared test logically 
incompatible with shrunken GLM coefficients? Secondly, is there any plan 
to extend the Wald test to testing contrasts of multiple coefficients or 
testing multiple coefficients/contrasts at once in an ANOVA-like test?

Thanks,

-Ryan Thompson

On 03/12/2013 01:51 PM, Wolfgang Huber wrote:
> Dear DESeq users,
>
> Mike Love, Simon Anders and I have been updating the DESeq package. This resulted in the package DESeq2, which is available from the development branch, and scheduled for the next release: http://www.bioconductor.org/packages/devel/bioc/html/DESeq2.html
>
> For several release cycles, the original package (DESeq) will be maintained at its current functionality, in order to not disrupt the workflows of DESeq users. For new projects, we recommend using DESeq2. Major innovations are:
>
> * Base class: SummarizedExperiment is used as the superclass for storing the data, rather than eSet.  This allows closer integration with upstream workflows involving GRanges and summarizeOverlaps, and facilitates downstream analyses of the genomic regions of interest.
>
> * Simplified workflow: the wrapper function DESeq() performs all steps for a differential expression analysis. The individual steps are of course also accessible.
>
> * More powerful statistics: incorporation of prior distributions into the estimation of dispersions and fold changes (empirical-Bayes shrinkage). The dispersion shrinkage improves power compared to the old DESeq. The fold changes shrinkage help moderate the otherwise large spread in log fold changes for genes with low counts, while it has negligible effect on genes with high counts; it may be particularly useful for visualisation, clustering, classification, ordination (PCA, MDS), similar to the variance-stabilizing transformation in the old DESeq. A Wald test for significance is provided as the default inference method, with the chi-squared test of the previous version is also available. A manuscript is in preparation.
>
> * Normalization: it is possible to provide a matrix of sample- *and* gene-specific normalization factors, which allows the use of normalisation factors from Bioconductor packages such as cqn and EDASeq.
>
> Examples of usage are provided in the vignette, and more details are available in the manual pages (specifically, the DESeq function and estimateDispersions function).
>
> Enjoy -
>
> 	Mike, Simon, Wolfgang.
>
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