DESeq2

[Simon Anders]

Is it just me or the bioconductor linked vignette is a 16Mb pdf with 10701 pages and a lot of repetitions starting from page 6?

Yes, that's a bug that Mike has already fixed. The corrected vignette should become available today or tomorrow.

[EGrassi]

Thanks! Looking forward to the new options.

Is it just me or the bioconductor linked vignette is a 16Mb pdf with 10701 pages and a lot of repetitions starting from page 6?

[turnersd]

Great news, thanks Simon, Mike, Wolfgang. Looking forward to going through the vignette. Are you publishing any comparisons with the original DESeq and/or other tools?

[chadn737]

Exciting news, thanks Simon. You guys have created some of the best tools out there and I am excited to see what this offers.

PS. I notice that the vignette is as well written as your last and puts the details on a level that people like me can easily grasp. Thanks.

[Simon Anders]

Hi,

An announcement of interest to users of DESeq:

Mike Love, Wolfgang Huber and I have been updating the DESeq package. This resulted in the package DESeq2, which is already now available from the Bioconductor development branch, and scheduled to be included in the next Bioconductor release.

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 (from the GenomicRanges package) is used as the superclass for storing the data, rather than eSet. This allows closer integration with upstream workflows involving GenomicRanges features, such as 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.