[BioC] DESeq2

[Michael Love]

hi Ryan,

Thanks for the comments.

On 03/13/13 08:59, Ryan C. Thompson wrote:
> 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? 
The latter was our reason.  With shrunken coefficients, the differences 
in deviances are no longer distributed as a chi-square. For example, 
with simulated null data (non-intercept betas equal to zero), the 
differences in deviances will pile up near zero and the resulting 
p-values will not be uniformly distributed.

> 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?
>
Yes, we are looking into a convenient interface for this.

best,

Mike

> 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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>
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