[BioC] DESeq2 - regularised log transformation blind or not?

[Mike Stubbington]

Dear Wolfgang,

Thank you for your reply. 

Attached are the PCA plots generated by plotPCA() from the rlog transformed data with blind set to TRUE or FALSE. Each cell type has two replicates. I would appreciate your thoughts on them.

If it helps in framing my question, I am more interested in how the genes cluster within a cell-type than how the cell types cluster.

Yours,

Mike




On 24 Feb 2014, at 14:46, Wolfgang Huber <whuber at embl.de> wrote:

> Hi Mike
> 
> Thanks.
> The other Mike (Love) will chime in regarding the theoretical considerations regarding the two choices (blind=FALSE or TRUE).
> What I’d be interested in is whether the two make any significant difference to the clustering result (e.g. PCA/MDS plot) for your data?
> 
> 	best wishes
> 		Wolfgang
> 
> On 24 Feb 2014, at 15:21, Mike Stubbington <mstubb at ebi.ac.uk> wrote:
> 
>> Hi,
>> 
>> I have just been reading the updated vignette for DESeq2 in the bioconductor devel branch (http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf) and was interested by the comments in section 2.1.1 about the appropriateness of setting the blind argument when performing regularised log transformation. Specifically, the comment that
>> 
>> “...blind dispersion estimation is not the appropriate choice if one expects that many or the majority of genes (rows) will have large differences in counts which are explanable by the experimental design…”
>> 
>> Given this, I would really appreciate some further advice about when one should set blind=FALSE.
>> 
>> For example, I am performing gene clustering using RNA-seq data for different six cell types. I would certainly expect a lot of genes to vary between the samples. Is this a case when blind=FALSE might be appropriate? 
>> 
>> Thank you for your help,
>> 
>> Mike
>> 
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>