hi Mike,


On 24 Feb 2014, at 15:21, Mike Stubbington <mstubb@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?
> >
>
> ​
Yes, I think it would be appropriate to use blind=FALSE here. I added this
note to the vignette after this discussion a month ago:

https://stat.ethz.ch/pipermail/bioconductor/2014-January/057293.html

​By default, the VST and rlog transformation use blind=TRUE, so that if
people are using these transformations for quality assessment, the
experimental design has absolutely no influence on the transformations
(i.e. it is an unsupervised method).

When blind=FALSE, the experimental design is only used by the VST and rlog
transformations in calculating the gene-wise dispersion estimates, in order
to fit a trend line through the dispersions over the mean. Only the trend
line is then used by the transformations, not the gene-wise estimates.
Therefore, for visualization, clustering, or machine learning applications
I tend to recommend blind=FALSE.

The downside of setting blind=TRUE, is that large differences due to the
experimental design (e.g., cell types in your case, or different water
columns in the linked discussion above), will inflate the gene-wise
dispersion estimates. When most of the genes contain such large differences
across conditions, this will raise the trend-line, and then the transformed
values will be greatly shrunken toward each other for most genes, which is
an undesirable loss of signal.

Mike

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