Hi Mike,

Sorry, I missed your second question,

On Feb 24, 2014 10:48 AM, "Mike Stubbington" <mstubb@ebi.ac.uk> wrote:
>
> Dear Wolfgang and Mike,
>
> These are not single-cell data.
>
> I agree that this is not a powerful approach! Rest assured that no major
conclusions will be drawn from the gene clusters; it was more for my own
interest whilst making sure that I understand the correct use of the rlog
transform. I should probably have said “I am more interested *at the
moment* in how the genes cluster…” since I’m also interested in the way
that the cell types cluster. The change from FALSE to TRUE seemed to have a
greater effect upon the gene clusters than the cell clusters so it was that
that piqued my interest.
>
>
> ---------
>
> Mike,
>
> Thank you very much for your reply. It was enormously helpful.
>
>
>
> If I may, I would like to ask one more question: I would like to look at
differential gene expression between the cell types using contrasts. Would
you recommend
>
> 1) Using DESeq2 v 1.2.10 with betaPrior=FALSE as an argument when calling
DESeq
>
> or
>
> 2) Using the development version where expanded model matrices have been
implemented?
>

Either approach is statistically sound. The major difference is that (1)
does not involve moderation of the effect size estimates, i.e. the log fold
change estimates. The p-values are often similar.

(2) will be available with the release of Bioconductor 2.14 on April 14.
Installing the development branch involves installing the development
version of R, which potentially can lead to complications depending on your
system. So (1) might be easiest for now.

Mike

>
> Thank you again for your help,
>
> Mike
>
> On 24 Feb 2014, at 15:33, Wolfgang Huber <whuber@embl.de> wrote:
>
> > Hi Mike
> >
> >> 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.
> >
> > I am not sure I can reconcile this question and the data (experimental
design) you presented. If the aim is to cluster genes within cell type,
would you not do more than two replicates per cell type? (Clustering of
genes based on only two samples seems the equivalent of "underpowered”.)
> > And does it mean you are interested in doing six different clusterings
of genes, and comparing them?
> >
> > I suppose these are not single-cell data? Since the default workflow of
DESeq2 may find difficulties with such data, due to their greater sampling
noise.
> >
> >       Wolfgang
> >
> >
> >
> >> Yours,
> >>
> >> Mike
> >>
> >> <Blind.png><notBlind.png>
> >>
> >>
> >> On 24 Feb 2014, at 14:46, Wolfgang Huber <whuber@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@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
> >>>>
> >>>> _______________________________________________
> >>>> Bioconductor mailing list
> >>>> Bioconductor@r-project.org
> >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>>> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>>
> >>
> >
>

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