hi Richard,

I am not sure then why the VST is not stabilizing the variance for your
data. You might try the alternate function, rlogTransformation. We noticed
that the VST was sensitive to large ranges in size factors, while the
rlogTransformation could better handle these cases. This transformation
should work just as well for your final goal of clustering across genes.
With the rlogTransformation, the data is reconstructed on the log2 scale
using a moderated fold change for each sample, described in section 7.1.
The fold changes from genes with low counts and high dispersion are pulled
closer towards zero.

On Tue, May 14, 2013 at 11:47 AM, Hickman, R.J. (Richard) <R.J.Hickman@uu.nl
> wrote:

>
> I think the problem is the variance across time points (there are 10 in
> total). My initial idea for a potential solution was to perform the VST
> using only samples at each time point (i.e, creating a DDS using the
> samples for each time point), however, looking at the manual it states:
> "Limitations: In order to preserve normalization, the same transformation
> has to be used for all
> samples". I have performed between sample normalization with
> estimateSizeFactors() using all samples and so I guess then applying the
> VST using only samples at each individual time point may not be appropriate?
>

Going back to your original question, I think it depends on what you want
to see. It might be the case that the genes act in different modules at
different timepoints and you miss this by clustering over the entire time
scale. Then it might make more sense to perform the VST and clustering for
each timepoint separately (and as you point out, you should calculate size
factors and dispersions separately for each subset). But if you want to
look for global patterns, I would recommend to perform one transformation
on the whole set.

Mike

	[[alternative HTML version deleted]]

