[BioC] Limma and time-course data

[Sean Davis]

On 2/28/06 7:30 AM, "michael watson (IAH-C)" <michael.watson at bbsrc.ac.uk>
wrote:

> Hi
> 
> Googling the list shows this up to be a rather hot topic, but I just
> wanted to ask a few more questions.
> 
> Firstly, it seems the plan for tackling time course data within limma is
> to treat each time-point/treatment combination as a coefficient to be
> estimated.  Thus, to ask "which genes are changing over time", one must
> fit contrasts that compare every single time point to every other time
> point, pairwise, and look for any gene that is significant in one or
> more of those comparisons.  Is that correct?

I would say that this is only one of several ways of analyzing time-course
data, and perhaps not the best one for all situations.  In fact, sometimes I
have the best solution to be simple filtering of genes followed by
clustering and display, but I think the "correct" solution depends on the
experimental design (numbers of time points, for example) and goals (for
example, it doesn't help a biologist to have 2000 genes in a list if the
goal is to find 10 transcription factors that seem to be affected at any
time point).  

For limma, you could use decideTests, for example, to give you some sense of
genes that are changing in the experiment.  Or you could filter for those
genes that are changing at  the "maximal" time point and then show those
genes for all the timepoints on a heatmap--this will allow the biologist to
quickly focus on genes of interest.

Sean