[BioC] Is a change in kit significant?

[Francois Pepin]

Hi Daniel,

I'm assuming that there should not be any differences between the arrays
with the different kits. If they did the healthy samples first and the
diseased ones on the new kit, then you obviously won't be able to
differentiate between the biological and the kit effect.

There are a few ways you could know if the differences are significant.
If clustering clearly separates samples that should be similar, then you
could use bootstrap (like the pvclust package) to determine
significance. You could also look at the probability to get X
differentially expressed probes/exons/genes between the kits compared to
random permutations of your samples. There should be a number of other
ways to get a p-value out of it.

I hope this helps,

Francois

On Wed, 2007-05-09 at 16:39 +0100, Daniel Brewer wrote: 
> Hi
> 
> I have a set of Affymetrix Exon data which has about 40 samples.  The
> last third of the samples have used a different kit for the experiment,
> and I have been asked to determine whether the change in kit is significant.
> 
> I have done clustering and PCA and the results suggest it does make a
> different, but I would like to put some sort of statistic on it.  What
> is the best way to do this?  I would think maybe this is a limma type
> problem but I am not sure how to get an overall statistic rather than
> just for individual probes.
> 
> Many thanks
> 
> Dan
>