[BioC] Analyzing mulitple tissues

[Uri David Akavia]

David Kipling wrote:

> Hi
> 
> a)  Method 1.   Use limma() on rma-processed data.  [It doesn't like MAS5 for
> reasons to do with the variance v expression relationship.].   You should then
> be able to get a set of moderated t-statistic p-values for each of your pairwise
> comparions, plus an overall moderated F statistic (which will pull out genes
> changed between any state).   Moderated stats are the way to go when you have so
> few replicates....it circumvents a nasty false positive effect with such
> granular data.   Read the limma users guide (there is a command in the package
> to bring this up).
> 
I didn't say it, but my arrays are Affymetrix arrays - no dye swaps, no 
repeats.
Is it actually possible to use limma (or any t-statistic) when you have 
1 (and only one) value for each sample? The limma guide states that 
three repeats are prefered. This is strengthened by the examples they 
give in http://bioinf.wehi.edu.au/limma/usersguide.pdf, all of which 
have at least a dye swap. So, how can t-statistic work?

> b)  Method 2.  Stick with MAS5, and select potentially differentially regulated
> genes based on having a high covariance (sd/mean).   You'll need to stabilise
> the variance first;  I have a script for this which I can send.  [Don't use
> vsn() on MAS5 data, it isn't designed for it....my script is.]
> 
Indeed, but how do I do the basics? Filter on all 6 samples, normalize 
all 6, and then select the variant genes using 3 samples, then 4 samples 
and other 3 samples as criteria?

Yours,

Uri David Akavia