[BioC] Analyzing mulitple tissues

[Naomi Altman]

Biological inference implies that the "signal" can be observed above the 
biological variation.  If you have no biological replicates, you cannot 
determine if your signal is higher than the biological variation.

So, there is no statistically valid means of analyzing your data that 
improves on an arbitrary choice of "fold difference", such as 2-fold 
difference.


Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

At 07:07 AM 6/6/2005, Uri David Akavia wrote:
>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 expressio n 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
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111