Greetings,

I have "inherited" a cDNA macroarray dataset that is structured as follows.
Three different stressors were tested.  For each stressor, there are two
treatments (control and stressed).  For each treatment, two biological
replicates exist, and these are paired (i.e., there is a stressed array for
colony A and a control array from this same colony).  For one of these
samples, duplicate arrays were performed (technical replicates).  This works
out to 18 different arrays corresponding to 12 independant biological
replicates.  But counting only the biological replicates for each stressor,
there are only n=2 stressed arrays and n=2 control arrays.

I am pretty well versed in the analysis of array data using R, but obviously
this dataset presents a real challenge because of the low replication.  For
logistical reasons, increasing the sample size is not a possibility.  My
main goal here is to salvage whatever valid findings can be salvaged from
the existing data, but I dont want to go too far in claiming significance
for an expression pattern if there isnt really anystatistical support for
it.

My questions are:
(1) Whether it is even possible to statistically compare the effects of
these stressors on gene expression,
(2) If so, what are folks' recomendations?
(3) Obviously low sample size means low statistical power, but I have always
been told that calculating variance for n=2 and doing stats on that basis is
not even mathematically valid.  Can anyone confirm or refute this?

Thank you for any advice you may have to offer,

-- 
Eli Meyer
Postdoctoral Fellow
Department of Integrative Biology
University of Texas at Austin
Austin, TX 78712
office: (512) 475-6424
cell: (310) 618-4483

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