Hi all,
  I have been working in a project where I have Affymetrix Hgene 1.0 St V1
data. And I have tree groups of patients having 6 samples each. I tried to
perform rma normalization and to filter my data based on expression values
20%. After that went for unpaired t-test to test each two combination of
groups. But the problem is my data is extremely variable.
I have tried to filter my genes based on variance and/or CV before testing,
to try to reduce the number of genes entering your test and multiple
correction.  But with different reasonable filtering also I am with no
luck. And I don't have the option to increase sample size of my project.
Further I tried to check for the bad samples and bad probes from
experimentand remove outlier if these are not of interest. Still the same
when run t-test (and other possible test like Mann-Whitney) with MTC there
are no genes.
On the other hand if I go on with out MTC and select a good p value cutoff
and reasonable fold change I get a list of significant gene which may be
good or reasonable for my study. but the problem is I somehow need to
justify the method for my finding. Do you know any study or paper where
anybody has treated their data without MTC?
My main concern is if I find a good story matching biological prospective,
would it be anyhow possible to justify the method without MTC?
Thanks a lot,
Suparna.

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