[BioC] Test: Treatment leads to more variance in groups?

[Benjamin Otto]

Hi,

normally we search for differentially expressed genes in different
observation or treatment groups. So, in a very basic way, one performs a
t.test for each gene between the two groups and takes the p-value as measure
for significance. Now, is it a) possible and b) reasonable to test whether
the two treatments may lead to differentially high expression variances (not
means) in the groups?

To give a very simple biological example I could compare non-tumor to tumor
cells. By intuition I would conclude that the non-tumor cells should have
not only no differentially expressed genes but also nearly no variance in
expression level per gene between the samples which are member of this
group. However the tumor cells could have as one possibility higher/lower
expressed genes (different means, the normal thing) or as second thought
genes which are just kicked out of balance and thus exhibit an extraordinary
high variance between the tumor samples. Now how do I test that? With a
simple F-test between the two groups across each gene?

And for a more global test with a hypothesis like "Tumor cells exhibit more
variance in gene expression across samples than non-tumor cells", do I
compute the variance across each gene for each group and perform a t.test
afterwards between the tumor- and non-tumor-variances?

If this approach seems reasonable, then what is the correct measure to use,
variance or standard deviation? The funny thing is, that when I perform a
t.test for two "variance" groups of mine I get a p-value of 0.3 while the
test for "sqrt(variance)" returns one of 2.3e-16. That really surprises me.

Regards

Benjamin


-- 
Benjamin Otto
Universitaetsklinikum Eppendorf Hamburg
Institut fuer Klinische Chemie
Martinistrasse 52
20246 Hamburg