[BioC] Test: Treatment leads to more variance in groups?
huber at ebi.ac.uk
Mon Jul 3 15:48:31 CEST 2006
the question is very reasonable. More general, you might not just look
for differential variance, but for different distributions in the
groups. Have a look at the EDD package.
Also, the work by Margarete Pepe on the pAUC statistic might be relevant.
You need a substantial number of samples in each group to meaningfully
do these things (more than if you just test for difference of location)
- that is probably one of the reasons why difference of the locations is
what most people go for.
> 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.
Wolfgang Huber EBI/EMBL Cambridge UK http://www.ebi.ac.uk/huber
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