Dear list,

I posted a question few days ago with any success, so I decided to try
again, explaining better my question and changing the header.

I am analyzing RNA-Seq data with edgeR - DeSeq. I have two biological
groups, two replicates each, and I want to test DE between the two
biological groups.

For instance, with edgeR, I calculated tagwise dispersion for each gene.
With this dispersion data, I calculated the variance according to the
formula V = mu *( 1 + dispersion * mu). I used the definition from
http://seqanswers.com/forums/showthread.php?t=5591&highlight=edgeR+variance.

When I plot the correlation of the variance between the two biological
groups, I found they have very good correlation. According to this result
we can conclude that for most genes the variance is equal between groups.
>From this it comes my first question: Is the assumption of equal variances
a requisite to perform the DE test?

After this, I calculated the sqrt(dispersion) for every gene, i.e.,
according to edgeR and DeSeq manuals, the coefficient of biological
variation (i.e, C.V = s.d./mean = sqrt(dispersion)). Well, when I plot the
correlation of the C.V. between the two biological groups, what I found now
is that the C.V. for one biological group is systematically higher than the
C.V. in the other group. In other words, for most genes in group 1, the
C.V. is higher than that in group 2. This result can be nicely seen as a
regression line that is parallel an above the expected y = x. Indeed I
found something like y = 0.11 + x.

This result scares me a lot. If I understood well, since C.V.1 > C.V.2;
sqrt(var1)/mean1 > sqrt(var2)/mean2; since var1 ~ var2, then mean1<mean2
for most of the genes, which is obviously false. What am I missing? How is
it possible that two groups have similar variance but one group have higher
C.V. than the other (for most genes!)?

I did not check this with DeSeq yet, but I assume the results will be
similar (given that the amount of DE genes are similar and congruent).

Any help would be very appreciated.

Many thanks,
Miguel Gallach

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