[BioC] ttest or fold change

[michael watson (IAH-C)]

>This seems small but with a microarray with thousands of genes, this
>easily produces a bunch of false positives. I looked at 10 chips from a
>real control group arbitrarily labeling 5 chips as control and 5 as
>experimental.  I would by theory expect 35 false positives and got
>exactly 32, that is 32 sitations in which all the low ranks were in one
>group and the high ranks in the other.  For a chip with 22000 genes, you
>would expect 175 false positive results by this criteria. Standard
>statistical methods would give you a specified type I error rate that
>you can count on, it would have found NONE of the genes significant
>(i.e. bonferroni adjustment)

A truly excellent reply, and one which I will no doubt refer to frequently; I am still
very much a novice statistician.  However, and please correct me if I am wrong, but
I presume that some scientists are equally afraid of false negatives as false positives?
i.e. that if we are so conservative such that we try to ENSURE that there are NO
false positives, we may throw away genes as not differentially expressed when in
reality they are?  It will be interesting to have a discussion on this - is it possible,
using statistics, to guarentee both no false positives and no false negatives?  If not,
then surely the investigator must decide which is relevant to the study in question before 
going on to decide which stats to use.