Entering edit mode
> Date: Tue, 6 May 2008 11:22:32 -0700
> From: "Donna Toleno" <toleno at="" usc.edu="">
> Subject: [BioC] global vs. separate in limma decideTests
> To: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch="">
> Content-Type: text/plain
>
> Hello,
>
> I have a short question about decideTests. I've read a few postings
on this
> topic and I thought that I understood the difference between
"global" and
> "separate". My understanding is that "global" considers all the
contrasts
> which means it is considering many more hypothesis tests than
"separate". I
> ran some analysis using "global" and then I realized that since my
gene
> lists of interest were different for each contrast, I decided that
> "separate" may be the better choice. What puzzled me is that I found
more
> differential expression using "global" than I did with "separate". I
thought
> that more tests would always lead to higher adjusted p-values and
fewer
> inferences of differential expression.
This is generally true for p-value adjustments like Bonferroni or Holm
but
not for FDR methods like BH. With FDR, the proportion of DE genes can
go
up or down as you add more tests. The reason for this is that FDR is
scalable: if you can keep the FDR below a proportion p in several
separate
sets of tests, then it follows that you've also kept FDR below p in
the
combined set of tests.
Strictly speaking, the proportion of DE can go up with Holm also as
you
add more tests, but this doesn't happen so often.
The reason for this phenomenon is the step-up or step-down nature of
these
adjustment methods, which takes the whole set of p-values into account
when adjusting each one.
Best wishes
Gordon
> There was a note of caution from
> Gordon Smyth posted on the list about being careful not to include
spurious
> contrasts. In my case, the other contrasts are not really spurious,
they
> just not the contrasts of interest for that particular subset. The
subsets
> do overlap and probably by a large fraction, that was why I chose
"global"
> at first.
>
> So here is some example code:
>
>> filtered_results_global_ref <-
> decideTests(fit2_tissues,method="global",adjust.method="BH",p.value=
0.05,lfc=log2(1.2))[names(which(selected)),
> 1]
>> standard_DE_down_global <- which (filtered_results_global_ref== -1)
>> length(standard_DE_down_global)
> [1] 274
>
> repeating the above using "separate" gives me fewer significant
> down-regulated genes.
> [1] 244
>
> filtered_results_global_ref <- decideTests(fit2_tissues
> [names(which(selected)),
> 1],method="global",adjust.method="BH",p.value=0.05,lfc=log2(1.2))
#This one
> is the same as using method "separate" because I really am only
considering
> one contrast.
>> standard_DE_down_global <- which (filtered_results_global_ref== -1)
>> length(standard_DE_down_global)
> [1] 244
>
> My questions are 1. Why are there fewer significant results for
"separate"
> than for "global"?