Question: should subgroups of comparisons be analyzed separately in Limma
gravatar for raf4
22 months ago by
raf420 wrote:

Dear Bioconductor,

I have a fluidigm (or other array) experiment which consists of 4 groups: A, B, C, and D.

The experimentalist I am helping wants to compare B to A and D to C,

but not B to D or B to C, etc.

Do I

1. Run all 4 groups together in Limma and then analyze the individual contrasts


2. do I compare B to A and D to C in 2 separate Limma runs?

I believe that 1 is correct, because in an ANOVA one should take into account

the variability of all of the samples, Furthermore, as I understand Limma, more

samples improves the empirical Bayesian estimate of the variance. However, this

approach has recently been questioned by 3 different experimental collaborators,

from 3 different labs, in 3 different contexts, so, I think that it would be prudent to ask the list.

Thanks and best wishes,


Richard Friedman







limma design matrix • 408 views
ADD COMMENTlink modified 22 months ago by Gavin Kelly560 • written 22 months ago by raf420
Answer: should subgroups of comparisons be analyzed separately in Limma
gravatar for Gavin Kelly
22 months ago by
Gavin Kelly560
United Kingdom / London / Francis Crick Institute
Gavin Kelly560 wrote:

It is a judgement call, and your situation is one that many of us can sympathise with.  (1) is the 'correct' approach from a statistical point of view, if you've no reason to doubt that the variance in A and in B is roughly similar to the variance in C and  in D.  You get more power that way, and the (unmoderated) fold-change estimates are identical to what you'd get with the other approach, it's just that you have more samples to use to estimate dispersion (and estimating this is hard, so it's best to have as many samples as possible involved).

The issue might be if the design is really two experiments cobbled together, so you'd be quite entitled to expect one branch to more variable than the other.  Or where there's a 'treatment' that is much more noise-inducing than another (e.g. normal and tumour, or preponderance of 'outlier samples' in one treatment) and all you're interested in is separate results within normal and within tumour - but as soon as you want to compare across the two branches, you're back to (1).  A PCA plot can sometimes provide insight one way or the other about the equality of variability.

Another common situation is for experimentalists to ask for A vs B, and C vs D (however you decide), and then take the results away and do a 'venn diagram in excel' analysis, which I always warn against (often preferable to do an interaction test - difference of significance not being the same as significant differences ) but sometimes have to allow, if the biology is really requiring 'no change' in one branch.

Then you've the problem of persuading the scientist.  I often ask them to explain why they struggled to control noise (or follow protocols) in one branch of their experiment as well as they did in the other branch; this is quite persuasive.


ADD COMMENTlink written 22 months ago by Gavin Kelly560

To expand on Gavin's answer, see Gordon's thoughts about this topic:

A: Correct assumptions of using limma moderated t-test

ADD REPLYlink modified 22 months ago • written 22 months ago by Aaron Lun24k
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 294 users visited in the last hour