[BioC] LIMMA : design (1, 2, 3, 3 ) , I got EXCITING results, what could be the logic, since i have 2 replicates for 3rd group only ?

Sean Davis sdavis2 at mail.nih.gov
Wed Apr 27 12:07:28 CEST 2005

On Apr 26, 2005, at 9:51 PM, Saurin Jani wrote:

> Hi Adai,
> Yes, you are right. I have 4 samples :
> Group1 = Growth Effect for Day 1 : 1 Affy GeneChip.
> Group2 = Growth Effect for Day 2 : 1 Affy GeneChip.
> Group3 = Growth Effect for Day 3 : 2 Affy GeneChips.
> so, my design matrix is:
> design <- model.matrix(~ -1+factor(c(1,2,3,3)));
> LIMMA did not give any error or waring even it has 1
> sample per group...! ( I thought similar thing,  since
> it needs technical replicates per group to make a
> decision). The results are very interesting. I have
> many genes for 0.01 FDR, which is very good.
> Somehow,I don't understand the logic. Do you think is
> this a valid design? Or You think I should go by Fold
> Change Logic. Please, let me know.

Limma can and does use a "pooled" variance estimate, so the estimate of 
variance used here, though not "within-gene", is probably not too far 
off (i.e., you can have an estimate of the variance with only one array 
in a group).  Without replicates, that estimate is certainly subject to 
more error than with replicates.  However, fold-change is probably even 
one more step away from any statistical footing, as it includes NO 
estimate of variance and probably offers little or no advantage.  That 
said, high fold-changes and low p-values are probably more likely than 
low fold-changes and higher p-values to be real, but I don't think that 
either measure is likely to be very robust.

Arguments can proceed down the statistical road, but obviously the best 
(only) option that will produce statistically meaningful results would 
be to do more arrays.  Short of that, I think the fold-change and limma 
are likely in practice to produce similar ordered lists that could, at 
best (and with the knowledge that the order is to be taken with a large 
grain of salt), be used to guide biologic validation or further 


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