[BioC] Limma or something else ? plus Normalization
smyth at wehi.edu.au
Mon Sep 29 16:22:29 MEST 2003
At 02:47 AM 29/09/2003, Phguardiol at aol.com wrote:
>let say I have 9 chips (Affy U133A)
>A1, A2, A3 triplicates = group A
>B1, B2, B3 triplicates = group B
>C1, C2, C3 triplicates = group C
>I d like to know what are the genes differentially expressed between A &
>B, A & C, B & C
>I see two options (1) comparing 2 by 2 these groups using for instance LPE
>or another test like this one,
> option (2) using Limma.
LPT is the "local pooled error test" proposed in a papery by Jain et al,
is apparently to appear in Bioinformatics.
Limma and LPT address the same problem but from different points of view.
Scanning the paper by Jain et al, they don't compare their method with any
of its natural competitors (a common complaint with the Bioinformatics
literature at the moment), so there are no objective grounds for choosing
between the different methods which moderate, pool or smooth the variances.
The LPT uses intensity as a predictor of variability and, given intensity,
doesn't seem to allow genes to have individual variances. One might guess
that LPT would do well if you are using a method like MAS for affy data or
local background correction for cDNA data which produces a strong
relationship between intensity and variability, and if the genes are not
otherwise very different re variability. One might guess that limma would
do better if you use BioC style normalization for affy or smoothed
background for cDNA or if the genes are greatly different re variability
not related to intensity.
One can't really know though without tests on good data where some form of
truth is available.
>What would be the best option ? I like using LPE since it is well designed
>for low replicates. If I use option (1) should I normalize all the chips
>during the same process or first normalize group A with group B and run
>the comparison..etc ? My concern here is that I will use a relatively low
>number of chips with RMA / Quantile normalization, in the other way
>(normalizing everything at the same time) I could introduce "a bias" that
>is not needed.
Don't really understand your question here. It's almost always best to
normalize your data all at once, and the same for the analysis. Is the
problem that LPT is applicable only to pairwise comparisons?
>Any comments is apppreciated as usual.Thanks
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