[BioC] Limma factorial experiment: interaction parameter

[Donald Dunbar]

Thanks for that Oscar, it's a big help.
With best wishes,
Donald

On 03/29/2011 05:07 PM, Oscar Rueda wrote:
> Dear Donald,
>
> Yes, the contrast to test the interaction effect should be DH-DC-CH+CC 
> ((combo-control)-(x-control)-(y-control)).
> If you don't get significant probes it means that the effect of the 
> combination of the two drugs is the sum of the marginal effects; that 
> could make sense depending on the nature of your experiment.
>
> Hope it helps,
>
> Oscar
>
>
> On 29/3/11 16:27, "Donald Dunbar" <donald.dunbar at ed.ac.uk> wrote:
>
>     Hello,
>     I have a gene expression experiment with four replicates each for four
>     groups: control, two drugs and their combination.
>
>     Control (C.C)
>     Drug1 (D.C)
>     Drug2 (C.H)
>     Drug1+Drug2 (D.H)
>
>     I have imported the Affymetrix data, normalised and set up a design to
>     analyse in Limma.
>
>         C.C D.C C.H D.H
>     1    1   0   0   0
>     2    1   0   0   0
>     3    1   0   0   0
>     4    1   0   0   0
>     5    0   1   0   0
>     6    0   1   0   0
>     7    0   1   0   0
>     8    0   1   0   0
>     9    0   0   1   0
>     10   0   0   1   0
>     11   0   0   1   0
>     12   0   0   1   0
>     13   0   0   0   1
>     14   0   0   0   1
>     15   0   0   0   1
>     16   0   0   0   1
>
>
>     I initially set up contrasts and following lmFit, contrasts.fit,
>     eBayes,
>     and decideTests find differentially expressed probe-sets:
>
>     drug 1 v control contrast 'D.C-C.C' gives 46 probesets
>     drug 2 v control contrast 'C.H-C.C' gives 233 probesets
>     drug 1 + drug 2 combination contrast 'D.H-C.C' gives 3897 probesets
>
>     Clearly there are several thousand probe-sets differentially expressed
>     by the drug combination and not by the individual drugs. I can find
>     these crudely by filtering the p-values of the contrasts, but I would
>     rather do it properly.
>
>     I would like to define an interaction parameter and select
>     differentially expressed genes. I read Gordon Smyth's Limma user guide
>     and several helpful posts on this list but have so far failed to
>     find an
>     appropriate parametrization. The user guide's factorial example has
>     sepatare controls for each treatment group so isn't applicable for
>     me I
>     think. James MacDonald gave a helpful reply to Suzanne Szak in May
>     2009
>     and I thought I'd try that:
>
>     (combo-control)-(x-control)-(y-control) but I think I'm
>     misunderstanding
>     his answer: if I try this (D.H-C.C)-(D.C-C.C)-(C.H-C.C) I get no
>     significant probe-sets.
>
>     My own idea was (D.H-D.C)-(C.H-C.C) but that also gave no significant
>     probe-sets and I think was a bit silly.
>
>     Any help on this would be greatly appreciated.
>
>     With best wishes,
>     Donald
>
>     --
>     Donald Dunbar
>     University of Edinburgh
>     donald.dunbar at ed.ac.uk
>
>
>
>
> Oscar M. Rueda, PhD.
> Postdoctoral Research Fellow, Breast Cancer Functional Genomics.
> Cancer Research UK Cambridge Research Institute.
> Li Ka Shing Centre, Robinson Way.
> Cambridge CB2 0RE
> England
>
>
>
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-- 
Donald Dunbar
donald.dunbar at ed.ac.uk

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