[BioC] Limma batch effect
Naomi Altman
naomi at stat.psu.edu
Fri Nov 9 15:31:58 CET 2007
The strain effect is confounded with the batch effect. As a result,
you cannot determine whether differences are due to strain or to batch.
--Naomi
At 10:27 PM 11/8/2007, Jason Pear wrote:
>Hi Dear BioC
>I have question about design matrix considering the batch effect
>using limma. The experiment design is a factorial design.
> Experiment (Affy chip)
> Array Strain Treatment Replicate Batch
> 1 A No 1 1
> 2 A No 2 1
> 3 A No 3 1
> 4 A Yes 1 1
> 5 A Yes 2 1
> 6 A Yes 3 1
> 7 B No 1 2
> 8 B No 2 2
> 9 B No 3 2
> 10 B Yes 1 2
> 11 B Yes 2 2
> 12 B Yes 3 2
> Here we want to konw which genes respond differently in treated
> vs. untreated, and we also want to find out the
> interaction. However, the cluster analysis showed that there is a
> clear batch effect. So I designed the following design matrix to remove the
>batch effect:
>design
> A_unTrt A_Trt B_unTrt B_Trt Batch
> 1 1 0 0 0 0
> 2 1 0 0 0 0
> 3 1 0 0 0 0
> 4 0 1 0 0 0
> 5 0 1 0 0 0
> 6 0 1 0 0 0
> 7 0 0 1 0 1
> 8 0 0 1 0 1
> 9 0 0 1 0 1
> 10 0 0 0 1 1
> 11 0 0 0 1 1
> 12 0 0 0 1 1
>
>contrast <- makeContrasts(
> TrtvsUntrt=(A_Trt+B_trt)-(A_unTrt+B_unTrt),
> interaction=(A_Trt-A_unTrt)-(B_Trt-B_unTrt),
> levels=design)
>However, when I tried to fit the data and the following happes
> fit <- lmFit(myData, design)
>Coefficients not estimable: Batch
>I am using limma version 2.10.5 and R version 2.5.1
>
>Any suggestions?
>
>Many thanks,
>
>Jason
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Naomi S. Altman 814-865-3791 (voice)
Associate Professor
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348 (Statistics)
University Park, PA 16802-2111
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