[BioC] duplicateCorrelation and day effect in limma
Naomi Altman
naomi at stat.psu.edu
Thu Jun 29 04:41:03 CEST 2006
You could do it either way, and your use of duplicateCorrelation is correct.
--Naomi
At 07:57 AM 6/28/2006, Pedro López Romero wrote:
>Dear list,
>
>I have a simple doubt concerning how I should deal with the *replicate day*
>effect using limma.
>It is clear that I have a *day effect* in my data that it has to be taken
>into account in the model. I am using 2 different model specifications and I
>do not know if one of them is correct.
>
>
>a) A first solution is simply to include in the model the *day effect* as an
>additional fixed effect. I will assume that there is not interaction between
>*day effect* and *treatment effect*, so my design matrix will be of the
>form:
>
> design=model.matrix(~ - 1 + factor(treatment) + factor(day) )
>
> fit=lmFit(eset,design)
>
>
>b) My question is if I can take into account the *day effect* as random
>effect using duplicateCorrelation.
>
> famrep=c(
)
> corfit=duplicateCorrelation(eset,ndups=1,block=famrep)
>
> design=model.matrix(~ - 1 + factor(treatment))
>
> fit=lmFit(eset,design,block=famrep,cor=corfit$consensus)
>
>
> Would be this second approach a valid one?
>
>
>
>
>I will appreciate any comment on this.-
>Thanks a lot.-
>
>Pedro
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives:
>http://news.gmane.org/gmane.science.biology.informatics.conductor
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
More information about the Bioconductor
mailing list