[BioC] nested effects?

[Naomi Altman]

Dear Anand,
It seems to me the simplest thing would be to consider patient as the 
block and viral titre as either a continuous covariate.  If the slope 
differs among the strains, you will need to include an interaction term.

--Naomi


At 02:51 PM 2/28/2011, Anand Patel wrote:
>I'm struggling with the best design for modeling effects of different
>viral strains in a complex experiment.
>
>Factors:
>1) Patient (p3, p4, p5)
>2) "Replicate" (a, b, c)
>3) Viral Titer (continuous integer variable)
>4) Viral Strain (O, F, S)
>
>Although all 3 of the "replicates" per patient were treated the same
>way, there are significant differences in the amount of virus
>recovered from each "replicate", and that appears to have a
>significant effect on gene expression (based on multivariate
>projection mapping plots).  As this is a biologically plausible
>result, I'm trying to figure out a way to include the titer
>information in a model while not treating the "replicates" as fully
>independent.
>
>This is complicated by the 0 titer occurring only in the untreated
>wells (again, this makes sense, but makes modeling a challenge).
>
>Using duplicateCorrelation without regards to the experimental design,
>I get a corfit$cor of 0.3790526 .
>
>When I use duplicateCorrelation using:
>design <- model.matrix(~0+p+v)
>(where p and v are factors representing patient and viral strain, 
>respectively)
>
>I get a corfit$cor of 0.1430260.
>
>While titer is related to the individual patient, it's acting
>independently based on mds plots of individual patient gene
>expression, but I'm just not sure how to best model this experiment.
>
>Thoughts?
>
>Thanks,
>Anand
>
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