[BioC] nested effects?

[Anand Patel]

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