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