Dear Community, 

I'm characterizing transcriptional responses to transcription factors (TFs) overexpression using RNA-seq.

In brief, I have 5 groups of samples (x, y, z, xyz, Ctrl), where I provide either x, y or z TFs individually, or simultaneously using a multicistronic vector (xyz); Ctrl represents a control vector infection. Each group has 5 replicates. Data has been filtered for 0 counts removal. 

So far, I've used the contrast argument as follows:

numerator<-c("conditionxyz")
denominator<-c("conditionx","conditiony","conditionz")

LC<-list(numerator,denominator)

DEcomposite<-results(dds, contrast=LC, listValues = c(1,-1/3), alpha=0.05, lfcThreshold = 1, altHypothesis = "greaterAbs", independentFiltering = T)

This allowed me to identify the DE genes between the average of the transcriptional effects of x, y, z and their simultaneous effect (xyz).

Currently, I'm trying to estimate the "dose" of x,y and z that better resemble the transcriptional profile of simultaneous xyz overexpression. Which basically, translates to identify the coefficients for the results function argument listValues that minimize the differentially expressed genes.

Does anybody know how to approach this problem?

Thanks in advance!

I don't see how to do this within the DESeq2 context. Let me see if I can turn it into a mathematic formulation:

Suppose you collapse the 5 replicates into an average vector X, Y, Z, and XYZ (these are vectors over genes).

You want to find alpha, beta, gamma > 0 to minimize:

d(alpha X + beta Y + gamma Z, XYZ)

where d(., .) is some distance.