I have a question regarding a RNA-seq data set I have. This data consists of 2 genotypes (WT, mutant), 2 treatments (Mock, treated), and 4 times points (Time1, Time2, Time3, Time4).

I set up my deseq dataset as such:

dds <- DESeqDataSetFromMatrix(countData = countData,
                               colData = metadata,
                               design = ~ Genotype+Treatment+Time+Genotype:Treatment)

dds$Genotype <- relevel(dds$Genotype, ref = "WT")
dds$Treatment <- relevel(dds$Treatment, ref = "Mock")
dds$Time <- relevel(dds$Time, ref = "Time1")

dds_deseq <- DESeq(dds, quiet = FALSE, parallel=TRUE) 


res = results(dds_deseq, contrast=list( c("GenotypeMutant.Treatmenttreated" ) ))

I am unsure if this is extracting the interaction of genotype and treatment across all time points or if the reference of "Time1", controls at what time point this comparison is pulled. If so, does that mean as long as the Time reference is revealed before running Deseq, I will be able to extract the DEGs from each time point separately?

I want to normalize all my samples consistently since there have slightly different read depths, so I don't want to separate the data and analyze them individually. I read in the vignette that running altogether is ideal. It seems like running the data together lowers the sensitivity to detect DEGs. I was wondering why that is.

Thanks!

I recommend working with a local statistician or someone familiar with linear models in R, to decide upon an analysis plan and help interpreting results. I have to restrict my time on the support site to software related questions and issues.