Hi,

I always gets confused with the design matrix and even though I figure out the solution I am not very confident. So I though of asking for help. 

I have 3 experiments: Control, wild and knockout. 2 time points: 2 and 4 hr. and 3 replicate for each.

I am not sure which of the following design matrix and contrast is best suited to get DEG for specified scenario.

Case 1:

dds <- DESeqDataSet(se, design = ~Time + Experiment + Time*Experiment)
dds <- dds[rowSums(counts(dds))>1,]
dds$Experiment <- relevel(dds$Experiment, ref="Control")
dds <- DESeq(dds,parallel = T)

resultsNames(dds)  

[1] "Intercept"             "Time_4_vs_2"           "Experiment_Wild_vs_Control"  "Experiment_Knockout_vs_Control" "Time4.ExperimentWild"     "Time4.ExperimentKnockout" 

res.05 <- results(dds,c(0,0,-1,1,-1,1), alpha=0.05)     #For knockout vs wild (both 2hr and 4hr)

res.05 <- results(dds,c(0,0,0,1,0,1), alpha=0.05)     #For knockout vs control (both 2hr and 4hr)

res.05 <- results(dds,c(0,0,1,0,0,0), alpha=0.05)     #For wild vs control 2hr

res.05 <- results(dds,c(0,0,0,-1,0,1), alpha=0.05)     #For knockout 4h vs knockout 2hr

Case 2:

dds <- DESeqDataSet(se, design = ~Time + Experiment)
dds <- dds[rowSums(counts(dds))>1,]
dds$Experiment <- relevel(dds$Experiment, ref="Control")
dds <- DESeq(dds,parallel = T)

resultsNames(dds)

[1] "Intercept"     "Time2"         "Time4"         "ExperimentControl" "ExperimentWild"   "ExperimentKnockout"

res.05 <- results(dds,c(0,0,0,0,-1,1), alpha=0.05)     #For knockout vs wild (both 2hr and 4hr)

res.05 <- results(dds,c(0,0,0,-1,0,1), alpha=0.05)     #For knockout vs control (both 2hr and 4hr)

res.05 <- results(dds,c(0,1,0,-1,1,0), alpha=0.05)     #For wild vs control 2hr

res.05 <- results(dds,c(0,-1,1,0,0,1), alpha=0.05)     #For knockout 4h vs knockout 2hr

Can anyone explain me which case/contrast to use for above scenario? And a little explanation would be very much appreciated.

Thanks

Nitesh

I'm starting a new thread to have more space. All of your contrasts of interest except for the "both 2hr and 4hr" are pairwise comparisons between groups, if you define the groups as unique combinations of factors. So I would recommend you follow the recommendation in the vignette about creating such a variable

dds$group <- factor(paste0(dds$time, dds$experiment))
design(dds) <- ~ group

And then use contrast=c("group","2hrknockout","2hrwild"), etc. There isn't a way to express with a contrast the requirement than something be DE both at 2 hours and at 4 hours, but you can look at the genes which are in the intersection of the DE lists from both pairwise contrasts.

The way to specify DE at either 2 hours and 4 hours or both would be to form a full and reduced model matrix, and use a likelihood ratio test. The reduced model matrix would remove the column corresponding to the main effect at time 2hr as well as the interaction effect.