Hello All, 

I have a question regarding one of the experiment that we just got rna seq data for, let me first show the design it looks like this,

We do not have replicates for any model (I hope we can still get some data from this model)

we would like to compare changes in gene expression and junction/exon expression between each time point to the vehicle 

i.e (veh vs 30min, veh vs 1 hour, beh vs 2 hours, veh vs 3 hours) 

My question is  what should the design model look like for deseq2  and dexseq and what are the steps that we need to consider before we do this analysis, would not having replicates be a big issue?

Apologies for asking help upfront, we do not have a statistician that we can reach out on our facility. 

Many thanks. 

Abi

While completely agreeing with Michael's answer on the impossibility of testing pairs of timepoints, I'm wondering if you could take some prior expectations into account.  If you're expecting WT and KO to behave similarly at certain timepoints (may be the 10min placebo), then you could label those samples as being replicates - or similarly if you expect one of the conditions to be constant during a certain time-interval, you could label those as replicates (by which I mean giving them all the same <sample, time, type> value.  Not ideal, by any means, as you're saying any variability between these 'virtual' replicates truly reflects the unobserved true replicate variability.

Expanding Michael's point about time profiles, you could model `design$time <- c(10/60, 10/60, 30/60, 30/60, 1, 1, 2, 2, 3, 3)` and then having `time` in the DESeq2 model, would look for things that look linear when plotting log expression against time.  You if you had prior biological reason to believe that the profiles should peak at t=1.5hr,  one could similarly have a '(time-1.5)^2` term. But you'd need to choose a model once-and-for-all, rather than exploring different models until you find something interesting.  As Michael says, it's definitely a case where consulting a statistician would be advised.

While you don't have replicates at each time point and so you can't make comparisons at each time point, you can use R to build models of the log of gene expression over time and compare the profiles overall. Have you seen how examples of genes of interest change over time in terms of normalized counts (on log scale)?