I have some general questions about doing a time series RNA-seq experiment.

The RNA is from a non-model organism. The experiment has no conditions for comparison (so just the "control" condition, essentially). Three biological replicates of around 500 individuals each were set up, and about 20-30 individuals (per replicate) were taken and sacrificed for sequencing every four hours for two days (thirteen time points in total).

So the data looks like this (with expression quantified for each Sample below):

Sample    Time    Replicate
t0_r1     00      01
t0_r2     00      02
t0_r3     00      03
.         .       .
.         .       .
.         .       .
t48_r1    48      01
t48_r2    48      02
t48_r3    48      03

The main objective of the study is to identify genes that are expressed in a circadian manner. I have decided that I will use MetaCycle to identify the "circadian genes". This analysis seems fairly straightforward. However, I looked at the PCA plot for this data, and the samples are not well-separated (and the replicates do not cluster together). But this is to be expected given that these samples are not from very "different" conditions? Should I attempt to impose some degree of separation upon the data by capturing this variation with latent variables? (E.g., using RUVSeq?)

My other question would be: what other analyses can I perform on this dataset?

For instance, would it make any sense to perform an all-vs.-all differential expression analysis and identify significantly expressed genes shared between all pairwise comparisons?

I am a bit stumped and I would be very grateful for some tips and/or pointers (publications included).