I've been trying to perform some time-series analysis (identification of genes with non-constant expression over time, clustering of genes given their trend over time...) of RNA-Seq data (counts obtained from featureCounts) for a group of 200 patients with arthritis, all sampled at 5 different time points (from diagnosis and up to 2 years after that).
I've come across many packages/tools (I focused on R and Python solutions) but most of them seem focused on differential expression analysis between two conditions or more, which is not what I'm looking for. I was wondering if anyone came across the same problem and what worked to address it?
I tried using R packages, using all genes (>50 000) or subsets, especially EBSeqHMM or maSigPro as they seemed to be able to deal with this but have failed to obtain results (it seems there are too many replicates in the case of EBSeqHMM and I don't get any significant results with maSigPro). I also considered fitting linear models to each one of the genes (something like gene~time+patient_id) and cluster them based on the output models but am unsure if this is a good way to go.
Recommendations would be greatly appreciated.