My understanding is that your recommended TimeCourse package seems to have been designed for microarray data, and so loosely - the voom transform in limma is to ensure RNA-Seq data can be modified to comply with the statistical assumptions underlying microarray data and its analyses, right?
Given that it's been almost 4 years since the original post here, my question is whether limma + timecourse would still be the best or one of the best ways to analyze time series data today. Your thoughts / advice? TIA!
What is the main goal here ? Do you also have the control for each time point or do you just want to see the genes that are altered in each time point w.r.t 0.h ?
If you are looking for genes that follow a pattern, you could use something like WGCNA to get the tightly co-expressed genes across all time points but you need to have enough samples.
You can also use JTK_CYCLE to get the cycling genes. I heard about this program from this science paper.
If you do pair-wise comparisons is that going to be done with just one sample each or one sample to some replicates of control or are there replicates for all samples at all time points?
Sounds about right....but there will be more considerations that the bioinformaticioan should know about...replicates etc.
Take a look at the original paper for cufflinks. They test they're new software on cells going through different stages of differentiation so gather data at 4 time points (instead of your 5). How they analyse and present the data should give you more ideas. The bioinformatician will (should!) already be familiar with cufflinks.
Agreed to all advices above... If you have enough replicates for each time point, you may also want to do clustering for differentially expressed genes in each time point.