Bayesian model of differential gene expression
My wife has been working on a project where she's looking at 3 time-points during a healing process and doing RNA-seq. I'm analyzing the known secreted proteins for differential expression, using a fully Bayesian model in Stan:
Fun, but non-trivial. There are around 6000 genes being considered, and there are 5 or 6 of these parameter vectors... 36000 parameters to be estimated from 60000 data points, and the parameters all have fairly long tails... cauchy priors and soforth. Then I need a couple hundred samples. so Stan is outputting something like 5-10 million numbers. Warmup has been a problem here, and the long tails have required re-parameterizations and tuning. Fun stuff.