A supervised Bayesian method for time (re)annotation of transcriptomics data

We propose a Bayesian method based on Gaussian process regression modeling to solve the problem. We use it to perform time annotation in old \textit{Clostridium botulinum} microarray experiments by utilising recently collected RNA-Seq time series. Additionally, we apply the method to time annotate RNA-Seq experiments and test its robustness toward noise on synthetically generated data. Time series experiments with wild type and various mutants