Bayesian method for time annotation of transcriptomics experiments
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We propose a Bayesian approach based on Gaussian process regression modeling to address this challenge. We employ this method to perform time annotation in legacy Clostridium botulinum microarray experiments, which were initially annotated based on growth phases, utilizing recently collected RNA-Seq time series data comprising multiple replicates as reference. We also test the performance of the method on RNA-Seq data by using the experiments collected on even time points as the training set and the rest as the validation set. Furthermore, we assess the method’s robustness to measurement errors by applying it to synthetically generated data with varying levels of noise.