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

Transcriptomics experiments are often conducted to capture changes in gene expression over time. However, time annotations may be missing, imprecise, or not reflect the same physiological state of the bacterial culture between different experiments. Assigning accurate time points to these experiments using a reference time course is therefore crucial for identifying differentially expressed genes, and understanding gene regulatory networks for elucidating the studied organism's physiology and life cycle. This important task, which could enhance the biological interpretation of the transcriptomics experiments, has not been previously addressed. In this work, we propose a novel method to solve the challenge of realigning transcriptomics experiments based on a reference time course. Our method is based on a Bayesian approach that uses Gaussian process regression modeling. We show a use case of applying our method for assigning time annotations in legacy microarray samples of the bacterium Clostridium botulinum, which were solely annotated based on the growth phase at the time when the culture aliquots were sampled, utilizing recently collected RNA-Seq time series data comprising multiple replicates as a reference. The method significantly improved the description of the growth phases of the microarray data compared to the original annotations by clearly delineating the microarray samples belonging to different growth phases, as demonstrated by principal component analysis. Consequently, a larger number of differentially expressed genes was detected when comparing experiments belonging to successive growth phases. We compare this innovative approach with a baseline method that uses k-nearest neighbor algorithm and show that our method offers a higher resolution in the description of the data by exposing smaller time changes between samples. We also test the performance of the method on sparse RNA-Seq time series (i.e. sampled every second hour). All the predictions for the samples were within a 30-min margin of their true time.