Multimodal probabilistic generative models for time-course gene expression data and Gene Ontology (GO) tags

• We propose generative models for gene expression profiles and GO tags.
• Our models cluster genes over the time-course, stages, or time-points.
• Biological stage prediction and stage boundary estimation are illustrated.
• GO tags provide prior information for clustering gene expression profiles.
• Stage prediction accuracies are higher using GO tags than otherwise.

We propose four probabilistic generative models for simultaneously modeling gene expression levels and Gene Ontology (GO) tags. Unlike previous approaches for using GO tags, the joint modeling framework allows the two sources of information to complement and reinforce each other. We fit our models to three time-course datasets collected to study biological processes, specifically blood vessel growth (angiogenesis) and mitotic cell cycles. The proposed models result in a joint clustering of genes and GO annotations. Different models group genes based on GO tags and their behavior over the entire time-course, within biological stages, or even individual time points. We show how such models can be used for biological stage boundary estimation de novo. We also evaluate our models on biological stage prediction accuracy of held out samples. Our results suggest that the models usually perform better when GO tag information is included.