Abstract
We propose a hierarchical Bayesian model to infer RNA synthesis, processing, and degradation rates from time-course RNA sequencing data, based on an ordinary differential equation system that models the RNA life cycle. We parametrize the latent kinetic rates, which rule the system, with a novel functional form and estimate their parameters through three Dirichlet process mixture models. Owing to the complexity of this approach, we are able to simultaneously perform inference, clustering, and model selection. We apply our method to investigate transcriptional and post-transcriptional responses of murine fibroblasts to the activation of the proto-oncogene Myc. Our approach uncovers simultaneous regulations of the rates, which had been largely missed in previous analyses of this biological system.
Funding Statement
The work of GM has been partially carried out within the FAIR—Future Artificial Intelligence Research Foundation and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)—MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3—D.D.1555 11/10/2022, PE00000013).
EB has received support under the National Plan for Complementary Investments to the NRRP, project “D34H—Digital Driven Diagnostics, prognostics and therapeutics for sustainable Health care” (project code: PNC0000001), Spoke 4, funded by the Italian Ministry of University and Research.
The work of MF has been supported by the Giorgio Boglio fellowship from the Italian Association for Cancer Research (AIRC-ID 26611).
Acknowledgments
The authors would like to thank Mattia Pelizzola (IIT and UNIMIB) for his comments that have greatly improved the manuscript.
Citation
Gianluca Mastrantonio. Enrico Bibbona. Mattia Furlan. "Multiple latent clustering model for the inference of RNA life-cycle kinetic rates from sequencing data." Ann. Appl. Stat. 18 (4) 3467 - 3485, December 2024. https://doi.org/10.1214/24-AOAS1945
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