Gene expression model inference from snapshot RNA data using Bayesian non-parametrics

Zeliha Kilic, Max Schweiger, Camille Moyer, Douglas Shepherd, Steve Pressé

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational frameworks do not perform simultaneous Bayesian inference of gene expression models and parameters from such data. Rather, gene expression models—composed of gene states, their connectivities and associated parameters—are currently deduced by pre-specifying gene state numbers and connectivity before learning associated rate parameters. Here we propose a method to learn full distributions over gene states, state connectivities and associated rate parameters, simultaneously and self-consistently from single-molecule RNA counts. We propagate noise from fluctuating RNA counts over models by treating models themselves as random variables. We achieve this within a Bayesian non-parametric paradigm. We demonstrate our method on the Escherichia colilacZ pathway and the Saccharomyces cerevisiaeSTL1 pathway, and verify its robustness on synthetic data.

Original languageEnglish (US)
Pages (from-to)174-183
Number of pages10
JournalNature Computational Science
Issue number2
StatePublished - Feb 2023

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Computer Networks and Communications


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