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

ABSTRACT: 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 nois

SUBMITTER: Kilic Z 

PROVIDER: S-EPMC10732567 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature