TY - JOUR
T1 - Gene expression model inference from snapshot RNA data using Bayesian non-parametrics
AU - Kilic, Zeliha
AU - Schweiger, Max
AU - Moyer, Camille
AU - Shepherd, Douglas
AU - Pressé, Steve
N1 - Funding Information:
We thank I. Golding for providing the experimental data analyzed herein. We thank I. Sgouralis, Z. Fox and B. Munsky for interesting discussions and insights. D.S. acknowledges support from the NIH NHLBI (R01HL068702) and NIH BRAIN (RF1MH128867), and S.P. acknowledges support from NIH NIGMS (R01GM130745) and NIH NIGMS (R01GM134426).
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
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U2 - 10.1038/s43588-022-00392-0
DO - 10.1038/s43588-022-00392-0
M3 - Article
AN - SCOPUS:85146581647
SN - 2662-8457
VL - 3
SP - 174
EP - 183
JO - Nature Computational Science
JF - Nature Computational Science
IS - 2
ER -