دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.
المؤلفون: Kilic Z; Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.; These authors contributed equally: Zeliha Kilic, Max Schweiger., Schweiger M; Center for Biological Physics, ASU, Tempe, AZ, USA.; Department of Physics, ASU, Tempe, AZ, USA.; These authors contributed equally: Zeliha Kilic, Max Schweiger., Moyer C; Center for Biological Physics, ASU, Tempe, AZ, USA.; School of Mathematics and Statistical Sciences, ASU, Tempe, AZ, USA., Shepherd D; Center for Biological Physics, ASU, Tempe, AZ, USA.; Department of Physics, ASU, Tempe, AZ, USA., Pressé S; Center for Biological Physics, ASU, Tempe, AZ, USA.; Department of Physics, ASU, Tempe, AZ, USA.; School of Molecular Sciences, ASU, Tempe, AZ, USA.
المصدر: Nature computational science [Nat Comput Sci] 2023 Feb; Vol. 3 (2), pp. 174-183. Date of Electronic Publication: 2023 Jan 19.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer Nature Country of Publication: United States NLM ID: 101775476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2662-8457 (Electronic) Linking ISSN: 26628457 NLM ISO Abbreviation: Nat Comput Sci Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة: Original Publication: [New York, N.Y.] : Springer Nature, [2021]-
مستخلص: 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 coli lacZ pathway and the Saccharomyces cerevisiae STL1 pathway, and verify its robustness on synthetic data.
Competing Interests: Competing interests The authors declare no competing interests.
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معلومات مُعتمدة: RF1 MH128867 United States MH NIMH NIH HHS; R01 GM130745 United States GM NIGMS NIH HHS; R01 HL068702 United States HL NHLBI NIH HHS; R01 GM134426 United States GM NIGMS NIH HHS; R35 GM148237 United States GM NIGMS NIH HHS
تواريخ الأحداث: Date Created: 20231221 Date Completed: 20231222 Latest Revision: 20240202
رمز التحديث: 20240202
مُعرف محوري في PubMed: PMC10732567
DOI: 10.1038/s43588-022-00392-0
PMID: 38125199
قاعدة البيانات: MEDLINE
الوصف
تدمد:2662-8457
DOI:10.1038/s43588-022-00392-0