A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.
Bayesian mixture models
gene expression
transcriptomics
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
11 08 2020
11 08 2020
Historique:
pubmed:
28
7
2020
medline:
21
10
2020
entrez:
26
7
2020
Statut:
ppublish
Résumé
Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets-where we demonstrate its ability to correctly infer true (known) expression states-and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.
Identifiants
pubmed: 32709743
pii: 1919748117
doi: 10.1073/pnas.1919748117
pmc: PMC7431084
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
19339-19346Subventions
Organisme : NIGMS NIH HHS
ID : F32 GM125107
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM122592
Pays : United States
Informations de copyright
Copyright © 2020 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
The authors declare no competing interest.
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