ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2016
Bayesian hierarchical modelling for inferring genetic interactions in yeast.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Practical Consequences of the Bias in the Laplace Approximation to Marginal Likelihood for Hierarchical Models.Entropy (Basel, Switzerland) · 2025Article
- Hierarchical Bayesian models of transcriptional and translational regulation processes with delays.Bioinformatics (Oxford, England) · 2021Article
- A Bayesian non-parametric mixed-effects model of microbial growth curves.PLoS computational biology · 2020Article
- Design considerations and analysis planning of a phase 2a proof of concept study in rheumatoid arthritis in the presence of possible non-monotonicity.BMC medical research methodology · 2017Article
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Authors and funding
4 authors.
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Abstract
Quantitative fitness analysis (QFA) is a high throughput experimental and computational methodology for measuring the growth of microbial populations. QFA screens can be used to compare the health of cell populations with and without a mutation in a query gene to infer genetic interaction strengths genomewide, examining thousands of separate genotypes. We introduce Bayesian hierarchical models of population growth rates and genetic interactions that better reflect QFA experimental design than current approaches. Our new approach models population dynamics and genetic interaction simultaneously, thereby avoiding passing information between models via a univariate fitness summary. Matching experimental structure more closely, Bayesian hierarchical approaches use data more efficiently and find new evidence for genes which interact with yeast telomeres within a published data set.
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