ArticleProceedings of the National Academy of Sciences of the United States of America2020
A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 27 citations in OpenAlex.
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- Quantifying Transcriptome Turnover on Phylogenies by Modeling Gene Expression as a Binary Trait.Molecular biology and evolution · 2025Article
- Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm.Cell reports methods · 2024Article
- De Novo Genes.Annual review of genetics · 2024Review
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- The BulkECexplorer compiles endothelial bulk transcriptomes to predict functional versus leaky transcription.Nature cardiovascular research · 2024Article
- Optimized method for differential gene expression analysis in non-model species: Case ofMethodsX · 2023Article
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- Folding Features and Dynamics of 3D Genome Architecture in Plant Fungal Pathogens.Microbiology spectrum · 2022Article
- Natural variation in the maternal and zygotic mRNA complements of the early embryo in Drosophila melanogaster.BMC genomics · 2022Article
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- Molecular Evolution across Mouse Spermatogenesis.Molecular biology and evolution · 2022Article
- The circadian oscillator analysed at the single-transcript level.Molecular systems biology · 2021Article
- Article
- A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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4 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.