ArticlePLoS computational biology2016
Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo.Nature neuroscience · 2026Article
- Functional implications of polygenic risk for schizophrenia in human neurons.Nature communications · 2026Article
- Uncovering coordinated pathway interactions through gene co-differential expression in yeast.Nucleic acids research · 2026Article
- Dynamic convergence of neurodevelopmental disorder risk genes across neurodevelopment.bioRxiv : the preprint server for biology · 2025Article
- Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis.Statistics and computing · 2025Article
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- A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes.Biostatistics (Oxford, England) · 2024Article
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- A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.Statistics in medicine · 2023Article
- Dual ICA to extract interacting sets of genes and conditions from transcriptomic data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers.Nature communications · 2023Article
- Robust integrative biclustering for multi-view data.Statistical methods in medical research · 2022Article
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- Identification of condition-specific regulatory mechanisms in normal and cancerous human lung tissue.BMC genomics · 2022Article
- Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Comparison of sparse biclustering algorithms for gene expression datasets.Briefings in bioinformatics · 2021Article
- Empirical Bayes Matrix Factorization.Journal of machine learning research : JMLR · 2021Article
- Causal network inference from gene transcriptional time-series response to glucocorticoids.PLoS computational biology · 2021Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
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5 authors.
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Abstract
Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.
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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.