ArticleBiostatistics (Oxford, England)2020
Bayesian generalized biclustering analysis via adaptive structured shrinkage.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A clustering approach to integrative analyses of multiomic cancer data.Journal of applied statistics · 2025Article
- Knowledge-guided learning methods for integrative analysis of multi-omics data.Computational and structural biotechnology journal · 2024Review
- Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information.Statistics in biosciences · 2024Article
- Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.Cell reports methods · 2024Article
- Robust knowledge-guided biclustering for multi-omics data.Briefings in bioinformatics · 2023Article
- Integrative Learning of Structured High-Dimensional Data from Multiple Datasets.Statistical analysis and data mining · 2023Article
- Robust integrative biclustering for multi-view data.Statistical methods in medical research · 2022Article
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
- GRIA: Graphical Regularization for Integrative Analysis.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2020Article
- Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology.JCO precision oncology · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Biclustering techniques can identify local patterns of a data matrix by clustering feature space and sample space at the same time. Various biclustering methods have been proposed and successfully applied to analysis of gene expression data. While existing biclustering methods have many desirable features, most of them are developed for continuous data and few of them can efficiently handle -omics data of various types, for example, binomial data as in single nucleotide polymorphism data or negative binomial data as in RNA-seq data. In addition, none of existing methods can utilize biological information such as those from functional genomics or proteomics. Recent work has shown that incorporating biological information can improve variable selection and prediction performance in analyses such as linear regression and multivariate analysis. In this article, we propose a novel Bayesian biclustering method that can handle multiple data types including Gaussian, Binomial, and Negative Binomial. In addition, our method uses a Bayesian adaptive structured shrinkage prior that enables feature selection guided by existing biological information. Our simulation studies and application to multi-omics datasets demonstrate robust and superior performance of the proposed method, compared to other existing biclustering methods.
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Registered trials
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.