ArticleProceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics2018
Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data.
Article in Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics, 2018. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model.Statistics in medicine · 2026Article
- Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition.Biostatistics (Oxford, England) · 2025Article
- GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning.American journal of human genetics · 2024Article
- 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
- Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.Environment international · 2024Article
- GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.bioRxiv : the preprint server for biology · 2024Article
- Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors.Biometrics · 2024Article
- Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.Briefings in bioinformatics · 2023Article
- Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology.Frontiers in molecular biosciences · 2022Review
- An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients.Human genomics · 2021Article
- Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data.BMC bioinformatics · 2020Article
- GRIA: Graphical Regularization for Integrative Analysis.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2020Article
- Knowledge-Guided Biclustering via Sparse Variational EM Algorithm.10th IEEE International Conference on Big Knowledge : proceedings : 10-11 November 2019, Beijing, China. IEEE International Conference on Big Knowledge (10th : 2019 : Beijing, China) · 2019Article
- 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
3 authors.
Funding
Abstract
Integrative clustering is a clustering approach for multiple datasets, which provide different views of a common group of subjects. It enables analyzing multi-omics data jointly to, for example, identify the subtypes of diseases, cells, and so on, capturing the complex underlying biological processes more precisely. On the other hand, there has been a great deal of interest in incorporating the prior structural knowledge on the features into statistical analyses over the past decade. The knowledge on the gene regulatory network (pathways) can potentially be incorporated into many genomic studies. In this paper, we propose a novel integrative clustering method which can incorporate the prior graph knowledge. We first develop a generalized Bayesian factor analysis (GBFA) framework, a sparse Bayesian factor analysis which can take into account the graph information. Our GBFA framework employs the spike and slab lasso (SSL) prior to impose sparsity on the factor loadings and the Markov random field (MRF) prior to encourage smoothing over the adjacent factor loadings, which establishes a unified shrinkage adaptive to the loading size and the graph structure. Then, we use the framework to extend iCluster+, a factor analysis based integrative clustering approach. A novel variational EM algorithm is proposed to efficiently estimate the MAP estimator for the factor loadings. Extensive simulation studies and the application to the NCI60 cell line dataset demonstrate that the propose method is superior and delivers more biologically meaningful outcomes.
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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.