ArticleBioinformatics (Oxford, England)2009
Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 446 papers, 1 of them a synthesis that pooled it.
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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
446 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
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- Semi-supervised Omics Factor Analysis (SOFA) disentangles known and latent sources of variation in multi-omic data.Nature communications · 2026Article
- Joint-RPCA: domain-aware multi-omics integration for systems microbiology.Molecular systems biology · 2026Article
- Livestock immunogenomics based on omics integration: from infection and stress to xenotransplantation.Journal of animal science and technology · 2026Review
- Multivariate Random Forests for Cross-Modal Multi-Omics Integration.bioRxiv : the preprint server for biology · 2026Article
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- Joint clinical and molecular subtyping of COPD with variational autoencoders.Nature communications · 2026Article
- Review
- Local graph estimation with pathwise false discovery control.Nature communications · 2026Article
- Epigenetic biomarkers in inflammatory bowel diseases-computational challenges and opportunities.Journal of Crohn's & colitis · 2026Review
- Retroelement Hypomethylation Links Hypoxia Signaling, Immune Phenotypes, and Survival in Clear Cell Renal Cell Carcinoma.bioRxiv : the preprint server for biology · 2026Article
- Multi Omics Integration in Colorectal Cancer: From Molecular Insights to Precision Oncology.Cancers · 2026Review
- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- MS-ConTab: multi-scale contrastive learning of mutation signatures for Pan-Cancer representation and stratification.Bioinformatics (Oxford, England) · 2026Article
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model.Statistics in medicine · 2026Article
- Understanding ischemia in children with tuberculous meningitis (iThemba): a protocol paper.BMC pediatrics · 2026Article
- Mapping the Analytical Landscape of Gene-Diet Interactions in Epidemiology: From Classical Models to Causal and Multi-Omics Frameworks.Nutrients · 2026Review
386 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
motivationThe molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment.
methodsWe developed a joint latent variable model for integrative clustering. We call the resulting methodology iCluster. iCluster incorporates flexible modeling of the associations between different data types and the variance-covariance structure within data types in a single framework, while simultaneously reducing the dimensionality of the datasets. Likelihood-based inference is obtained through the Expectation-Maximization algorithm.
resultsWe demonstrate the iCluster algorithm using two examples of joint analysis of copy number and gene expression data, one from breast cancer and one from lung cancer. In both cases, we identified subtypes characterized by concordant DNA copy number changes and gene expression as well as unique profiles specific to one or the other in a completely automated fashion. In addition, the algorithm discovers potentially novel subtypes by combining weak yet consistent alteration patterns across data types. AVAILABILITY: R code to implement iCluster can be downloaded at http://www.mskcc.org/mskcc/html/85130.cfm
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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.