Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis
Open accessAbstract
Motivation: The 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. Methods: We 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. Results: We 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 Contact: shenr@mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online.
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Numerous research efforts are focused on utilizing genetic data and artificial intelligence algorithms to develop diagnostic models to enhance the clinical efficiency and accuracy of breast cancer diagnosis.
“Currently, numerous research efforts are focused on utilizing genetic data and artificial intelligence algorithms to develop diagnostic models to enhance the clinical efficiency and accuracy of breast cancer diagnosis [109,110,111].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [110]
iCluster uses principles similar to NMF but allows integration of datasets with negative values.
“Similarly, Shen (Shen et al., 2009) proposed iCluster framework which used principles similar to NMF but allows integration of datasets having negative values.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Shen et al., 2009
iCluster was demonstrated using copy number, mRNA expression and methylation data for cancer subtype discovery in glioblastoma.
“They showed the functioning of the framework by using copy number, mRNA expression and methylation data to conduct a cancer subtype discovery in glioblastoma.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Shen et al., 2009
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- Authors
- Shen, R., Olshen, A. B., & Ladanyi, M.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Shen, Ronglai
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-09-16
- Volume
- 25
- Issue
- 22
- Pages
- 2906-2912
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W4252676770
- Cited by
- 849
- References
- 18
- Citations by year
- 2026: 44 · 2025: 65 · 2024: 70 · 2023: 82 · 2022: 98 · 2021: 124 · 2020: 71 · 2019: 78 · 2018: 55 · 2017: 49 · 2016: 32 · 2015: 26 · 2014: 19 · 2013: 16 · 2012: 12
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Funders
- National Cancer Institute (P30CA008748)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- integrative clustering, multi-omics integration, breast cancer subtypes, lung cancer subtypes, copy number alterations, gene expression, cancer subtyping, dimensionality reduction, expectation-maximization algorithm
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