Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
Open accessAbstract
Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called 'multi-dimensional genomic data'. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional 'omic' data.
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jNMF, PLS, and CCA are frequently employed in multi-omics integration tasks, aiding in the analysis of gene modules and exploring underlying biological mechanisms in the multi-omics latent space.
“These methods (jNMF, PLS, and CCA) are frequently employed in multi-omics integration tasks, aiding in the analysis of gene modules and exploring underlying biological mechanisms in the multi-omics latent space [42,46].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [46]
The Introduction and Materials and Methods describe joint NMF integrating multiple genomic data types in a common space to identify modules of genes, miRNAs and methylation markers. The paper does not establish the sentence’s claims about PLS or CCA, or that all three methods are frequently employed for these tasks. (LLM, conf=0.86)
Experimental — accuracy not yet measuredJoint NMF was proposed to integrate multi-omics data with non-negative values.
“Joint NMF (non-negative matrix factorisation) (Zhang et al., 2012) was proposed to integrate multi-omics data with non-negative values.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zhang et al., 2012
Joint NMF decomposes the joint matrix into loadings and factors, bringing different omics into a common basis matrix.
“It involved decomposing the joint matrix into loadings and factors, bringing the different omics into a common basis matrix.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zhang et al., 2012
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- Authors
- Zhang, S., Liu, C.-C., Li, W., Shen, H., Laird, P. W., & Zhou, X. J.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Zhou, Xianghong Jasmine
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-08-08
- Volume
- 40
- Issue
- 19
- Pages
- 9379-9391
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3479191
- OpenAlex
- W2117968640
- Cited by
- 374
- References
- 59
- Citations by year
- 2026: 7 · 2025: 23 · 2024: 20 · 2023: 25 · 2022: 46 · 2021: 59 · 2020: 33 · 2019: 43 · 2018: 23 · 2017: 24 · 2016: 22 · 2015: 16 · 2014: 15 · 2013: 14 · 2012: 3
- FWCI
- 6.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Cancer Institute (U24CA143882)
- National Institute of General Medical Sciences (R01GM074163)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- DNA methylation, gene expression, microRNA expression, ovarian cancer, tumor subtypes, pathway perturbation, integrative genomics, multi-omics integration
- MeSH terms
- Female, Humans, Neoplasms, Ovarian Neoplasms, Survival Analysis, DNA Methylation, Genomics, MicroRNAs, Transcriptome
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