Integrating multi-omics data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study
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Motivation: Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analysis (CCA) approach is a promising one that seeks to penalize the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets. Results: Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al., penalized matrix decomposition CCA proposed by Witten and Tibshirani and its extension proposed by Suo et al. The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets. Availability and implementation: https://github.com/theorod93/sCCA. Supplementary information: Supplementary data are available at Bioinformatics online.
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Shallow learning methods including jNMF, PLS, CCA, and MKL have been developed for multimodal representation learning.
“In the early stages of multimodal representation learning, numerous shallow learning methods were developed and utilized. Notable approaches include joint non-negative matrix factorization (jNMF) methods [42], partial least square (PLS) [43], canonical correlation analysis (CCA) [44], and multiple kernel learning (MKL) [45].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [44]
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- Authors
- Rodosthenous, T., Shahrezaei, V., & Evangelou, M.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Rodosthenous, Theodoulos
- Evangelou, Marina
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-21
- Volume
- 36
- Issue
- 17
- Pages
- 4616-4625
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7750936
- OpenAlex
- W3027052013
- Cited by
- 67
- References
- 43
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 14 · 2023: 13 · 2022: 10 · 2021: 4 · 2020: 1
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc)
- Funders
- Engineering and Physical Sciences Research Council (EP/K503381/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- sparse canonical correlation analysis, multi-omics integration, high-dimensional data, penalty method
- MeSH terms
- Algorithms, Humans, Phenotype, Multivariate Analysis, Multifactorial Inheritance
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