SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration
Open accessAbstract
Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method of multi-omics integration called supervised deep generalized canonical correlation analysis (SDGCCA) for modeling correlation structures between nonlinear multi-omics manifolds that aims at improving the classification of phenotypes and revealing the biomarkers related to phenotypes. SDGCCA addresses the limitations of other canonical correlation analysis (CCA)-based models (such as deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data correlations between multiple ( ≥ 2) modalities. Although there are a few methods to learn nonlinear CCA projections for classifying phenotypes, they only consider two views. Methods extended to multiple views either do not perform classification or do not provide feature ranking. In contrast, SDGCCA is a nonlinear multi-view CCA projection method that performs classification and ranks features. When we applied SDGCCA in predicting patients with Alzheimer's disease (AD) and discrimination of early- and late-stage cancers, it outperformed other CCA-based and other supervised methods. In addition, we demonstrate that SDGCCA can be applied for feature selection to identify important multi-omics biomarkers. On applying AD data, SDGCCA identified clusters of genes in multi-omics data, well known to be associated with AD.
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Frameworks like SDGCCA capture non-linear correlations across omics modalities, facilitating the discovery of robust cross-layer biomarkers.
“Furthermore, frameworks like SDGCCA capture non-linear correlations across omics modalities, facilitating the discovery of robust cross-layer biomarkers288.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [288]
Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
Novel methods improve prediction of species’ distributions from occurrence data
Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., et al. (2006) · Ecography · doi:10.1111/j.2006.0906-7590.04596.x · Cited by 9,088 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.-H., Pagès, F., Trajanoski, Z., & Galon, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp101 · Cited by 6,280 · see citing works
A Tutorial on the Cross-Entropy Method
Boer, P., Kroese, D. P., Mannor, S., & Rubinstein, R. (2005) · Annals of Operations Research · doi:10.1007/s10479-005-5724-z · Cited by 3,348 · see citing works
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440 · see citing works
Deep Canonical Correlation Analysis
Andrew, G., Arora, R., Bilmes, J., & Livescu, K. (2013) · Cited by 1,603 · see citing works
Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works
DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M. A., Tebbutt, S. J., & Cao, K. L. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1054 · Cited by 1,076 · see citing works
Chicco, D., Tötsch, N., & Jurman, G. (2021) · BioData Mining · doi:10.1186/s13040-021-00244-z · Cited by 893 · see citing works
Canonical analysis of several sets of variables
Kettenring, J. R. (1971) · Biometrika · doi:10.1093/biomet/58.3.433 · Cited by 798 · see citing works
Wang, T., Shao, W., Huang, Z., Tang, H., Zhang, J., Ding, Z., & Huang, K. (2021) · Nature Communications · doi:10.1038/s41467-021-23774-w · Cited by 671 · see citing works
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
Huang, S., Chaudhary, K., & Garmire, L. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00084 · Cited by 637 · see citing works
Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer’s disease
Whelan, C. D., Mattsson, N., Nagle, M. W., Vijayaraghavan, S., Hyde, C., Janelidze, S., Stomrud, E., Lee, J., Fitz, L., Samad, T. A., et al. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0795-2 · Cited by 262 · see citing works
Variable selection for generalized canonical correlation analysis
Tenenhaus, A., Philippe, C., Guillemot, V., Cao, K. L., Grill, J., & Frouin, V. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu001 · Cited by 227 · see citing works
Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data
Ma, B., Meng, F., Ge, Y., Yan, H., Chai, B., & Song, F. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103761 · Cited by 221 · see citing works
Olfactory dysfunction in Alzheimer’s disease
Zou, Y., Lü, D., Liu, L., Zhang, H., & Zhou, Y. (2016) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s104886 · Cited by 185 · see citing works
Interleukin-10 and interleukin-6 gene polymorphisms as risk factors for Alzheimer’s disease
Arosio, B., Trabattoni, D., Galimberti, L., Bucciarelli, P., Fasano, F. R., Calabresi, C., Cazzullo, C. L., Vergani, C., Annoni, G., & Clerici, M. (2003) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2003.10.009 · Cited by 143 · see citing works
Amyloid-β Toxicity and Tau Hyperphosphorylation are Linked Via RCAN1 in Alzheimer's Disease
Lloret, A., Badía, M.-C., Giraldo, E., Ermak, G., Alonso, M.-D., Pallardó, F. V., Davies, K. J. A., & Viña, J. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-110890 · Cited by 133 · see citing works
From cellular senescence to Alzheimer’s disease: The role of telomere shortening
Boccardi, V., Pelini, L., Ercolani, S., Ruggiero, C., & Mecocci, P. (2015) · Ageing Research Reviews · doi:10.1016/j.arr.2015.04.003 · Cited by 121 · see citing works
Cellular Senescence and Iron Dyshomeostasis in Alzheimer’s Disease
Masaldan, S., Belaidi, A. A., Ayton, S., & Bush, A. I. (2019) · Pharmaceuticals · doi:10.3390/ph12020093 · Cited by 103 · see citing works
MicroRNAs, Aging, Cellular Senescence, and Alzheimer's Disease
Reddy, P. H., Williams, J. M., Smith, F., Bhatti, J. S., Kumar, S., Vijayan, M., Kandimalla, R. J. L., Kuruva, C. S., Wang, R., Mańczak, M., et al. (2017) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2016.12.009 · Cited by 87 · see citing works
Caspase-8, association with Alzheimer’s Disease and functional analysis of rare variants
Rehker, J., Rodhe, J., Nesbitt, R., Boyle, E. A., Martin, B., Lord, J., Karaca, I., Naj, A. C., Jessen, F., Helisalmi, S., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185777 · Cited by 47 · see citing works
A whitening approach to probabilistic canonical correlation analysis for omics data integration
Jendoubi, T., & Strimmer, K. (2019) · BMC Bioinformatics · doi:10.1186/s12859-018-2572-9 · Cited by 47 · see citing works
Distribution, levels and phosphorylation of Raf‐1 in Alzheimer's disease
Mei, M., Su, B., Harrison, K. H., Chao, M. P., Siedlak, S., Previll, L. A., Jackson, L., Cai, D., & Zhu, X.-W. (2006) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2006.04174.x · Cited by 34 · see citing works
FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data
Mandal, A., & Maji, P. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2685625 · Cited by 31 · see citing works
SMSPL: Robust Multimodal Approach to Integrative Analysis of Multiomics Data
Yang, Z., Wu, N., Liang, Y. Q., Zhang, H., & Ren, Y. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3006240 · Cited by 23 · see citing works
Signal Transduction Mechanisms in Alzheimer Disease
Shimohama, S., & Matsushima, H. (1995) · Alzheimer Disease & Associated Disorders · doi:10.1097/00002093-199501002-00004 · Cited by 14 · see citing works
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Baião, A. R., Cai, Z., Poulos, R. C., Robinson, P. J., Reddel, R. R., Zhong, Q., Vinga, S., & Gonçalves, E. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf355 · Cited by 249 · see citing works
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
MUMA: A Multi-Omics Meta-Learning Algorithm for Data Interpretation and Classification
Huang, H., Shu, J., & Liang, Y. Q. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3363081 · Cited by 40 · see citing works
DriverDBv4: a multi-omics integration database for cancer driver gene research
Liu, C.-H., Lai, Y., Shen, P.-C., Liu, H.-C., Tsai, M.-H., Wang, Y.-D., Lin, W.-J., Chen, F.-H., Li, C.-Y., Wang, S.-C., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1060 · Cited by 34 · see citing works
Muneer, A., Waqas, M. M., Saad, M. B., Showkatian, E., Bandyopadhyay, R., Xu, H. M., Li, W., Chang, J., Liao, Z., Haymaker, C., et al. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11522-9 · Cited by 21 · see citing works
Zhou, R., Zhou, H., Chen, B. Y., Shen, L., Zhang, Y., & He, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43895-0_64 · Cited by 16 · see citing works
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Sheng, X., Cai, H., Nie, Y., He, S., Cheung, Y., & Chen, J. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3439530 · Cited by 9 · see citing works
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Wróbel, S., Turek, C., Stępień, E. Ł., & Piwowar, M. W. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104575 · Cited by 8 · see citing works
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Liu, W. M., Vu, T., Konigsberg, I. R., Pratte, K., Zhuang, Y., & Kechris, K. J. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05900-9 · Cited by 6 · see citing works
MMMF: Multimodal Multitask Matrix Factorization for Classification and Feature Selection
Hwang, J., & Lee, H. (2022) · IEEE Access · doi:10.1109/access.2022.3221089 · Cited by 5 · see citing works
Self-citationApplication of Mass Cytometry Platforms to Solid Organ Transplantation
Zhang, W., Sen, A., Peña, J. K., Reitsma, A. J., Alexander, O. C., Tajima, T., Martinez, O. M., & Krams, S. M. (2024) · Transplantation · doi:10.1097/tp.0000000000004925 · Cited by 4 · see citing works
Yang, R., Kong, W., Liu, K., Wen, G., & Yu, Y. (2024) · Journal of Molecular Neuroscience · doi:10.1007/s12031-024-02190-x · Cited by 4 · see citing works
NMFProfiler: a multi-omics integration method for samples stratified in groups
Mercadié, A., Gravier, É., Josse, G., Fournier, I., Viodé, C., Vialaneix, N., & Brouard, C. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf066 · Cited by 4 · see citing works
Deep metric loss for multimodal learning
Moon, S., & Lee, H. (2025) · Machine Learning · doi:10.1007/s10994-024-06709-6 · Cited by 3 · see citing works
Self-citationDisease Dynamics: Insights from Microbiome and Multi-Omics Analysis
Mishra, T., Tiwari, P. B., Rezaei, A. R., Mallik, B., Kanchan, S., & Kesheri, M. (2025) · Springer eBooks · doi:10.1007/978-981-96-7067-3_3 · Cited by 2 · see citing works
Alotaibi, R. A., Alshahrani, S. S., Alotaibi, H. F., Alsaleh, A. K., Alharbi, A. K., Alharthi, R. T., Alshahrani, Y. S., Alomar, E. S., Mohaini, M. A., Mansour, M. A., et al. (2025) · Journal of Advanced Trends in Medical Research · doi:10.4103/atmr.atmr_24_25 · Cited by 2 · see citing works
Editorial: Interactions at the viral-host nexus in animals: from omics insights to immune modulation
Qu, S., Munir, M., Qian, J., He, J., Wang, J., & Xu, X. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1667802 · Cited by 2 · see citing works
Saito, N., Maeda, K., Ogawa, T., Asamizu, S., & Haseyama, M. (2023) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2023.p1321 · Cited by 1 · see citing works
Mass Spectrometry Based Multi‐Omics Integration: Analytical Methods and Biological Applications
Battineni, J. K., Mamidisetti, Y. D., Kuchukuntla, M., Amaravadi, D., Ravali, R. S., & Jadi, R. K. (2026) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.70097 · Cited by 1 · see citing works
Zhang, Y., Cao, X., Yang, L. W., Chen, J., & Hwarari, D. (2026) · Plants · doi:10.3390/plants15142185 · Cited by 1 · see citing works
Baruri, S., Batra, L., Adhikari, S., & El-Baz, A. (2026) · Epigenomes · doi:10.3390/epigenomes10030053
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Maeda, K., Matsumoto, M., Saito, N., Ogawa, T., & Haseyama, M. (2024) · IEEE Access · doi:10.1109/access.2024.3437328 · Cited by 1 · see citing works
Chen, X., Xie, C., Zhang, Y., He, S., Bo, X., & Zhang, Z. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385631
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Wang, Y., Peng, W., Zhang, Y., Adeli, E., Zhao, Q., & Pohl, K. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78761-4_3
Lert-itthiporn, W. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1011785
Oh, S., Kwon, J., Kim, J., Kim, S., Ra, S., & Park, H. (2026) · IEEE International Symposium on Biomedical Imaging · doi:10.1109/isbi61048.2026.11515455
Huang, M., Hu, H., Li, M. F., Zhang, J., Zhang, H., Ye, X., Ma, T., Sun, H., Ye, L., & Hou, T. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00494
- Authors
- Moon, S., Hwang, J., & Lee, H.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- Corresponding author
- Lee, Hyunju
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 2022-08-01
- Volume
- 29
- Issue
- 8
- Pages
- 892-907
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- PMC
- PMC9805883
- OpenAlex
- W4224300102
- Cited by
- 28
- References
- 29
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 7 · 2023: 5 · 2022: 1
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Computational Biology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (R01AG017917, R01AG030146, R01AG036836, R01AG036042, R01AG015819, P30AG010161)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- multi-omics integration, nonlinear canonical correlation analysis, phenotype classification, biomarker discovery, feature ranking, feature selection, Alzheimer's disease, gene clustering
- MeSH terms
- Canonical Correlation Analysis, Alzheimer Disease, Humans
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