Integrative Analysis of Genome Data Using Deep Embedded Clustering to Identify Population Stratification and Functional Gene Modules

Öz

Abstract Introduction: Next-Generation Sequencing (NGS) data analysis faces computational challenges due to high dimensionality. Traditional clustering methods fail to capture biological complexity in human genetic variation. This study introduces Deep Embedded Clustering (DEC) for genomic pattern discovery. Methods: This study suggests a comprehensive DEC framework applied to 1000 Genomes Project NGS data. Performance was evaluated against conventional clustering algorithms using Adjusted Rand Index (ARI) across varying cluster configurations. Pathway enrichment analysis assessed biological relevance. Results: DEC achieved highly efficient recovery of population structure (ARI=0.892±0.015) with robust performance across cluster numbers. Identified clusters showed significant enrichment for population-specific adaptations: lactase persistence (FDR=1.1×10⁻¹⁰), alcohol metabolism (FDR=2.3×10⁻⁷), and malaria resistance (FDR=3.4×10⁻¹²). Discussion: DEC improves conventional clustering by integrating dimensionality reduction with cluster assignment, revealing biologically meaningful patterns missed by traditional methods. This bridges computational methodology with functional genomics interpretation, though validation in diverse cohorts is warranted. Conclusion: DEC offers an unsupervised learning framework for genomics, enabling biologically meaningful pattern discovery beyond statistical clustering. Our reproducible pipeline provides a foundation for functional module identification in complex genomic datasets.

Anahtar Kelimeler

• Deep Learning
• Next-Generation Sequencing
• Clustering
• Deep Embedded Clustering
• Functional Genomics

Kaynakça

1. References 1. Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., ... & Das, G., Next-Generation Sequencing Technology: Current Trends and Advancements, 2023, Biology 2023, 12, 997. doi: https://doi.org/10.3390/ biology12070997
2. 2. 1000 Genomes Project Consortium, A global reference for human genetic variation, 2015, Nature, 526(7571), 68. doi: 10.1038/nature15393
3. 3. Ezugwu, A.E.; Ikotun, A.M.; Oyelade, O.O.; Abualigah, L.; Agushaka, J.O.; Eke, C.I.; Akinyelu, A.A. A Comprehensive Survey of Clustering Algorithms: State-of-the-Art Machine Learning Applications, Taxonomy, Challenges, and Future Research Prospects. Eng. Appl. Artif. Intell., 2022, 110, 104743. https://doi.org/10.1016/j.engappai.2022.104743
4. 4. Karim, M.R.; Khan, M.A.; Rahaman, M.M.; et al. Deep Learning-Based Clustering Approaches for Bioinformatics. Brief. Bioinform., 2021, 22(1), 393–407. https://doi.org/10.1093/bib/bbz170
5. 5. Wang, J., Zou, Q., & Lin, C., A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data, 2022, Briefings in Bioinformatics, 23(1), bbab345. https://doi.org/10.1093/bib/bbab345
6. 6. Arias, P.M.; Soto, D.; Cardenas, J. DeLUCS: Deep Learning for Unsupervised Clustering of DNA Sequences. PLoS ONE, 2022, 17(1), e0261531. https://doi.org/10.1371/journal.pone.0261531
7. 7. Alipour, F. Advanced Machine Learning Techniques for Taxonomic Classification and Clustering of DNA Sequences. Univ. Waterloo Repos., 2025. https://uwspace.uwaterloo.ca. Accessed: 17.06.2025
8. 8. Ren, Y.; Pu, J.; Yang, Z.; et al. Deep Clustering: A Comprehensive Survey. IEEE Trans. Neural Netw. Learn. Syst., 2024, 36(4), 5858–5878. doi: 10.1109/TNNLS.2024.3403155

9. 9. Xie, J.; Girshick, R.; Farhadi, A. Unsupervised Deep Embedding for Clustering Analysis. In Proc. Int. Conf. Mach. Learn., 2016, 478–487. PMLR 48:478-487, 2016.
10. 10. Van der Auwera, G.A.; O'Connor, B.D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra; O’Reilly Media: 2020. Accessed: 19.06.2025
11. 11. Pilalis, E.; Zisis, D.; Andrinopoulou, C.; et al. Genome-Wide Functional Annotation of Variants: A Systematic Review of State-of-the-Art Tools, Techniques and Resources. Front. Pharmacol., 2025, 16, 1474026. https://doi.org/10.3389/fphar.2025.1474026.
12. 12. Kolberg, L.; Raudvere, U.; Kuzmin, I.; et al. g:Profiler—Interoperable Web Service for Functional Enrichment Analysis and Gene Identifier Mapping (2023 Update). Nucleic Acids Res., 2023, 51(W1), W207–W213. https://doi.org/10.1093/nar/gkad347
13. 13. Sartori, F.; Codicè, F.; Caranzano, I.; Rollo, C.; Birolo, G. A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research. Genes, 2025, 16(6), 648. https://doi.org/10.3390/genes16060648
14. 14. Sarumi, O.A., Leung, C.K., Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction. In: Mehta, M., Fournier-Viger, P., Patel, M., Lin, J.CW. (eds) Tracking and Preventing Diseases with Artificial Intelligence. Intelligent Systems Reference Library,2022, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-030-76732-7_5
15. 15. Mohamadi, Z.; Abtahi, E.; Shayegh, Z.; Kachouei, M.A. Support Vector Machine-Based Models for Cancer Detection Using Multi-Omic Data: A Systematic Review. bioRxiv, 2025. https://doi.org/10.1101/2025.07.10.664049
16. 16. Zou, D.; Korkalainen, H. Novel Technologies in the Diagnosis and Management of Sleep-Disordered Breathing: Insights from Genome-Wide Studies. Front. Sleep, 2025, 6, 1696478. doi:10.3389/frsle.2025.1696478
17. 17. Ziyi, M. (2024). Scalable and robust deep-learning methods power evolutionary-genetic studies of biobank-scale population genomic data (Doctoral dissertation, Cold Spring Harbor Laboratory). [https://repository.cshl.edu/id/eprint/41642/]. Accessed: 21.05.2025
18. 18. Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M.,. Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges, 2023, Applied Sciences, 13(12), 7082. https://doi.org/10.3390/app13127082
19. 19. Polu, O.R. AI-Driven Clustering of Medical Genomics Data for Disease Discovery. Res. Gate, 2024. doi :10.63397/ISCSITR-IJCSE_05_01_003
20. 20. Wu, D.; Zhao, J.; Wang, Y.; et al. Machine Learning in Genetics and Genomics. In: Mach. Learn. Healthc. Informatics, Springer, 2021. https://doi.org/10.1007/978-3-030-71881-7_13
21. 21. Meuwissen, T. H., Sonesson, A. K., Gebregiwergis, G., & Woolliams, J. A. (2020). Management of genetic diversity in the era of genomics. Frontiers in genetics, 11, 880. doi: https://doi.org/10.3389/fgene.2020.00880
22. 22. Novakovsky, G., Dexter, N., Libbrecht, M.W. et al. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet 24, 125–137 (2023). https://doi.org/10.1038/s41576-022-00532-2
23. 23. Valatkaitė, Augustė. Interpretable deep learning methods for tissue classification in histopathology images. Diss. Vilniaus universitetas., 2024. [https://epublications.vu.lt/object/elaba:192057693/]. Accessed: 29.05.20225
24. 24. R. J. Chen et al., "Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis," in IEEE Transactions on Medical Imaging, vol. 41, no. 4, pp. 757-770, April 2022, doi: 10.1109/TMI.2020.3021387.
25. 25. Popejoy, A., Fullerton, S. Genomics is failing on diversity. Nature 538, 161–164 (2016). https://doi.org/10.1038/538161a