Abstract
Type 2 diabetes mellitus (T2D) remains a critical health concern, particularly in its early disease stages such as prediabetes. Understanding these early stages is paramount for improving patient outcomes. Multiomics data integration tools offer promise in unraveling the underlying mechanisms of T2D. The advent of high-throughput technology and the increasing availability of multiomics data has led to the development of several statistical and network-based integration methods. However, the performance of such methods varies, requiring their output evaluation in an unbiased manner. Here, we conducted a comparative analysis of three represented unsupervised multiomics integration tools, MOFA + , GFA, and ICluster alongside an in-house supervised model EMFR, using two complementary benchmarks. First, we assessed how well the features selected by each tool could discriminate between patient and control samples using both linear and nonlinear classification models. Second, we quantified how much each type of omics data-selected features contributed to the total variance. Through such detailed comparisons between the unsupervised, we observed that the features selected by MOFA + and GFA gave the best F1 score (0.7) in the nonlinear classification model, clearly discriminating between patient and control classes. Hence, we recommend these two unsupervised integration tools for feature selection purposes. Our comparative analyses were conducted on a real biological dataset to further study prediabetes patients. Such multiomics data enabled the detection of prediabetes subtypes and provided several clinical insights that will open a new gate toward the era of personalized medicine for diabetic disease.
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Data availability
The dataset supporting the findings of this study is available in the GitHub repository at (https://github.com/ahmedtariq/MultiOmic-Ensembled-Feature-Reduction) and was retrieved from the integrative human microbiome project 'iHMP' (https://portal.hmpdacc.org; T2D iHMP Google Cloud platform).
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We would like to thank reviewers for taking the effort and time to review the manuscript. We appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
Funding
The main author ME acknowledges funding by the “la Caixa” Foundation (ID 100010434), within the Doctoral INPhINIT Program LCF/BQ/D122/11940015. AA was partially supported by the Strategic Funding U-IDB/04423/2020 and UIDP/04423/2020 through national funds provided by the Fundação para a Ciência e a Tecnologia (FCT) and the European Regional Development Fund (ERDF) in the framework of the program PT2020, by the European Structural and Investment Funds (ESIF) through the Competitiveness and Internationalization Operational Program—COMPETE 2020 and by National Funds through the FCT under the projects PTDC/CTA-AMB/31774/2017 (POCI-01–0145-FEDER/031774/2017). ME, AT, MH and MS were funded via two DAAD grants (1) GED-PerMED Z57546888: German-Egyptian Dialog on Tackling Precision Medicine using Artificial Intelligence, and (2) Eg-CompBio Z57587968: Empowering computational biology and bioinformatics research in Egypt, both funded by the DAAD (German Academic Exchange Service) in Germany.
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Mohamed Emam: conceptualization, methodology, formal analysis, investigation, writing—original draft, data curation. Ahmed Tarek: formal analysis, writing—review and editing, methodology. Mohamed Soudy: formal analysis, writing—review and editing, methodology. Agostinho Antunes: conceptualization, supervision, funding acquisition, software, visualization, writing—review and editing. Mohamed El Hadid: project administration, funding acquisition, supervision, methodology, writing—review and editing. Mohamed Hamed: conceptualization, project administration, funding acquisition, supervision, methodology, review and editing.
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The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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The study is based on multiomics integration analysis, and the data were retrieved from publicly available databases.
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Emam, M., Tarek, A., Soudy, M. et al. Comparative evaluation of multiomics integration tools for the study of prediabetes: insights into the earliest stages of type 2 diabetes mellitus. Netw Model Anal Health Inform Bioinforma 13, 8 (2024). https://doi.org/10.1007/s13721-024-00442-9
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DOI: https://doi.org/10.1007/s13721-024-00442-9