Multi-omics integration in allergy, viral infections and asthma: a comprehensive analysis of bioinformatics tools.

Introduction: Allergic and inflammatory respiratory conditions are complex and heterogeneous diseases affecting a large portion of the population. Although omics technologies have enabled the identification of potential biomarkers, single-omics-based approaches often fail to capture the full complexity of these disorders. Multi-omics integration provides a more comprehensive perspective; however, selecting the most suitable tool remains challenging due to the wide range of available tools and the variability of biological datasets. In this study, we compare different integration approaches for these diseases, evaluating their performance and functionality to ensure a robust and objective assessment across heterogeneous datasets. Methods: We compared multiple multi-omics integration tools across four heterogeneous respiratory disease datasets. Unsupervised methods were applied following dataset-specific preprocessing, including filtering, imputation, and normalization. Integration was performed across matched metabolomics, proteomics, and transcriptomics layers. Performance was evaluated using the Adjusted Rand Index (ARI). Results: Unsupervised methods showed variable performance across datasets. NEMO consistently achieved the most accurate clustering of clinical phenotypes, whereas MOFA and intNMF performed well in specific contexts. Conclusion: Our findings indicate that NEMO, intNMF and MOFA are promising tools for multi-omics integration in allergic and respiratory diseases, as they effectively capture complementary information across different omics layers and facilitate the identification of coordinated molecular signatures associated with disease phenotypes. Overall, these results emphasize the importance of selecting integration methods based on the study goals and the dataset characteristics.