Motivation: The rapid increase of high-throughput biomedical assays has improved patient phenotyping but also amplified analytic challenges. Heterogeneous study protocols, batch effects, large numbers of features, modality-specific distributions and frequent missing values make straightforward aggregation and interpretation of multi-omics cohorts difficult. These challenges complicate unsupervised tasks such as patient stratification, which remain critical for discovery of disease subtypes and actionable biomarkers. Thus, there is an acute need for computational methods that can integrate heterogeneous omics layers, handle modality-specific data characteristics, learn non-linear cross-modal structure, and produce outputs that are both useful and interpretable for domain experts. We present a framework that fuses heterogeneous gut microbiome multi-omics profile (metagenomics and metabolomics) for unsupervised patient stratification.
Results: Applied to cohorts composed of Crohn's disease, ulcerative colitis and healthy individuals, our approach yields coherent and well-separated patient clusters and outperforms traditional multi-omics integration based solely on graph fusion. The model identifies a set of discriminative features that drive stratification. Healthy clusters are distinguished by Alistipes shahii and deoxycholic acid, suggesting functional bile-acid and indole metabolism. In contrast, inflammatory clusters are defined by Ruminococcus gnavus and tryptophan accumulation, pointing to impaired anti-inflammatory processing. Additionally, the metabolite ADMA separates these subgroups through divergent associations with E. coli and F. prausnitzii. Statistical analyses and literature evidence support the biological relevance of these microbial-metabolic signatures. All together, this graph-based strategy provides a robust solution for integrating heterogenous multi-omics data from gut microbiome, highlighting molecular signature and potential actionable candidate biomarker for IBD medicine.

Co-authors: Sarah Dandou, Calliopée Desenfans, Samuel Deliens, Elena Maria Visibelli