Li, ZuqiMelograna, FedericoHoskens, HanneDuroux, DianeMarazita, Mary L.Walsh, SusanWeinberg, Seth M.Shriver, Mark D.Müller-Myhsok, BertramClaes, PeterVan Steen, Kristel2024-01-032024-01-032023-05-05Li Z, Melograna F, Hoskens H, et al. netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity. Preprint. bioRxiv. 2023;2023.05.04.539350. Published 2023 May 5. doi:10.1101/2023.05.04.539350https://hdl.handle.net/1805/37583Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.en-USAttribution 4.0 InternationalMulti-view dataPrecision medicineSingle-view dataArticle