Introduction: Multi-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.Methods: 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 better waters xl7000 on these network representations.
Results: 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 vibrating table for chocolate its superior performance compared with both baseline and benchmark methods for multi-view clustering.The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels.In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups.
Discussion: 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.