netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Blog Article

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 on these network representations.

Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata flex 4 heartworm test and showed how it could be used for a refined obesity characterization.Benchmark analysis of netMUG on synthetic data read more with known strata of individuals indicated 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.