AUTHOR=Li Zuqi , Melograna Federico , Hoskens Hanne , Duroux Diane , Marazita Mary L. , Walsh Susan , Weinberg Seth M. , Shriver Mark D. , Müller-Myhsok Bertram , Claes Peter , Van Steen Kristel 

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

JOURNAL=Frontiers in Genetics

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1286800

DOI=10.3389/fgene.2023.1286800

ISSN=1664-8021

ABSTRACT=<p><bold>Introduction:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> 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. 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.</p><p><bold>Discussion:</bold> 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.</p>