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

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 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. 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.

Original languageEnglish (US)
Article number1286800
JournalFrontiers in Genetics
Volume14
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)

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