Li, Zuqi, Melograna, Federico, Hoskens, Hanne, Duroux, Diane, Marazita, Mary L, Walsh, Susan, Weinberg, Seth M, Shriver, Mark D, Müller-Myhsok, Bertram 
ORCID: 0000-0002-0719-101X, Claes, Peter et al (show 1 more authors)
(2023)
netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.
Frontiers in genetics, 14.
p. 1286800.
Abstract
<b>Introduction:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Discussion:</b> 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | distinguishing genetics, facial images, genomics, multi-modal data, multi-view clustering, obesity subtyping, personalized network, social heterogeneity |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 01 Feb 2024 09:37 |
| Last Modified: | 01 Feb 2024 09:37 |
| DOI: | 10.3389/fgene.2023.1286800 |
| Open Access URL: | https://doi.org/10.3389/fgene.2023.1286800 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3178251 |
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