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netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.
- Source :
- Frontiers in Genetics; 2023, p1-15, 15p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16648021
- Database :
- Complementary Index
- Journal :
- Frontiers in Genetics
- Publication Type :
- Academic Journal
- Accession number :
- 174435056
- Full Text :
- https://doi.org/10.3389/fgene.2023.1286800