Clustering individuals using INMTD

Authors

• Zuqi Li, KU LeuvenFollow
• Sam F.L. Windels, Barcelona Supercomputing Center
• Noël Malod-Dognin, Barcelona Supercomputing Center
• Seth M. Weinberg, University of Pittsburgh
• Mary L. Marazita, University of Pittsburgh
• Susan Walsh, Indiana University-Purdue University Indianapolis
• Mark D. Shriver, The Pennsylvania State University
• David W. Fardo, University of KentuckyFollow
• Peter Claes, KU Leuven
• Nataša Przulj, Barcelona Supercomputing Center
• Kristel Van Steen, KU Leuven

Abstract

Motivation: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). Results: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation.

Document Type

Article

Publication Date

4-1-2025

Notes/Citation Information

Publisher Copyright: © 2025 The Author(s).

Digital Object Identifier (DOI)

10.1093/bioinformatics/btaf122

Archival?

Archival

Repository Citation

Li, Zuqi; Windels, Sam F.L.; Malod-Dognin, Noël; Weinberg, Seth M.; Marazita, Mary L.; Walsh, Susan; Shriver, Mark D.; Fardo, David W.; Claes, Peter; Przulj, Nataša; and Van Steen, Kristel, "Clustering individuals using INMTD" (2025). Biostatistics Faculty Publications. 120.
https://uknowledge.uky.edu/biostatistics_facpub/120