Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

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 applic

SUBMITTER: Li Z 

PROVIDER: S-EPMC11978392 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature