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

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, that integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. 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. Availability and implementation: https://github.com/ZuqiLi/INMTD