Monju: Multi-criteria clustering in single-cell omics

Abstract

Clustering is a fundamental step in single-cell omics analysis. Although single-cell omics data can, in principle, be partitioned according to multiple biologically meaningful criteria, existing methods typically cluster cells using a single criterion. To address this problem, we developed Monju, a multi-criteria clustering method based on a deep generative mixture model. Monju divides cells into biologically reasonable submodels, each of which is equipped with an interpretable latent space. Furthermore, although the partitioning of cells into submodels varies across random seeds, each solution remains biologically plausible, collectively yielding multi-criteria clustering. Moreover, by integrating these multiple clustering solutions to perform meta-clustering, Monju enables the assessment of cluster stability. We applied Monju to human peripheral blood CITE-seq data and demonstrated that it can achieve multi-criteria clustering. Monju therefore provides a powerful and practical framework for dissecting cellular heterogeneity from multiple biological perspectives.