K-Volume Clustering Algorithms for scRNA-Seq Data Analysis.

Abstract

Clustering high-dimensional and structural data remains a key challenge in computational biology, especially for complex single-cell and multi-omics datasets. In this study, we present K-volume clustering, a novel algorithm that uses the total convex volume defined by points within a cluster as a biologically relevant and geometrically interpretable criterion. This method simultaneously optimizes both the hierarchical structure and the number of clusters at each level through nonlinear optimization. Validation on real datasets shows that K-volume clustering outperforms traditional methods across a range of biological applications. With its theoretical foundation and broad applicability, K-volume clustering holds great promise as a core tool for diverse data analysis tasks.