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

## TL;DR

This paper introduces a new clustering algorithm for single-cell RNA sequencing data that improves performance by using geometric volume as a clustering criterion.

## Contribution

The novel K-volume clustering algorithm uses convex volume as a criterion to optimize hierarchical structure and cluster count simultaneously.

## Key findings

- K-volume clustering outperforms traditional methods in various biological applications.
- The algorithm optimizes hierarchical structure and cluster number through nonlinear optimization.
- Validation on real datasets confirms the algorithm's effectiveness.

## Abstract

Clustering high-dimensional and structural data remains a significant challenge in computational biology, particularly for complex single-cell and multi-omics datasets. In this work, we introduce a novel clustering algorithm that utilizes the total convex volume defined by points within a cluster as a biologically relevant and geometrically interpretable criterion. This approach simultaneously optimizes both the hierarchical structure and the number of clusters at each level through nonlinear optimization. We evaluate our algorithm against other clustering methods, and the results demonstrate that our approach outperforms traditional techniques across a variety of biological applications.

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.