Clustering Schemes on the Torus with Application to RNA Clashes

TL;DR

This paper introduces new clustering methods for data on the torus, specifically applied to RNA dihedral angles, addressing errors in molecular structure reconstructions and advancing statistical techniques on manifolds.

Contribution

It proposes novel PCA and clustering methods for torus data, enhancing analysis of RNA structures and contributing to the broader field of statistics on manifolds.

Findings

Clustering results vary with different parameter settings.

The methods improve RNA clash detection.

Advances in directional statistics are demonstrated.

Abstract

Molecular structures of RNA molecules reconstructed from X-ray crystallography frequently contain errors. Motivated by this problem we examine clustering on a torus since RNA shapes can be described by dihedral angles. A previously developed clustering method for torus data involves two tuning parameters and we assess clustering results for different parameter values in relation to the problem of so-called RNA clashes. This clustering problem is part of the dynamically evolving field of statistics on manifolds. Statistical problems on the torus highlight general challenges for statistics on manifolds. Therefore, the torus PCA and clustering methods we propose make an important contribution to directional statistics and statistics on manifolds in general.