Cross-Study Replicability in Cluster Analysis

TL;DR

This paper reviews methods for assessing the replicability of clustering results across multiple datasets, emphasizing their importance in cancer research for identifying consistent biological subtypes.

Contribution

It introduces a framework for evaluating cross-study clustering replicability applicable to any clustering algorithm and demonstrates its utility with experiments on gene expression data.

Findings

Replicability metrics can effectively assess consistent cluster identification across datasets.

The framework is versatile and applicable to various clustering methods.

Experiments show the approach's usefulness in real and synthetic data.

Abstract

In cancer research, clustering techniques are widely used for exploratory analyses and dimensionality reduction, playing a critical role in the identification of novel cancer subtypes, often with direct implications for patient management. As data collected by multiple research groups grows, it is increasingly feasible to investigate the replicability of clustering procedures, that is, their ability to consistently recover biologically meaningful clusters across several datasets. In this paper, we review existing methods to assess replicability of clustering analyses, and discuss a framework for evaluating cross-study clustering replicability, useful when two or more studies are available. These approaches can be applied to any clustering algorithm and can employ different measures of similarity between partitions to quantify replicability, globally (i.e. for the whole sample) as well as locally (i.e. for individual clusters). Using experiments on synthetic and real gene expression data, we illustrate the utility of replicability metrics to evaluate if the same clusters are identified consistently across a collection of datasets.