# Cross-Study Replicability in Cluster Analysis

arXiv: https://arxiv.org/abs/2202.01910 · HTML: https://mlsanity.com/paper/2202.01910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2022.

## Built on by (most cited first, 1 of 1)

- [ERICA: Quantifying Replicability of Cluster Analysis](https://mlsanity.com/paper/2606.00302/) (2026)

## Abstract (arXiv)

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.