CEMUSA: a graph-based integrative metric for evaluating clusters in spatial transcriptomics

TL;DR

This paper introduces CEMUSA, a new graph-based metric for evaluating clustering in spatial transcriptomics that considers label agreement, spatial organization, and error severity.

Contribution

CEMUSA is a novel integrative metric that combines label agreement, spatial organization, and error severity for evaluating spatial transcriptomics clustering.

Findings

CEMUSA outperforms conventional metrics in differentiating clustering results with subtle topological differences.

The metric maintains computational efficiency while capturing error severity and spatial organization.

CEMUSA is validated on both simulated and real datasets.

Abstract

Spatial clustering is a critical analytical task in spatial transcriptomics (ST) that aids in uncovering the spatial molecular mechanisms underlying biological phenotypes. Along with the numerous spatial clustering methods, there comes the imperative need for an effective metric to evaluate their performance. An ideal metric should consider three factors: label agreement, spatial organization, and error severity. However, existing evaluation metrics focus solely on either label agreement or spatial organization, leading to biased and misleading evaluations.

To fill this gap, we propose CEMUSA, a novel graph-based metric that integrates these factors into a unified evaluation framework. Extensive testing on both simulated and real datasets demonstrate CEMUSA’s superiority over conventional metrics in differentiating clustering results with subtle differences in topology and error severity, while maintaining computational efficiency.

The source code and data are freely available at https://github.com/YihDu/CEMUSA. CEMUSA is implemented as an R package at https://yihdu.github.io/CEMUSA.

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