A general framework for testing clustering significance and variable-level inference in high-dimensional data.

Authors

Hui Shen, Dongmei Li, Yufeng Liu

Published in

Biometrics. Volume 82. Issue 3. Jul 01, 2026.

Abstract

Clustering is a fundamental tool for uncovering heterogeneity in data, but two challenges remain: determining whether observed clusters reflect genuine structure rather than sampling variability, and identifying the variables that drive any significant clustering pattern. Statistical significance of clustering (SigClust) addresses the first problem by assessing clustering significance using the cluster index under a Gaussian null model, with the null distribution estimated by Monte Carlo simulation in high dimensions. We propose SigClust-DE, a method that improves null covariance estimation in SigClust and extends the framework to variable-level inference for identifying features associated with cluster separation. In this way, SigClust-DE provides a joint framework for clustering significance testing and differential expression analysis, a central task in RNA-seq studies. Through extensive simulations and an application to RNA-seq data, we show that SigClust-DE controls Type I error in clustering significance testing, controls the false discovery rate in variable-level inference, and achieves strong power for detecting differentially expressed features.

PMID:
42613050
Bibliographic data and abstract were imported from PubMed on 19 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.