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Nonparametric Hypothesis Testing of High-dimensional Clustering With Application to Single-cell RNA Data

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Single-cell RNA sequencing studies routinely use clustering to define putative cell types and cell states, yet the observed separation may arise from sampling variability rather than genuine biological heterogeneity. This paper studies formal significance testing of such clustering structure in high-dimensional data. Existing SigClust methods assess clustering significance through Monte Carlo simulation under a Gaussian single-cluster null, but this assumption can be unreliable for normalized gene expression data and other non-Gaussian settings. We propose SigClust-LCP, a nonparametric extensi

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