Statistical Significance of Clustering for High-Dimensional Count Data

Yifan Dai, Di Wu, Yufeng Liu

Keywords: Unsupervised learning, Cluster index, Generalized PCA, Dimension reduction, High-dimension low-sample size data

Abstract: Clustering is widely used in biomedical research for meaningful subgroup identification. However, most existing clustering algorithms do not account for the statistical uncertainty of the resulting clusters and consequently may generate spurious clusters due to natural sampling variation. To address this problem, the Statistical Significance of Clustering (SigClust) method was developed to evaluate significance of clusters in high-dimensional data. While SigClust has been successful in testing mixtures of continuous distributions, it is not specifically designed for discrete distributions, such as count data in genomics. Moreover, SigClust and its variations often suffer from reduced statistical power when applied to non-Gaussian high-dimensional data. To overcome these limitations, we propose SigClust-DEV, a method designed to evaluate the significance of clusters in count data. Through extensive simulations, we compare SigClust-DEV against other existing SigClust approaches across various count distributions and demonstrate its superior performance. Furthermore, we apply our method SigClust-DEV to Hydra single-cell RNA sequencing (scRNA) data and electronic health records (EHRs) of cancer patients to identify meaningful latent cell types and patient subgroups, respectively.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 7518