2022/08/07 by H Chung, Hee Cheol Chung, Chung, Hee Cheol +4 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · #Cancer-related molecular mechanisms research #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME) #Statistical Methods and Inference #stat.ME
paper · pdf · doi:10.48550/arxiv.2208.03734
arxiv created 2022/08/07 · openalex publication_date 2022/08/07 · arxiv updated 2022/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sequencing-based technologies provide an abundance of high-dimensional biological datasets with skewed and zero-inflated measurements. Classification of such data with linear discriminant analysis leads to poor performance due to the violation of the Gaussian distribution assumption. To address this limitation, we propose a new semiparametric discriminant analysis framework based on the truncated latent Gaussian copula model that accommodates both skewness and zero inflation. By applying sparsity regularization, we demonstrate that the proposed method leads to the consistent estimation of classification direction in high-dimensional settings. On simulated data, the proposed method shows superior performance compared to the existing method. We apply the method to discriminate healthy controls from patients with Crohn's disease based on microbiome data and to identify genera with the most influence on the classification rule.