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Sequencing-based technologies provide an abundance of high-dimensionalbiological datasets with skewed and zero-inflated measurements. Classificationof such data with linear discriminant analysis leads to poor performance due tothe violation of the Gaussian distribution assumption. To address thislimitation, we propose a new semiparametric discriminant analysis frameworkbased on the truncated latent Gaussian copula model that accommodates bothskewness and zero inflation. By applying sparsity regularization, wedemonstrate that the proposed method leads to the consistent estimation ofclassification direction in high-dimensional settings. On simulated data, theproposed method shows superior performance compared to the existing method. Weapply the method to discriminate healthy controls from patients with Crohn'sdisease based on microbiome data and to identify genera with the most influenceon the classification rule.

Sparse semiparametric discriminant analysis for high-dimensional zero-inflated data