netQDA: Local Network-Guided High-Dimensional Quadratic Discriminant Analysis

Xueping Zhou, Wei Chen and Yanming Li ()
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Xueping Zhou: Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15216, USA
Wei Chen: Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
Yanming Li: Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA

Mathematics, 2024, vol. 12, issue 23, 1-21

Abstract: Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a local network-guided QDA method that integrates the local connection structures of high-dimensional predictors. In the context of gene expression research, our method can identify genes that show differential expression levels as well as gene networks that exhibit different connection patterns between various biological state groups, thereby enhancing our understanding of underlying biological mechanisms. Extensive simulations and real data applications demonstrate its superior performance in both feature selection and outcome classification compared to commonly used discriminant analysis methods.

Keywords: differentially connected gene; feature selection; gene coregulation network; quadratic discriminant analysis; untralhigh-dimensional feature screening; weak differentially expressed gene (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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