PCLDA: An interpretable cell annotation tool for single-cell RNA-sequencing data based on simple statistical methods.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, yet accurate and consistent cell-type annotation remains a crucial challenge. Numerous automated tools exist, but their complex modeling assumptions can hinder reliability across varied datasets and protocols. We propose PCLDA, a pipeline composed of three modules: t-test-based gene screening, principal component analysis (PCA) and linear discriminant analysis (LDA), all built on simple statistical methods. An ablation study shows that each module in PCLDA contributes significantly to performance and robustness, with two novel enhancements in the second module yielding substantial gains. Despite these additions, the model retains its original assumptions, computational efficiency, and interpr

SUBMITTER: Bai K 

PROVIDER: S-EPMC12329077 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature