DIFS: Discriminative Feature Selection for Cell Clustering based on Single-Cell RNA Sequencing Data

Abstract

A key challenge in single-cell RNA sequencing (scRNA-seq) analysis is clustering cells based on their expression profiles. Effective clustering requires selecting the most informative gene features whose varying expression levels in different cell types can be used to discriminate between different cell types. This study introduces DIFS, a novel statistical framework designed to enhance discriminative feature selection for scRNA-seq-based cell clustering. DIFS operates in two stages. In the first stage, a modified dip test identifies genes with significant multimodal expression patterns, as these are likely to have different expression levels in different cell types. In the second stage, cells are clustered based on the selected features from stage one, and additional cluster-specific features are identified, capturing genes that may be expressed in only one cell cluster. Through real data analysis, we demonstrate that DIFS improves the accuracy of cell type classification and enhances the understanding of cellular heterogeneity in single-cell studies.