CSFeatures improves the identification of cell-type-specific differential features in single-cell and spatial omics data

Introduction Advances in single-cell and spatial omics have enabled deep insights into cellular heterogeneity, characterized by molecular features that vary significantly across cell types, functional states, or diseases. However, current differential analysis methods often fail to identify molecular features that are both highly specific and applicable for experimental applications such as cell sorting and clinical diagnostics. Objectives This study aims to develop a novel methodology for identifying cell-type-specific molecular features from single-cell and spatial omics data. Methods We present CSFeatures, a computational method designed to identify cell-type-specific differential features. We performed a comprehensive evaluation on a simulated dataset and 14 real datasets, spanning multiple omics data, including single-cell RNA-seq (scRNA-seq), single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), spatial transcriptomics (ST), and spatial ATAC-seq (spaATAC-seq). Furthermore, we performed protein-level validation using multiplex immunofluorescence. Results CSFeatures integrates expression levels, distribution smoothness, and proportional representation across cell populations to identify cell-type-specific differential features from single-cell and spatial omics data. Benchmarking with 10 existing methods on 10 scRNA-seq datasets demonstrated that CSFeatures reliably identifies cell-type-specific genes highly expressed in target populations and minimally expressed elsewhere. These genes are enriched in pathways and functional categories relevant to the target cells’ functional states. Crucially, the high specificity of these markers was further validated at the protein level through multiplex immunofluorescence analysis, indicating their applicability for experimental applications such as cell sorting and clinical diagnostics. Moreover, we demonstrate that CSFeatures can robustly identify cell-type-specific features from diverse omics data, including scATAC-seq, ST, and spaATAC-seq, which offers insights into gene regulation and spatial heterogeneity. Conclusions Overall, CSFeatures enhances the identification of cell-type-specific features from multi-modal single-cell and spatial omics data. This yields high-fidelity molecular markers poised for the accurate characterization of cell populations, thereby enabling deeper mechanistic investigations within experimental and clinical settings.