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

Abstract

Recent advancements in single-cell and spatial omics have enabled the analysis of gene expression patterns in cells and tissues with unprecedented precision. Genes that exhibit significant variation across different cell types or states are typically closely linked to cellular functional states and disease processes. However, current differential analysis methods often fail to adequately account for cell type specificity when identifying differentially expressed genes. CSFeatures effectively addresses this issue by comprehensively considering the expression level, the smoothness of expression distribution, and the proportion of gene expression in the target cell population as well as in each of the other cell populations. Through comprehensive experiments on eight single-cell RNA-seq datasets, it was demonstrated that CSFeatures can effectively identify cell type-specific genes that are highly expressed in the target cell population while being lowly expressed in all other populations. Moreover, it was validated that the underlying principle of CSFeatures can be generalized to other omics data, including single-cell ATAC-seq, spatial transcriptomics, and spatial ATAC-seq data. Furthermore, the differential features identified by CSFeatures are enriched in pathways, functional categories, and regulatory regions that are highly relevant to the specific functional states of the target cell population, providing insights into its gene regulation.