Exploring cell-to-cell variability and functional insights through differentially variable gene analysis

作者：Victoria Gatlin, Shreyan Gupta, Selim Romero, Robert S. Chapkin, James J. Cai · 发表于：npj Systems Biology and Applications · 年份：2025 · DOI：10.1038/s41540-025-00507-z · 被引用次数：8 · 研究领域：Single-cell and spatial transcriptomics、Gene Regulatory Network Analysis、Gene expression and cancer classification

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular variability by capturing gene expression profiles of individual cells. The importance of cell-to-cell variability in determining and shaping cell function has been widely appreciated. Nevertheless, differential expression (DE) analysis remains a cornerstone method in analytical practice. Current computational analyses overlook the rich information encoded by variability within the single-cell gene expression data by focusing exclusively on mean expression. To offer a deeper understanding of cellular systems, there is a need for approaches to assess data variability rather than just the mean. Here we present spline-DV, a statistical framework for differential variability (DV) analysis using scRNA-seq data. The spline-DV method identifies genes exhibiting significantly increased or decreased expression variability among cells derived from two experimental conditions. Case studies show that DV genes identified using spline-DV are representative and functionally relevant to tested cellular conditions, including obesity, fibrosis, and cancer.