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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月20日

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  • Victoria Gatlin,
  • Shreyan Gupta,
  • Selim Romero,
  • Robert S. Chapkin,
  • James J. Cai

摘要

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.