Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.
Victoria Gatlin, Shreyan Gupta, Selim Romero, Robert S Chapkin, James J Cai
Author Information
Victoria Gatlin: Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA. ORCID
Shreyan Gupta: Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA. ORCID
Selim Romero: Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA.
Robert S Chapkin: CPRIT Single Cell Data Science Core, Texas A&M University, College Station, TX, 77843, USA.
James J Cai: Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA. jcai@tamu.edu. ORCID
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.
