Exploring cell-to-cell variability and functional insights through differentially variable gene analysis
Abstract 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, diffe...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00507-z |
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| author | Victoria Gatlin Shreyan Gupta Selim Romero Robert S. Chapkin James J. Cai |
| author_facet | Victoria Gatlin Shreyan Gupta Selim Romero Robert S. Chapkin James J. Cai |
| author_sort | Victoria Gatlin |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-313353dfb0d14d848f8f0cf0f447258b |
| institution | DOAJ |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-313353dfb0d14d848f8f0cf0f447258b2025-08-20T02:52:17ZengNature Portfolionpj Systems Biology and Applications2056-71892025-03-0111111110.1038/s41540-025-00507-zExploring cell-to-cell variability and functional insights through differentially variable gene analysisVictoria Gatlin0Shreyan Gupta1Selim Romero2Robert S. Chapkin3James J. Cai4Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M UniversityDepartment of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M UniversityDepartment of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M UniversityCPRIT Single Cell Data Science Core, Texas A&M UniversityDepartment of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M UniversityAbstract 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.https://doi.org/10.1038/s41540-025-00507-z |
| spellingShingle | Victoria Gatlin Shreyan Gupta Selim Romero Robert S. Chapkin James J. Cai Exploring cell-to-cell variability and functional insights through differentially variable gene analysis npj Systems Biology and Applications |
| title | Exploring cell-to-cell variability and functional insights through differentially variable gene analysis |
| title_full | Exploring cell-to-cell variability and functional insights through differentially variable gene analysis |
| title_fullStr | Exploring cell-to-cell variability and functional insights through differentially variable gene analysis |
| title_full_unstemmed | Exploring cell-to-cell variability and functional insights through differentially variable gene analysis |
| title_short | Exploring cell-to-cell variability and functional insights through differentially variable gene analysis |
| title_sort | exploring cell to cell variability and functional insights through differentially variable gene analysis |
| url | https://doi.org/10.1038/s41540-025-00507-z |
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