ArticleNPJ systems biology and applications2025
Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Single-cell gene regulatory networks characterize colonic stem and immune cell homeostasis iniScience · 2026Article
- Article
- Assessing the variation of DNA damage response of individual human cells exposed to photon radiation.Radiation and environmental biophysics · 2026Article
- Auxin promotes robust founder cell specification during Arabidopsis lateral root initiation.Genetics · 2026Article
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Leveraging Single-Cell Technologies to Advance Understanding of Myocardial Disease.Circulation research · 2026Review
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology.International journal of molecular sciences · 2025Review
- Comparing gene-gene co-expression network approaches for the analysis of cell differentiation and specification on scRNAseq data.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.