ArticleCell systems2018
Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.
Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 49 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed.
- Protein Variability Patterns in Ovarian Serous Carcinoma.International journal of molecular sciences · 2026Article
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.Biostatistics (Oxford, England) · 2026Article
- Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon.Biostatistics (Oxford, England) · 2025Article
- distQTL: distribution quantitative trait loci identification by population-scale single-cell data.NAR genomics and bioinformatics · 2025Article
- A novel gene expression stability metric to unveil homeostasis and regulation.Genome biology · 2025Article
- Genome-scale metabolic modeling and machine learning unravel metabolic reprogramming and mast cell role in lung cancer: a multi-level analysis.Translational lung cancer research · 2025Article
- distQTL: Distribution Quantitative Trait Loci Identification by Population-Scale Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Mapping Organism-wide Single Cell mRNA Expression Linked to Extracellular Vesicle Biogenesis, Secretion, and Cargo.Function (Oxford, England) · 2025Article
- Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.NPJ systems biology and applications · 2025Article
- Addressing the mean-variance relationship in spatially resolved transcriptomics data withbioRxiv : the preprint server for biology · 2024Article
- Method of moments framework for differential expression analysis of single-cell RNA sequencing data.Cell · 2024Article
- Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.BMC genomics · 2024Review
- Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis.Nature communications · 2024Article
- Pianno: a probabilistic framework automating semantic annotation for spatial transcriptomics.Nature communications · 2024Article
- Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics.Bioinformatics (Oxford, England) · 2023Article
- Spatial and single-cell profiling of the metabolome, transcriptome and epigenome of the aging mouse liver.Nature aging · 2023Article
- Article
- Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging.Genome biology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays an important role in tissue function and development. Single-cell RNA sequencing can characterize this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinder meaningful comparison of expression variability between cell populations. To address this problem, we introduce an analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. This includes a robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronization of biosynthetic machinery components in immune cells upon activation. In contrast to the uniform up-regulation of the biosynthetic machinery, CD4
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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 OpenQuestion graph.