ArticleNature biotechnology2013
Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments.
Article in Nature biotechnology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
145 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- CroCoNet: a framework for the quantitative comparison of gene regulatory networks across species.Genome biology · 2026Article
- Logistic regression for estimating functional effects with spatial transcriptomics.Nucleic acids research · 2026Article
- eQTL analysis: A bridge from genome to mechanism.Genes & diseases · 2026Review
- Beyond the mean: genetic control of gene expression fidelity and dispersion.bioRxiv : the preprint server for biology · 2026Article
- CILP Inhibits hyaline cartilage fibrosis and chondrocyte ferroptosis via keap1-Nrf2 axis in early osteoarthritis exercise therapy.Cellular and molecular life sciences : CMLS · 2026Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Epstein-Barr Virus Infection at Single-Cell Resolution.Journal of medical virology · 2026Review
- Single-cell omics reveals arg-1 as a key regulator of age-dependent macrophage-mediated cartilage repair.PloS one · 2026Article
- Advancing breast cancer treatment through dual targeting CAR T cell therapy.Discover oncology · 2025Review
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- PPARγ controls ESCRT-dependent fibroblast-like synoviocyte exosome biogenesis and alleviates chondrocyte osteoarthritis mediated by exosomal ANXA1.Journal of orthopaedic translation · 2025Article
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- Deterministic patterns in single-cell transcriptomic data.NPJ systems biology and applications · 2025Article
- Lung Immune Cell Niches and the Discovery of New Cell Subtypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues.Heliyon · 2024Article
- Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.BMC genomics · 2024Review
- scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.Bioinformatics (Oxford, England) · 2024Article
- scQTLbase: an integrated human single-cell eQTL database.Nucleic acids research · 2024Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
85 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
7 authors.
Funding
Abstract
Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of genetic variation. Here we measure the expression of 92 genes affected by Wnt signaling in 1,440 single cells from 15 individuals to associate single-nucleotide polymorphisms (SNPs) with gene-expression phenotypes, while accounting for stochastic and cell-cycle differences between cells. We provide evidence that many heritable variations in gene function--such as burst size, burst frequency, cell cycle-specific expression and expression correlation/noise between cells--are masked when expression is averaged over many cells. Our results demonstrate how single-cell analyses provide insights into the mechanistic and network effects of genetic variability, with improved statistical power to model these effects on gene expression.
Indexed as
Identifiers
23873083What OpenQuestion holds
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 OpenQuestion graph.