ArticleNature communications2021
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 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
67 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.
- Functional genomic mechanisms of opioid action and opioid use disorder: a systematic review of animal models and human studies.Molecular psychiatry · 2023Pooled it
- PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- VINE-seq and MultiVINE-seq for single-nucleus and multiome profiling of the brain vasculature.Nature protocols · 2026Review
- Fibronectin mediates APOE4-driven blood-brain barrier dysfunction in Alzheimer's disease.Nature aging · 2026Article
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Cerebrovascular vulnerability and fibrosis in human brain aneurysms.Nature neuroscience · 2026Article
- Spatially resolved fetal and maternal cell contributions to severe preeclampsia across gestation.Science advances · 2026Article
- Transcriptomic Profiling Identifies Disease-Specific miRNA-mRNA Regulatory Networks in Systemic Sclerosis.Biomolecules · 2026Article
- Multimodal Peripheral Blood Immunophenotyping and Single-Cell Epigenetic Analysis of Knee Osteoarthritis Reveals Expansion and Epigenetic Dysregulation of Treg Cell and Nonclassical Monocyte Populations.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- PerturbPlan: An analytical framework for designing Perturb-seq experiments.bioRxiv : the preprint server for biology · 2026Article
- Spatial transcriptomics and single-nucleus RNA sequencing reveal rAAV2- and rAAV9-specific transduction signatures in the mouse liver.Gene therapy · 2026Article
- eQTL analysis: A bridge from genome to mechanism.Genes & diseases · 2026Review
- Persistent Increased Plasmacytoid Dendritic Cells and Inflammation in People With HIV Years After Tuberculosis.The Journal of infectious diseases · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- Liver single-nucleus multiome profiling reveals cell-type mechanisms for cardiometabolic traits.American journal of human genetics · 2026Article
- Article
- Single-cell transcriptome profiling reveals immunological fitness of HIV long-term non-progressors.Journal of virology · 2025Article
- The BCGMethodsX · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.
Indexed as
Identifiers
What 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.