ArticleBioinformatics (Oxford, England)2020
Simulation, power evaluation and sample size recommendation for single-cell RNA-seq.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Effects of auricular point acupressure on inflammatory signaling in chronic low back pain: a secondary analysis of a randomized controlled trial.BMC complementary medicine and therapies · 2025Trial
- VINE-seq and MultiVINE-seq for single-nucleus and multiome profiling of the brain vasculature.Nature protocols · 2026Review
- Article
- Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.bioRxiv : the preprint server for biology · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.Briefings in bioinformatics · 2026Article
- Depower: An R Package for Simulation-Based Power Analysis of Differential Expression Studies.Cancer informatics · 2026Article
- Implications of Single-Cell RNA Sequencing in Cervical Cancer: Unravelling the Molecular Landscape.ACS omega · 2025Review
- Simulating paired and longitudinal single-cell RNA sequencing data with rescueSim.Bioinformatics (Oxford, England) · 2025Article
- PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.PLoS computational biology · 2025Article
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.Communications biology · 2025Article
- Implementation and validation of single-cell genomics experiments in neuroscience.Nature neuroscience · 2024Review
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- A distribution-free and analytic method for power and sample size calculation in single-cell differential expression.Bioinformatics (Oxford, England) · 2024Article
- Comprehensive analysis of genetic associations and single-cell expression profiles reveals potential links between migraine and multiple diseases: a phenome-wide association study.Frontiers in neurology · 2024Article
- The shaky foundations of simulating single-cell RNA sequencing data.Genome biology · 2023Article
- Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives.Biomolecules · 2023Review
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
- Practical Considerations for Single-Cell Genomics.Current protocols · 2022Review
- The R Language: An Engine for Bioinformatics and Data Science.Life (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationDetermining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge.
resultsWe propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA-sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. The data simulator in POWSC outperforms two other state-of-art simulators in capturing key characteristics of real datasets. The power assessor in POWSC provides a variety of power evaluations including stratified and marginal power analyses for DEs characterized by two forms (phase transition or magnitude tuning), under different comparison scenarios. In addition, POWSC offers information for optimizing the tradeoffs between sample size and sequencing depth with the same total reads. AVAILABILITY AND IMPLEMENTATION: POWSC is an open-source R package available online at https://github.com/suke18/POWSC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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