ReviewNature methods2021
The triumphs and limitations of computational methods for scRNA-seq.
Review in Nature methods, 2021. 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 203 papers.
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
203 citing papers in PubMed.
- Regorafenib plus nivolumab in unresectable hepatocellular carcinoma: the phase 2 RENOBATE trial.Nature medicine · 2024Trial
- Intestinal Stem Cells: Regulation, Plasticity, and Therapeutic Potential.Methods in molecular biology (Clifton, N.J.) · 2027Review
- Single nucleus transcriptomics reveals ITGA8-driven fibroblast remodeling in rheumatic valvular atrial fibrillation.iScience · 2026Article
- Interpretable machine learning enables de novo mapping of cell type-specific RNA splicing regulation from scRNA-seq data.Nature communications · 2026Article
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- Identifying phenotype-genotype-function coupling in 3D organoid imaging using Shape, Appearance and Motion Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- SKIM: A fast sketching strategy integrated with model's dynamic-feedback for large-scale single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Single-cell RNA sequencing in ovarian cancer: decoding the tumor microenvironment for personalized therapy.Journal of ovarian research · 2026Review
- ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories.Nature communications · 2026Article
- stGNN: Spatially Informed Cell-Type Deconvolution Based on Deep Graph Learning and Statistical Modeling.Interdisciplinary sciences, computational life sciences · 2026Article
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.Nature communications · 2026Article
- BiGER: Bayesian rank aggregation in genomics with extended ranking schemes.Nature communications · 2026Article
- scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.Briefings in bioinformatics · 2026Article
- Unravelling bacterial complexity at high resolution with single-cell transcriptomics.Nature microbiology · 2026Review
- Decoding the CAF-TAM axis: multi-omics dissection and therapeutic targeting of stromal-immune crosstalk in the tumor microenvironment.Cell death & disease · 2026Review
- Illuminating cell states by a comprehensive and interpretable single cell foundation model.Nature communications · 2026Article
143 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
Abstract
The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the underlying biology, from cell type composition to gene regulation to developmental dynamics. At the same time, rapid growth has forced continuous reevaluation of the underlying statistical models, experimental aims, and sheer volumes of data processing that are handled by these computational tools. Here, I review key computational steps of single-cell RNA sequencing (scRNA-seq) analysis, examine assumptions made by different approaches, and highlight successes, remaining ambiguities, and limitations that are important to keep in mind as scRNA-seq becomes a mainstream technique for studying biology.
Indexed as
Identifiers
34155396What 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.