ReviewNature protocols2021
Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods.
Review in Nature protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 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
138 citing papers in PubMed.
- HPRT1 drives mitochondrial metabolic reprogramming and immunosuppressive microenvironment in oral squamous cell carcinoma.Apoptosis : an international journal on programmed cell death · 2026Article
- scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.Briefings in bioinformatics · 2026Article
- Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.BioData mining · 2026Article
- CellTypeAI: cell annotation for scRNA-seq using local generative-AI.Bioinformatics (Oxford, England) · 2026Article
- Application of human induced pluripotent stem cells for tissue modeling and therapy: are we on track?Stem cells (Dayton, Ohio) · 2026Review
- Interpretable deep generative ensemble learning for single-cell omics with Hydra.Molecular systems biology · 2026Article
- Immune context governs stage-specific responses to depleting anti-PD-1 antibody in type 1 diabetes.Cell biomaterials · 2026Article
- ICON: An isoform-aware hierarchical random forest model for cell type classification.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- scGPT: end-to-end protocol for fine-tuned retinal cell type annotation.Nature protocols · 2026Review
- Enumeration, Phenotyping, and Clinical Associations of Tissue-Resident T Cells in the Ecto- and Endocervix of Women Attending a Colposcopy Clinic.American journal of reproductive immunology (New York, N.Y. : 1989) · 2026Article
- Autophagy Activators Normalize Aberrant Tau Proteostasis and Rescue Synapses in Human Familial Alzheimer's Disease iPSC-Derived Cortical Organoids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
- A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios.PLoS computational biology · 2026Article
- Transcriptional Remodeling of Microglia After Experimental Myocardial Infarction.International journal of molecular sciences · 2026Article
- Single-Cell Transcriptomic Analysis of Macaque LGN Neurons Reveals Novel Subpopulations.Molecular neurobiology · 2026Article
- Single-cell analysis of the peripheral immune landscape in Parkinson's disease: insights into dendritic cell and CD4+ T-cell transcriptomics.NPJ Parkinson's disease · 2026Article
- Probing the Feasibility of Single-Cell Fixed RNA Sequencing from FFPE Tissue.International journal of molecular sciences · 2026Article
- OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- A single-cell atlas and Shiny-based framework for murine lung injury and remodeling.Frontiers in bioinformatics · 2026Article
78 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.
Indexed as
Identifiers
34031612What 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.