ArticleNature genetics2024
Consensus prediction of cell type labels in single-cell data with popV.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference.Nature communications · 2026Article
- Single-cell splice isoform usage reveals distinct axes of cellular identity and senescence.bioRxiv : the preprint server for biology · 2026Article
- Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.Briefings in bioinformatics · 2026Article
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr.Genome research · 2026Article
- A stretch-responsive fibroblast program promotes epidermal stem cell self-renewal during skin expansion.Nature communications · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- Transient YAP activation uncovers the neurogenic potential of proliferative mammalian Müller glia.PNAS nexus · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- AnnQ: reference-based quantification of cellular abnormality at single-cell resolution.Briefings in bioinformatics · 2026Article
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- sigNATURE maps cohort-specific T-cell states to reproducible programs of ICI response.bioRxiv : the preprint server for biology · 2026Article
- Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas.Scientific data · 2026Article
- Improving atlas-scale single-cell annotation models with hierarchical cross-entropy loss.Nature computational science · 2026Article
- Identity crisis: exploring the boundaries of cell type identification in the age of single-cell transcriptomics.Frontiers in cellular neuroscience · 2026Article
- Regulatory element modules as universal features for single-cell chromatin analysis.bioRxiv : the preprint server for biology · 2025Article
- GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data.bioRxiv : the preprint server for biology · 2025Article
- Mapping atherogenesis mechanisms in smooth muscle cells by targeting genes linked to coronary artery disease.iScience · 2025Article
- Predicting emergent phenotypes from single cell populations using CELLECTION.bioRxiv : the preprint server for biology · 2025Article
- Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age.Nature immunology · 2025Article
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Authors and funding
9 authors.
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Abstract
Cell-type classification is a crucial step in single-cell sequencing analysis. Various methods have been proposed for transferring a cell-type label from an annotated reference atlas to unannotated query datasets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate by label transfer. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.
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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.