ArticleNAR genomics and bioinformatics2023
Single-cell reference mapping to construct and extend cell-type hierarchies.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- An integrated reference atlas of human skeletal muscle.EBioMedicine · 2026Article
- Cross-dataset annotation harmonization for cell-type hierarchy construction.Bioinformatics (Oxford, England) · 2026Article
- AnnQ: reference-based quantification of cellular abnormality at single-cell resolution.Briefings in bioinformatics · 2026Article
- NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.Nature neuroscience · 2026Article
- Article
- Quantifying pathological progression from single-cell transcriptomic data with scPSS.Genome research · 2026Article
- scSpecies: enhancement of network architecture alignment in comparative single-cell studies.Genome biology · 2025Article
- Article
- Article
- CellWalker2: Multi-omic discovery using hierarchical cell type relationships.Cell genomics · 2025Article
- Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life.Nucleic acids research · 2025Article
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.Nature reviews. Molecular cell biology · 2025Review
- Considerations for building and using integrated single-cell atlases.Nature methods · 2025Review
- BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets.Computational and structural biotechnology journal · 2024Article
- Hierarchical annotation of eQTLs by H-eQTL enables identification of genes with cell type-divergent regulation.Genome biology · 2024Article
- scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data.Genome biology · 2024Article
- Review
- Partial label learning for automated classification of single-cell transcriptomic profiles.PLoS computational biology · 2024Article
- Article
- scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.Communications biology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge in the field. Whereas recent computational techniques, referred to as 'reference mapping' methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present 'treeArches', a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.
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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.