ArticleBioinformatics (Oxford, England)2025
uHAF: a unified hierarchical annotation framework for cell type standardization and harmonization.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Benchmarking AI scientists for omics data-driven biological discovery.Bioinformatics (Oxford, England) · 2026Article
- Large language model agents for biological intelligence across genomics, proteomics, spatial biology, and biomedicine.Briefings in bioinformatics · 2026Review
- hECA v2.0: an AI-ready ensemble cell atlas of single-cell RNA and ATAC sequencing data.Scientific data · 2025Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
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4 authors.
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Abstract
summaryIn single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community. AVAILABILITY AND IMPLEMENTATION: uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.
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