Cross-dataset annotation harmonization for cell-type hierarchy construction

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ID: 320354
2026
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Abstract
MOTIVATION: Single-cell transcriptomic datasets annotate cell types with diverse schemes and varying resolution. This poses challenges in building unified hierarchical cell-type structures and hinders integration of large-scale datasets. To address this, several computational methods have been developed to harmonize cell type annotations across datasets and build data-driven hierarchies of cell types. RESULTS: Here, we benchmarked three state-of-the-art methods: scHPL, treeArches and CellHint. We evaluated these methods across 5 simulated scenarios and 5 real-world scenarios across cell types and organs. To assess harmonization results, we designed three metrics, Annotation Harmonization F1-score (AH-F1), Tree Edit Distance Similarity (TEDS) and Parent-Children Branches Similarity (PCBS), comparing the constructed cell-type hierarchies and the knowledge-based ones. Based on the benchmarking results, we found that methods performed well in simulated scenarios but still have room for improvement in complex real-world data. Thus, we developed OTHarmonizer, a tool based on partial optimal transport (OT) for cell-type harmonization and hierarchy construction. OTHarmonizer excels in accurately capturing equivalent and hierarchical relationships between cell types, offering a more effective approach for the cell-type hierarchy construction across datasets. AVAILABILITY AND IMPLEMENTATION: The simulated and real-world datasets in the benchmark are available on https://figshare.com/articles/dataset/OTHarmonizer/28243205. The source codes for the benchmark and OTHarmonizer are available online on GitHub at https://github.com/Duck-Boss/OTHarmonizer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7167817196 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tianhong Zhou, Yixin Chen, Zhu Y, Jinmeng Jia, Xi Zhang, Lei Wei
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag506
URL
https://doi.org/10.1093/bioinformatics/btag506
Keywords Keywords not found

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