Automatic cell-type harmonization and integration across Human Cell Atlas datasets

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biase...

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Vydáno v:Cell Ročník 186; číslo 26; s. 5876
Hlavní autoři: Xu, Chuan, Prete, Martin, Webb, Simone, Jardine, Laura, Stewart, Benjamin J, Hoo, Regina, He, Peng, Meyer, Kerstin B, Teichmann, Sarah A
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 21.12.2023
Témata:
organ atlas
single cell
cell hierarchy
cell-type harmonization
Human Cell Atlas
predictive clustering tree
machine learning
data integration
harmonization graph
ISSN:1097-4172, 1097-4172
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Shrnutí:Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1097-4172
1097-4172
DOI:10.1016/j.cell.2023.11.026