Automatic cell-type harmonization and integration across Human Cell Atlas datasets.
Cell
; 186(26): 5876-5891.e20, 2023 12 21.
Article
em En
| MEDLINE
| ID: mdl-38134877
ABSTRACT
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 â¼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Perfilação da Expressão Gênica
/
Transcriptoma
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article