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• W4367680094 abstract "Summary Harmonizing cell types with respect to transcriptomics identity and nomenclature across the single-cell community and assembling them into a common framework is an essential step towards building a standardized Human Cell Atlas. Here we present CellTypist v2.0 (a new version of our automated annotation tool, https://github.com/Teichlab/celltypist ), where we develop a predictive clustering tree-based approach to resolve cell type differences across datasets that have different naming conventions, annotation resolution, and technical biases. CellTypist v2.0 accurately quantifies cell-cell transcriptomic similarities and enables robust and efficient cross-dataset meta-analyses. Cell types are placed into a relationship graph that hierarchically defines shared and novel cell subtypes. Application to multiple immune datasets confirms its sensitivity and specificity by recapitulating expert-curated cell annotations. We also apply CellTypist to datasets from eight diseases which all lead to pulmonary fibrosis, and reveal underexplored relationships between healthy cell types and a variety of diseased cell states. Furthermore, we present a workflow for fast cross-dataset integration guided by the harmonized cell types at different levels of annotation granularity. Finally, we apply CellTypist to 12 tissue atlases from 38 datasets, and provide a deeply curated cross-tissue database ( www.celltypist.org/organs ) with more than 3.6 million cells and 250 cell types. This is complemented by a large collection of machine learning models for automatic cell type annotation across human tissues." @default.
• W4367680094 created "2023-05-03" @default.
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• W4367680094 date "2023-05-01" @default.
• W4367680094 modified "2023-10-14" @default.
• W4367680094 title "Automatic cell type harmonization and integration across Human Cell Atlas datasets" @default.
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• W4367680094 doi "https://doi.org/10.1101/2023.05.01.538994" @default.
• W4367680094 hasPublicationYear "2023" @default.
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