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 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…

• SF
  Schmidt Futures
• WT
  Wellcome Trust

  Awards: 220540/Z/20/A, R-TEC-001, 206194
• AT
  Alan Turing Institute
• NI
  National Institutes of Health
• EA
  Engineering and Physical Sciences Research Council