Supervised classification enables rapid annotation of cell atlases

Single-cell molecular profiling technologies are gaining rapid traction, but the manual process by which resulting cell types are typically annotated is labor intensive and rate-limiting. We describe Garnett, a tool for rapidly annotating cell types in single-cell transcriptional profiling and single-cell chromatin accessibility datasets, based on an interpretable, hierarchical markup language of cell type-specific genes. Garnett successfully classifies cell types in tissue and whole organism datasets, as well as across species. Garnett uses a hierarchical markup language and machine learning to define cell types and their marker genes and identifies these cell types in scRNA-seq datasets from tissues and whole organisms and across species.

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