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• W4224290465 abstract "Abstract Scaling scRNA-seq to profile millions of cells is crucial for constructing high-resolution maps of transcriptional manifolds. Current analysis strategies, in particular dimensionality reduction and two-phase clustering, offer only limited scaling and sensitivity to define such manifolds. We introduce Metacell-2, a recursive divide-and-conquer algorithm allowing efficient decomposition of scRNA-seq datasets of any size into small and cohesive groups of cells called metacells. Metacell-2 improves outlier cell detection and rare cell type identification, as shown with human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline." @default.
• W4224290465 created "2022-04-26" @default.
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• W4224290465 date "2022-04-19" @default.
• W4224290465 modified "2023-10-14" @default.
• W4224290465 title "Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis" @default.
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• W4224290465 doi "https://doi.org/10.1186/s13059-022-02667-1" @default.
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