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Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis.

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.

SUBMITTER: Ben-Kiki O

PROVIDER: S-EPMC9019975 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis.

Ben-Kiki Oren O Bercovich Akhiad A Lifshitz Aviezer A Tanay Amos A

Genome biology 20220419 1

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 ...[more]

PMID: 35440087

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