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

作者：Oren Ben-Kiki, Akhiad Bercovich, Aviezer Lifshitz, Amos Tanay · 发表于：Genome biology · 年份：2022 · DOI：10.1186/s13059-022-02667-1 · 被引用次数：124 · 研究领域：Single-cell and spatial transcriptomics、Gene expression and cancer classification、Cell Image Analysis Techniques

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.