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Cited by 98Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

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

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Ben-Kiki, Oren, Bercovich, Akhiad, Lifshitz, Aviezer, Tanay, Amos·2022·Genome Biology
DOI 10.1186/s13059-022-02667-1PMC PMC9019975

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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.

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  • SEACells and Metacell2 do not account for cell labels in their aggregation procedures, which may result in a number of metacells containing transcriptomes from differently labeled cells.

    “Notably, SEACells and Metacell2 do not account for cell labels in their aggregation procedures, which may result in a number of metacells containing transcriptomes from differently labeled cells.”

    From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [14]

    The Methods sections on graph construction and the divide-and-conquer algorithm describe forming Metacell-2 metacells from gene-expression correlations and graph partitions, without using cell labels. That supports the Metacell-2 portion and makes mixed-label metacells a plausible possibility, but the paper does not establish how many such metacells occur or address SEACells. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • All tested metacell approaches were suitable for downstream network analysis in the CD34+ hematopoietic stem and progenitor cell dataset.

    “We applied hdWGCNA to a dataset of CD34+ hematopoietic stem and progenitor stem cells16 using two additional metacell approaches14,16 and found that all approaches were suitable for downstream network analysis (Figure S2; Data S1).”

    From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [14]

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Research works printed but not linked: 21

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Cited by

Sorted by citations

Authors
Ben-Kiki, O., Bercovich, A., Lifshitz, A., & Tanay, A.
Affiliations
Weizmann Institute of Science (IL)
Corresponding author
Tanay, Amos
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2022-04-19
Volume
23
Issue
1
Pages
100-100
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-022-02667-1
PMC
PMC9019975
OpenAlex
W4224290465
Cited by
98
References
7
Citations by year
2026: 18 · 2025: 27 · 2024: 39 · 2023: 10 · 2022: 3 · 2021: 1
FWCI
6.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
metacells, single-cell RNA sequencing, divide-and-conquer, scalability, dimensionality reduction, two-stage clustering, mouse embryo development, Scanpy
MeSH terms
Exome Sequencing, Algorithms, Animals, Cluster Analysis, Sequence Analysis, RNA, Mice, Single-Cell Analysis

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