Cited by 383Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor

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Crow, Megan, Paul, Anirban, Ballouz, Sara, Huang, Z. Josh, Gillis, Jesse·2018·Nature Communications

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Abstract

Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover and characterize cell types; however, the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine its replicability. Meta-analysis is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that quantifies the degree to which cell types replicate across datasets, and enables rapid identification of clusters with high similarity. We first measure the replicability of neuronal identity, comparing results across eight technically and biologically diverse datasets to define best practices for more complex assessments. We then apply this to novel interneuron subtypes, finding that 24/45 subtypes have evidence of replication, which enables the identification of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types with high accuracy, suggesting a general route forward for large-scale evaluation of scRNA-seq data.

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  • Paul et al. applied MetaNeighbour to screen about 620 HGNC gene families for those whose differential single-cell expression reliably distinguish the PCPs.

    “Paul et al.28 applied a supervised machine learning-based algorithm (MetaNeighbour50) to screen the ~620 HGNC (Human Genome Nomenclature Committee) gene families for those whose differential single-cell expression reliably distinguish these PCPs.”
  • A rigorous replicability test learns a cell type's transcriptomic signature from one or more datasets and predicts identities in an independent dataset, yielding an AUC score for cluster matching.

    “A stringent test of cell type replicability uses transcriptomic signatures of a cell type learned from one or more datasets to try to predict the identity of cells in an independent dataset, providing a statistical score (area under the ROC curve) that quantifies the match between clusters (Crow et al., 2018).”

    From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Crow et al., 2018

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Authors
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J.
Affiliations
Cold Spring Harbor Laboratory (US)
Corresponding author
Gillis, Jesse
Venue
Nature Communications
Published
2018-02-28
Volume
9
Issue
1
Pages
884-884
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2804847416
Cited by
365
References
59
Citations by year
2026: 51 · 2025: 51 · 2024: 68 · 2023: 45 · 2022: 30 · 2021: 26 · 2020: 44 · 2019: 32 · 2018: 14 · 2017: 2 · 2015: 1
FWCI
10.94
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
  • UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Alliance for Research on Schizophrenia and Depression
  • Cold Spring Harbor Laboratory
  • National Institutes of Health (1f32mh114501-01)
  • National Institute of Mental Health (R01MH094705, R01MH109665, R01MH113005, F32-MH114501)
  • U.S. National Library of Medicine (R01LM012736)
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
replicability, single-cell RNA sequencing, cell type annotation, meta-analysis, neuronal identity, interneuron subtypes, marker genes, cluster similarity, technical bias
MeSH terms
Humans, Neurons, RNA, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Single-Cell Analysis

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