Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor
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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.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [50]
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)
- PMC
- PMC5830442
- 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
- gold
- doi.org
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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