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Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor

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Abstract

Single-cell RNA-sequencing data have significantly advanced the characterization of cell-type diversity and composition. However, cell-type definitions vary across data and analysis pipelines, raising concerns about cell-type validity and generalizability. With MetaNeighbor, we proposed an efficient and robust quantification of cell-type replicability that preserves dataset independence and is highly scalable compared to dataset integration. In this protocol, we show how MetaNeighbor can be used to characterize cell-type replicability by following a simple three-step procedure: gene filtering, neighbor voting and visualization. We show how these steps can be tailored to quantify cell-type replicability, determine gene sets that contribute to cell-type identity and pretrain a model on a reference taxonomy to rapidly assess newly generated data. The protocol is based on an open-source R package available from Bioconductor and GitHub, requires basic familiarity with Rstudio or the R command line and can typically be run in <5 min for millions of cells.

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Fig. 1: MetaNeighbor quantifies and characterizes cell-type replicability.
Fig. 2: Cell types from four pancreas datasets cluster according to their biological similarity.
Fig. 3: Restricting the four pancreas datasets to endocrine subtypes allows for a more stringent replicability assessment.
Fig. 4: 1-vs-best AUROCs automatically identify each cell type’s closest outgroup.
Fig. 5: Replicating cell types can be extracted as meta-clusters.
Fig. 6: Assessment of cell-type annotations from the mouse primary visual cortex against reference neuron taxonomy from the primary motor cortex (medium resolution).
Fig. 7: Assessment of inhibitory cell types from the mouse primary visual cortex against reference inhibitory cell types (high resolution).
Fig. 8: 1-vs-best AUROCs enable rapid identification of 1:1 hits and 1:n hits.
Fig. 9: A small fraction of functional gene sets contributes highly to cell-type replicability.
Fig. 10: Top-scoring gene sets can be broken down into characteristic genes for each cell type.
Fig. 11: Selection of a bad highly variable gene set leads to suboptimal performance and obscures biological signal.
Fig. 12: Absence of biological overlap between datasets leads to almost random performance and lack of hierarchical cell-type structure.
Fig. 13: Disrupting formatting of cell type names in pre-trained models leads to random performance.
Fig. 14: MetaNeighbor results are robust to batch effects.
Fig. 15: MetaNeighbor finds replicable cell types in a multimodal dataset of the mouse primary motor cortex.
Fig. 16: MetaNeighbor AUROCs offer a generalizable and batch-effect-free quantification of cell-type similarity.

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Data availability

The datasets analyzed in the protocol are all previously published and publicly available. Human pancreas datasets were from Baron et al.33 (Gene Expression Omnibus (GEO) accession code GSE84133), Lawlor et al.34 (GEO accession code GSE86473), Muraro et al.35 (GEO accession code GSE85241) and Segerstolpe et al.36 (ArrayExpress accession code E-MTAB-5061). These datasets are accessed through the Bioconductor scRNAseq library in the protocol. The mouse primary visual cortex dataset was from Tasic et al.32 (GEO accession code GSE71585), accessed through the Bioconductor scRNAseq library. The BICCN dataset for the mouse primary motor cortex from Yao et al.4 is available on the Neuroscience Multi-Omic archive (https://assets.nemoarchive.org/dat-ch1nqb7). The subset of the BICCN data necessary to run the protocol is also available on FigShare at https://doi.org/10.6084/m9.figshare.13020569 (R version) and https://doi.org/10.6084/m9.figshare.13034171 (Python version).

Code availability

The code for the procedures (including all figures) is freely available on GitHub at https://github.com/gillislab/MetaNeighbor-Protocol in multiple formats (Rmd, PDF and jupyter notebook for R and Python). The scripts used to generate the protocol data are available in the same repository. The stable R version of MetaNeighbor is available through Bioconductor (https://www.bioconductor.org/install/) at https://www.bioconductor.org/packages/release/bioc/html/MetaNeighbor.html (the protocol was generated by using version 3.12), and the development versions are available on GitHub at https://github.com/gillislab/MetaNeighbor (R version) and https://github.com/gillislab/pyMN (Python version).

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Acknowledgements

J.G. was supported by NIH grants R01MH113005 and R01LM012736. S.F. was supported by NIH grant U19MH114821. B.D.H. was supported by the CSHL Crick Cray Fellowship. M.C. was supported by NIH grant K99MH120050.

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Author notes

  1. These authors contributed equally: Stephan Fischer, Megan Crow.

Authors and Affiliations

  1. Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Stephan Fischer, Megan Crow, Benjamin D. Harris & Jesse Gillis

  2. Watson School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Benjamin D. Harris & Jesse Gillis

Authors
  1. Stephan Fischer
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  2. Megan Crow
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Contributions

S.F., M.C., B.D.H. and J.G. designed the experiments, performed the data analysis and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jesse Gillis.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Praneet Chaturvedi, Guoji Guo, Ahmed Mahfouz, Nathan Salomonis and Daniel Schnell for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Crow, M. et al. Nat. Commun. 9, 884 (2018): https://doi.org/10.1038/s41467-018-03282-0

Paul, A. et al. Cell 171, 522–539.e20 (2017): https://doi.org/10.1016/j.cell.2017.08.032

Yao, Z. et al. Preprint at bioRxiv (2020): https://doi.org/10.1101/2020.02.29.970558

Bakken, T. E. et al. Preprint at bioRxiv (2020): https://doi.org/10.1101/2020.03.31.016972

Key data used in this protocol

Yao, Z. et al. Preprint at bioRxiv (2020) https://doi.org/10.1101/2020.02.29.970558

Baron, M. et al. Cell Syst. 3, 346–360.e4 (2016) https://doi.org/10.1016/j.cels.2016.08.011

Lawlor, N. et al. Genome Res. 27, 208–222 (2017) https://doi.org/10.1101/gr.212720.116

Muraro, M. J. et al. Cell Syst. 3, 385–394.e3 (2016) https://doi.org/10.1016/j.cels.2016.09.002

Segerstolpe, Å. et al. Cell Metab. 24, 593–607 (2016) https://doi.org/10.1016/j.cmet.2016.08.020

Tasic, B. et al. Nat. Neurosci. 19, 335–346 (2016) https://doi.org/10.1038/nn.4216

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Fischer, S., Crow, M., Harris, B.D. et al. Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor. Nat Protoc 16, 4031–4067 (2021). https://doi.org/10.1038/s41596-021-00575-5

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