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FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test

Yun Zhang, Brian D. Aevermann, Trygve E. Bakken, Jeremy A. Miller, Rebecca D. Hodge, Ed S. Lein, Richard H. Scheuermann
doi: https://doi.org/10.1101/2020.05.01.073445
Yun Zhang
1J. Craig Venter Institute, La Jolla, CA, USA
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Brian D. Aevermann
1J. Craig Venter Institute, La Jolla, CA, USA
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Trygve E. Bakken
2Allen Institute for Brain Science, Seattle, WA, USA
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Jeremy A. Miller
2Allen Institute for Brain Science, Seattle, WA, USA
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Rebecca D. Hodge
2Allen Institute for Brain Science, Seattle, WA, USA
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Ed S. Lein
2Allen Institute for Brain Science, Seattle, WA, USA
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Richard H. Scheuermann
1J. Craig Venter Institute, La Jolla, CA, USA
3Department of Pathology, University of California San Diego, La Jolla, CA, USA
4Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, USA
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  • For correspondence: rscheuermann@jcvi.org
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Abstract

Single cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method – FR-Match – that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 29, 2020.
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FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test
Yun Zhang, Brian D. Aevermann, Trygve E. Bakken, Jeremy A. Miller, Rebecca D. Hodge, Ed S. Lein, Richard H. Scheuermann
bioRxiv 2020.05.01.073445; doi: https://doi.org/10.1101/2020.05.01.073445
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FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test
Yun Zhang, Brian D. Aevermann, Trygve E. Bakken, Jeremy A. Miller, Rebecca D. Hodge, Ed S. Lein, Richard H. Scheuermann
bioRxiv 2020.05.01.073445; doi: https://doi.org/10.1101/2020.05.01.073445

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