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matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments

View ORCID ProfileElisabetta Mereu, Giovanni Iacono, Amy Guillaumet-Adkins, Catia Moutinho, Giulia Lunazzi, Catarina Santos, Irene Miguel-Escalada, Jorge Ferrer, Francisco X Real, Ivo Gut, Holger Heyn
doi: https://doi.org/10.1101/314831
Elisabetta Mereu
CNAG-CRG;
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  • ORCID record for Elisabetta Mereu
Giovanni Iacono
CNAG-CRG;
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Amy Guillaumet-Adkins
CNAG-CRG;
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Catia Moutinho
CNAG-CRG;
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Giulia Lunazzi
CNAG-CRG;
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Catarina Santos
CNIO;
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Irene Miguel-Escalada
IDIBAPS;
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Jorge Ferrer
Imperial College London
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Francisco X Real
CNIO;
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Ivo Gut
CNAG-CRG;
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Holger Heyn
CNAG-CRG;
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  • For correspondence: holger.heyn@cnag.crg.eu
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Abstract

Single-cell transcriptomics allows the identification of cellular types, subtypes and states through cell clustering. In this process, similar cells are grouped before determining co-expressed marker genes for phenotype inference. The performance of computational tools is directly associated to their marker identification accuracy, but the lack of an optimal solution challenges a systematic method comparison. Moreover, phenotypes from different studies are challenging to integrate, due to varying resolution, methodology and experimental design. In this work we introduce matchSCore (https://github.com/elimereu/matchSCore), an approach to match cell populations fast across tools, experiments and technologies. We compared 14 computational methods and evaluated their accuracy in clustering and gene marker identification in simulated data sets. We further used matchSCore to project cell type identities across mouse and human cell atlas projects. Despite originating from different technologies, cell populations could be matched across data sets, allowing the assignment of clusters to reference maps and their annotation.

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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 May 07, 2018.
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matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments
Elisabetta Mereu, Giovanni Iacono, Amy Guillaumet-Adkins, Catia Moutinho, Giulia Lunazzi, Catarina Santos, Irene Miguel-Escalada, Jorge Ferrer, Francisco X Real, Ivo Gut, Holger Heyn
bioRxiv 314831; doi: https://doi.org/10.1101/314831
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matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments
Elisabetta Mereu, Giovanni Iacono, Amy Guillaumet-Adkins, Catia Moutinho, Giulia Lunazzi, Catarina Santos, Irene Miguel-Escalada, Jorge Ferrer, Francisco X Real, Ivo Gut, Holger Heyn
bioRxiv 314831; doi: https://doi.org/10.1101/314831

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