Imperial College London
Alternate

ProfessorJorgeFerrer

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Chair in Medicine and Genetics
 
 
 
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Contact

 

+44 (0)20 7594 0968j.ferrer

 
 
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Location

 

535ICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Mereu:2018:10.1101/314831,
author = {Mereu, E and Iacono, G and Guillaumet-Adkins, A and Moutinho, C and Lunazzi, G and Santos, CP and Miguel-Escalada, I and Ferrer, J and Real, FX and Gut, I and Heyn, H},
doi = {10.1101/314831},
title = {matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments},
url = {http://dx.doi.org/10.1101/314831},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>Abstract</jats:title><jats:p>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 <jats:italic>matchSCore (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/elimereu/matchSCore">https://github.com/elimereu/matchSCore</jats:ext-link>)</jats:italic>, 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 <jats:italic>matchSCore</jats:italic> 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.</jats:p>
AU  - Mereu,E
AU  - Iacono,G
AU  - Guillaumet-Adkins,A
AU  - Moutinho,C
AU  - Lunazzi,G
AU  - Santos,CP
AU  - Miguel-Escalada,I
AU  - Ferrer,J
AU  - Real,FX
AU  - Gut,I
AU  - Heyn,H
DO  - 10.1101/314831
PY  - 2018///
TI  - matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments
UR  - http://dx.doi.org/10.1101/314831
ER  -
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