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Automated identification of maximal differential cell populations in flow cytometry data

We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is a...

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Detalles Bibliográficos
Autores principales: Yue, Alice, Chauve, Cedric, Libbrecht, Maxwell W., Brinkman, Ryan R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810629/
https://www.ncbi.nlm.nih.gov/pubmed/34559446
http://dx.doi.org/10.1002/cyto.a.24503
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author Yue, Alice
Chauve, Cedric
Libbrecht, Maxwell W.
Brinkman, Ryan R.
author_facet Yue, Alice
Chauve, Cedric
Libbrecht, Maxwell W.
Brinkman, Ryan R.
author_sort Yue, Alice
collection PubMed
description We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice‐based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor.
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spelling pubmed-88106292022-10-14 Automated identification of maximal differential cell populations in flow cytometry data Yue, Alice Chauve, Cedric Libbrecht, Maxwell W. Brinkman, Ryan R. Cytometry A Original Articles We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice‐based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor. John Wiley & Sons, Inc. 2021-10-22 2022-02 /pmc/articles/PMC8810629/ /pubmed/34559446 http://dx.doi.org/10.1002/cyto.a.24503 Text en © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Yue, Alice
Chauve, Cedric
Libbrecht, Maxwell W.
Brinkman, Ryan R.
Automated identification of maximal differential cell populations in flow cytometry data
title Automated identification of maximal differential cell populations in flow cytometry data
title_full Automated identification of maximal differential cell populations in flow cytometry data
title_fullStr Automated identification of maximal differential cell populations in flow cytometry data
title_full_unstemmed Automated identification of maximal differential cell populations in flow cytometry data
title_short Automated identification of maximal differential cell populations in flow cytometry data
title_sort automated identification of maximal differential cell populations in flow cytometry data
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810629/
https://www.ncbi.nlm.nih.gov/pubmed/34559446
http://dx.doi.org/10.1002/cyto.a.24503
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