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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
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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. |
format | Online Article Text |
id | pubmed-8810629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
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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