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Automated and reproducible cell identification in mass cytometry using neural networks

The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology...

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Detalles Bibliográficos
Autores principales: Saihi, Hajar, Bessant, Conrad, Alazawi, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630086/
https://www.ncbi.nlm.nih.gov/pubmed/37930029
http://dx.doi.org/10.1093/bib/bbad392
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author Saihi, Hajar
Bessant, Conrad
Alazawi, William
author_facet Saihi, Hajar
Bessant, Conrad
Alazawi, William
author_sort Saihi, Hajar
collection PubMed
description The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration.
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spelling pubmed-106300862023-11-08 Automated and reproducible cell identification in mass cytometry using neural networks Saihi, Hajar Bessant, Conrad Alazawi, William Brief Bioinform Problem Solving Protocol The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration. Oxford University Press 2023-11-02 /pmc/articles/PMC10630086/ /pubmed/37930029 http://dx.doi.org/10.1093/bib/bbad392 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Problem Solving Protocol
Saihi, Hajar
Bessant, Conrad
Alazawi, William
Automated and reproducible cell identification in mass cytometry using neural networks
title Automated and reproducible cell identification in mass cytometry using neural networks
title_full Automated and reproducible cell identification in mass cytometry using neural networks
title_fullStr Automated and reproducible cell identification in mass cytometry using neural networks
title_full_unstemmed Automated and reproducible cell identification in mass cytometry using neural networks
title_short Automated and reproducible cell identification in mass cytometry using neural networks
title_sort automated and reproducible cell identification in mass cytometry using neural networks
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630086/
https://www.ncbi.nlm.nih.gov/pubmed/37930029
http://dx.doi.org/10.1093/bib/bbad392
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