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Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data

The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of a...

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Autores principales: Liu, Peng, Liu, Silvia, Fang, Yusi, Xue, Xiangning, Zou, Jian, Tseng, George, Konnikova, Liza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198724/
https://www.ncbi.nlm.nih.gov/pubmed/32411698
http://dx.doi.org/10.3389/fcell.2020.00234
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author Liu, Peng
Liu, Silvia
Fang, Yusi
Xue, Xiangning
Zou, Jian
Tseng, George
Konnikova, Liza
author_facet Liu, Peng
Liu, Silvia
Fang, Yusi
Xue, Xiangning
Zou, Jian
Tseng, George
Konnikova, Liza
author_sort Liu, Peng
collection PubMed
description The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches.
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spelling pubmed-71987242020-05-14 Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data Liu, Peng Liu, Silvia Fang, Yusi Xue, Xiangning Zou, Jian Tseng, George Konnikova, Liza Front Cell Dev Biol Cell and Developmental Biology The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches. Frontiers Media S.A. 2020-04-28 /pmc/articles/PMC7198724/ /pubmed/32411698 http://dx.doi.org/10.3389/fcell.2020.00234 Text en Copyright © 2020 Liu, Liu, Fang, Xue, Zou, Tseng and Konnikova. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Liu, Peng
Liu, Silvia
Fang, Yusi
Xue, Xiangning
Zou, Jian
Tseng, George
Konnikova, Liza
Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data
title Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data
title_full Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data
title_fullStr Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data
title_full_unstemmed Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data
title_short Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data
title_sort recent advances in computer-assisted algorithms for cell subtype identification of cytometry data
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198724/
https://www.ncbi.nlm.nih.gov/pubmed/32411698
http://dx.doi.org/10.3389/fcell.2020.00234
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