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Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline
Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technic...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035035/ https://www.ncbi.nlm.nih.gov/pubmed/27662185 http://dx.doi.org/10.1371/journal.pcbi.1005112 |
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author | Chen, Hao Lau, Mai Chan Wong, Michael Thomas Newell, Evan W. Poidinger, Michael Chen, Jinmiao |
author_facet | Chen, Hao Lau, Mai Chan Wong, Michael Thomas Newell, Evan W. Poidinger, Michael Chen, Jinmiao |
author_sort | Chen, Hao |
collection | PubMed |
description | Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14(−)CD19(−) PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4(+) T cell dataset, cytofkit uncovered multiple subtypes of T(FH) cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis. |
format | Online Article Text |
id | pubmed-5035035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50350352016-10-10 Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline Chen, Hao Lau, Mai Chan Wong, Michael Thomas Newell, Evan W. Poidinger, Michael Chen, Jinmiao PLoS Comput Biol Research Article Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14(−)CD19(−) PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4(+) T cell dataset, cytofkit uncovered multiple subtypes of T(FH) cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis. Public Library of Science 2016-09-23 /pmc/articles/PMC5035035/ /pubmed/27662185 http://dx.doi.org/10.1371/journal.pcbi.1005112 Text en © 2016 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Chen, Hao Lau, Mai Chan Wong, Michael Thomas Newell, Evan W. Poidinger, Michael Chen, Jinmiao Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline |
title | Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline |
title_full | Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline |
title_fullStr | Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline |
title_full_unstemmed | Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline |
title_short | Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline |
title_sort | cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035035/ https://www.ncbi.nlm.nih.gov/pubmed/27662185 http://dx.doi.org/10.1371/journal.pcbi.1005112 |
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