How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed...

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Autores principales:	Hannah den Braanker, Margot Bongenaar, Erik Lubberts
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	spectral flow cytometry machine learning workflow data analysis - methods R Immunologic diseases. Allergy RC581-607
Acceso en línea:	https://doaj.org/article/329e841f3f30488188d05aba33aeed9e
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spelling	oai:doaj.org-article:329e841f3f30488188d05aba33aeed9e2021-11-19T06:26:07ZHow to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow1664-322410.3389/fimmu.2021.768113https://doaj.org/article/329e841f3f30488188d05aba33aeed9e2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fimmu.2021.768113/fullhttps://doaj.org/toc/1664-3224Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.Hannah den BraankerHannah den BraankerHannah den BraankerMargot BongenaarMargot BongenaarErik LubbertsErik LubbertsFrontiers Media S.A.articlespectral flow cytometrymachine learningworkflowdata analysis - methodsRImmunologic diseases. AllergyRC581-607ENFrontiers in Immunology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	spectral flow cytometry machine learning workflow data analysis - methods R Immunologic diseases. Allergy RC581-607
spellingShingle	spectral flow cytometry machine learning workflow data analysis - methods R Immunologic diseases. Allergy RC581-607 Hannah den Braanker Hannah den Braanker Hannah den Braanker Margot Bongenaar Margot Bongenaar Erik Lubberts Erik Lubberts How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow
description	Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.
format	article
author	Hannah den Braanker Hannah den Braanker Hannah den Braanker Margot Bongenaar Margot Bongenaar Erik Lubberts Erik Lubberts
author_facet	Hannah den Braanker Hannah den Braanker Hannah den Braanker Margot Bongenaar Margot Bongenaar Erik Lubberts Erik Lubberts
author_sort	Hannah den Braanker
title	How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow
title_short	How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow
title_full	How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow
title_fullStr	How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow
title_full_unstemmed	How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow
title_sort	how to prepare spectral flow cytometry datasets for high dimensional data analysis: a practical workflow
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/329e841f3f30488188d05aba33aeed9e
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How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

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