Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies...

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Autores principales:	Andrew Cron, Cécile Gouttefangeas, Jacob Frelinger, Lin Lin, Satwinder K Singh, Cedrik M Britten, Marij J P Welters, Sjoerd H van der Burg, Mike West, Cliburn Chan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/06513624c96542608300c537b0a8b6af
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spelling	oai:doaj.org-article:06513624c96542608300c537b0a8b6af2021-11-18T05:52:01ZHierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.1553-734X1553-735810.1371/journal.pcbi.1003130https://doaj.org/article/06513624c96542608300c537b0a8b6af2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23874174/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.Andrew CronCécile GouttefangeasJacob FrelingerLin LinSatwinder K SinghCedrik M BrittenMarij J P WeltersSjoerd H van der BurgMike WestCliburn ChanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 7, p e1003130 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Andrew Cron Cécile Gouttefangeas Jacob Frelinger Lin Lin Satwinder K Singh Cedrik M Britten Marij J P Welters Sjoerd H van der Burg Mike West Cliburn Chan Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
description	Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.
format	article
author	Andrew Cron Cécile Gouttefangeas Jacob Frelinger Lin Lin Satwinder K Singh Cedrik M Britten Marij J P Welters Sjoerd H van der Burg Mike West Cliburn Chan
author_facet	Andrew Cron Cécile Gouttefangeas Jacob Frelinger Lin Lin Satwinder K Singh Cedrik M Britten Marij J P Welters Sjoerd H van der Burg Mike West Cliburn Chan
author_sort	Andrew Cron
title	Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
title_short	Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
title_full	Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
title_fullStr	Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
title_full_unstemmed	Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
title_sort	hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/06513624c96542608300c537b0a8b6af
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_version_	1718424708949475328

Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

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