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GenePattern flow cytometry suite

BACKGROUND: Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the develo...

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Autores principales: Spidlen, Josef, Barsky, Aaron, Breuer, Karin, Carr, Peter, Nazaire, Marc-Danie, Hill, Barbara Allen, Qian, Yu, Liefeld, Ted, Reich, Michael, Mesirov, Jill P, Wilkinson, Peter, Scheuermann, Richard H, Sekaly, Rafick-Pierre, Brinkman, Ryan R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717030/
https://www.ncbi.nlm.nih.gov/pubmed/23822732
http://dx.doi.org/10.1186/1751-0473-8-14
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author Spidlen, Josef
Barsky, Aaron
Breuer, Karin
Carr, Peter
Nazaire, Marc-Danie
Hill, Barbara Allen
Qian, Yu
Liefeld, Ted
Reich, Michael
Mesirov, Jill P
Wilkinson, Peter
Scheuermann, Richard H
Sekaly, Rafick-Pierre
Brinkman, Ryan R
author_facet Spidlen, Josef
Barsky, Aaron
Breuer, Karin
Carr, Peter
Nazaire, Marc-Danie
Hill, Barbara Allen
Qian, Yu
Liefeld, Ted
Reich, Michael
Mesirov, Jill P
Wilkinson, Peter
Scheuermann, Richard H
Sekaly, Rafick-Pierre
Brinkman, Ryan R
author_sort Spidlen, Josef
collection PubMed
description BACKGROUND: Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research. RESULTS: In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines. CONCLUSIONS: GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.
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spelling pubmed-37170302013-07-21 GenePattern flow cytometry suite Spidlen, Josef Barsky, Aaron Breuer, Karin Carr, Peter Nazaire, Marc-Danie Hill, Barbara Allen Qian, Yu Liefeld, Ted Reich, Michael Mesirov, Jill P Wilkinson, Peter Scheuermann, Richard H Sekaly, Rafick-Pierre Brinkman, Ryan R Source Code Biol Med Methodology BACKGROUND: Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research. RESULTS: In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines. CONCLUSIONS: GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser. BioMed Central 2013-07-03 /pmc/articles/PMC3717030/ /pubmed/23822732 http://dx.doi.org/10.1186/1751-0473-8-14 Text en Copyright © 2013 Spidlen et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology
Spidlen, Josef
Barsky, Aaron
Breuer, Karin
Carr, Peter
Nazaire, Marc-Danie
Hill, Barbara Allen
Qian, Yu
Liefeld, Ted
Reich, Michael
Mesirov, Jill P
Wilkinson, Peter
Scheuermann, Richard H
Sekaly, Rafick-Pierre
Brinkman, Ryan R
GenePattern flow cytometry suite
title GenePattern flow cytometry suite
title_full GenePattern flow cytometry suite
title_fullStr GenePattern flow cytometry suite
title_full_unstemmed GenePattern flow cytometry suite
title_short GenePattern flow cytometry suite
title_sort genepattern flow cytometry suite
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717030/
https://www.ncbi.nlm.nih.gov/pubmed/23822732
http://dx.doi.org/10.1186/1751-0473-8-14
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