FlowAnd: Comprehensive Computational Framework for Flow Cytometry Data Analysis

Anna-Maria Kristiina Lahesmaa-Korpinen, Sari Jalkanen, Ping Chen, Erkka Antero Valo, Javier Nunez-Fontarnau, Ville Rantanen, Ali Oghabian, Jukka Matti Vakkila, Kimmo Porkka, Satu Mustjoki, Sampsa Hautaniemi

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Flow cytometry is a widely used high-throughput measurement technology in basic research and diagnostics. Recently the amount of data generated from flow cytometry experiments has been increasing, both in sample numbers and the number of parameters measured per cell. These highly multivariate datasets have become too large for use with tools depending mainly on manual analysis.

We have implemented a computational framework (FlowAnd) that is designed to analyze and integrate largescale, multi-color flow cytometry data. The tool implements methods for data importing, various transformations, several clustering algorithms for automatic clustering, visualization tools as well as straightforward statistical testing. We applied FlowAnd to a phosphoproteomics data set from 37 chronic myeloid leukemia patients treated with two kinase inhibitors. Our results indicate high concordance between automated gating using three clustering algorithms and manual gating. Analysis of more than 70 flow cytometry experiments demonstrate the utility of features in FlowAnd, such as a graphical tool for rapid validation of clustering results, in large-scale flow cytometry data analysis.

The FlowAnd framework allows accurate, fast and well documented analysis of multidimensional flow cytometry experiments. It provides several clustering algorithms for automatic gating, the possibility to add novel tools in various programming languages, such as Java, R, Python or MATLAB in an environment amenable to high-performance computing. FlowAnd can also be easily modified to comply with various marker panels and parameter settings. FlowAnd, all data and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/flowand.
Original languageEnglish
JournalJournal of Proteomics and Bioinformatics
Volume4
Issue number11
Pages (from-to)245-249
ISSN0974-276X
DOIs
Publication statusPublished - 2011
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 3111 Biomedicine
  • bioinformatics
  • flow cytometry
  • 3121 Internal medicine

Cite this

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title = "FlowAnd: Comprehensive Computational Framework for Flow Cytometry Data Analysis",
abstract = "Flow cytometry is a widely used high-throughput measurement technology in basic research and diagnostics. Recently the amount of data generated from flow cytometry experiments has been increasing, both in sample numbers and the number of parameters measured per cell. These highly multivariate datasets have become too large for use with tools depending mainly on manual analysis. We have implemented a computational framework (FlowAnd) that is designed to analyze and integrate largescale, multi-color flow cytometry data. The tool implements methods for data importing, various transformations, several clustering algorithms for automatic clustering, visualization tools as well as straightforward statistical testing. We applied FlowAnd to a phosphoproteomics data set from 37 chronic myeloid leukemia patients treated with two kinase inhibitors. Our results indicate high concordance between automated gating using three clustering algorithms and manual gating. Analysis of more than 70 flow cytometry experiments demonstrate the utility of features in FlowAnd, such as a graphical tool for rapid validation of clustering results, in large-scale flow cytometry data analysis. The FlowAnd framework allows accurate, fast and well documented analysis of multidimensional flow cytometry experiments. It provides several clustering algorithms for automatic gating, the possibility to add novel tools in various programming languages, such as Java, R, Python or MATLAB in an environment amenable to high-performance computing. FlowAnd can also be easily modified to comply with various marker panels and parameter settings. FlowAnd, all data and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/flowand.",
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author = "Lahesmaa-Korpinen, {Anna-Maria Kristiina} and Sari Jalkanen and Ping Chen and Valo, {Erkka Antero} and Javier Nunez-Fontarnau and Ville Rantanen and Ali Oghabian and Vakkila, {Jukka Matti} and Kimmo Porkka and Satu Mustjoki and Sampsa Hautaniemi",
year = "2011",
doi = "10.4172/jpb.1000197",
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FlowAnd: Comprehensive Computational Framework for Flow Cytometry Data Analysis. / Lahesmaa-Korpinen, Anna-Maria Kristiina; Jalkanen, Sari; Chen, Ping; Valo, Erkka Antero; Nunez-Fontarnau, Javier; Rantanen, Ville; Oghabian, Ali; Vakkila, Jukka Matti; Porkka, Kimmo; Mustjoki, Satu; Hautaniemi, Sampsa.

In: Journal of Proteomics and Bioinformatics, Vol. 4, No. 11, 2011, p. 245-249.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - FlowAnd: Comprehensive Computational Framework for Flow Cytometry Data Analysis

AU - Lahesmaa-Korpinen, Anna-Maria Kristiina

AU - Jalkanen, Sari

AU - Chen, Ping

AU - Valo, Erkka Antero

AU - Nunez-Fontarnau, Javier

AU - Rantanen, Ville

AU - Oghabian, Ali

AU - Vakkila, Jukka Matti

AU - Porkka, Kimmo

AU - Mustjoki, Satu

AU - Hautaniemi, Sampsa

PY - 2011

Y1 - 2011

N2 - Flow cytometry is a widely used high-throughput measurement technology in basic research and diagnostics. Recently the amount of data generated from flow cytometry experiments has been increasing, both in sample numbers and the number of parameters measured per cell. These highly multivariate datasets have become too large for use with tools depending mainly on manual analysis. We have implemented a computational framework (FlowAnd) that is designed to analyze and integrate largescale, multi-color flow cytometry data. The tool implements methods for data importing, various transformations, several clustering algorithms for automatic clustering, visualization tools as well as straightforward statistical testing. We applied FlowAnd to a phosphoproteomics data set from 37 chronic myeloid leukemia patients treated with two kinase inhibitors. Our results indicate high concordance between automated gating using three clustering algorithms and manual gating. Analysis of more than 70 flow cytometry experiments demonstrate the utility of features in FlowAnd, such as a graphical tool for rapid validation of clustering results, in large-scale flow cytometry data analysis. The FlowAnd framework allows accurate, fast and well documented analysis of multidimensional flow cytometry experiments. It provides several clustering algorithms for automatic gating, the possibility to add novel tools in various programming languages, such as Java, R, Python or MATLAB in an environment amenable to high-performance computing. FlowAnd can also be easily modified to comply with various marker panels and parameter settings. FlowAnd, all data and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/flowand.

AB - Flow cytometry is a widely used high-throughput measurement technology in basic research and diagnostics. Recently the amount of data generated from flow cytometry experiments has been increasing, both in sample numbers and the number of parameters measured per cell. These highly multivariate datasets have become too large for use with tools depending mainly on manual analysis. We have implemented a computational framework (FlowAnd) that is designed to analyze and integrate largescale, multi-color flow cytometry data. The tool implements methods for data importing, various transformations, several clustering algorithms for automatic clustering, visualization tools as well as straightforward statistical testing. We applied FlowAnd to a phosphoproteomics data set from 37 chronic myeloid leukemia patients treated with two kinase inhibitors. Our results indicate high concordance between automated gating using three clustering algorithms and manual gating. Analysis of more than 70 flow cytometry experiments demonstrate the utility of features in FlowAnd, such as a graphical tool for rapid validation of clustering results, in large-scale flow cytometry data analysis. The FlowAnd framework allows accurate, fast and well documented analysis of multidimensional flow cytometry experiments. It provides several clustering algorithms for automatic gating, the possibility to add novel tools in various programming languages, such as Java, R, Python or MATLAB in an environment amenable to high-performance computing. FlowAnd can also be easily modified to comply with various marker panels and parameter settings. FlowAnd, all data and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/flowand.

KW - 3111 Biomedicine

KW - bioinformatics

KW - flow cytometry

KW - 3121 Internal medicine

U2 - 10.4172/jpb.1000197

DO - 10.4172/jpb.1000197

M3 - Article

VL - 4

SP - 245

EP - 249

JO - Journal of Proteomics and Bioinformatics

JF - Journal of Proteomics and Bioinformatics

SN - 0974-276X

IS - 11

ER -