Functional analysis and classification of phytoplankton based on data from an automated flow cytometer

Autor: Anthony Malkassian, Marc van Dijk, Gérald Grégori, David Nerini, Claude Manté, Melilotus Thyssen
Přispěvatelé: Laboratoire de MicrobiologiE de Géochimie et d'Ecologie Marines (LMGEM), Centre National de la Recherche Scientifique (CNRS)-Université de la Méditerranée - Aix-Marseille 2, MoNOS, Huygens Laboratory, Universiteit Leiden [Leiden], Microbial Ecology (ME), Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS), Universiteit Leiden, Laboratoire de MicrobiologiE de Géochimie et d'Ecologie Marines ( LMGEM ), Centre National de la Recherche Scientifique ( CNRS ) -Université de la Méditerranée - Aix-Marseille 2
Rok vydání: 2011
Předmět:
[ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean
Atmosphere
0106 biological sciences
Multivariate statistics
Histology
multivariate statistics
Fresh Water
Cell Separation
Biology
01 natural sciences
Pathology and Forensic Medicine
03 medical and health sciences
Histogram
Animals
Seawater
14. Life underwater
Cluster analysis
ComputingMilieux_MISCELLANEOUS
Phylogeny
functional data analysis
030304 developmental biology
Automation
Laboratory
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean
Atmosphere
0303 health sciences
Ecology
business.industry
010604 marine biology & hydrobiology
Sampling (statistics)
Functional data analysis
Pattern recognition
Cell Biology
Flow Cytometry
automated flow cytometry
Flow (mathematics)
Phytoplankton
A priori and a posteriori
Artificial intelligence
Scale (map)
business
clustering
Zdroj: Cytometry Part A
Cytometry Part A, Wiley, 2011, 79A, pp.263-275. ⟨10.1002/cyto.a.21035⟩
Cytometry Part A, 79A(4), 263-275. Wiley-Liss Inc.
Cytometry Part A, 2011, 79A, pp.263-275. ⟨10.1002/cyto.a.21035⟩
Cytometry Part A, Wiley, 2011, 79A, pp.263-275. 〈10.1002/cyto.a.21035〉
ISSN: 1552-4922
1552-4930
DOI: 10.1002/cyto.a.21035
Popis: International audience; Analytical flow cytometry (FCM) is well suited for the analysis of phytoplankton communities in fresh and sea waters. The measurement of light scatter and autofluorescence properties of particles by FCM provides optical fingerprints, which enables different phytoplankton groups to be separated. A submersible version of the CytoSense flow cy-tometer (the CytoSub) has been designed for in situ autonomous sampling and analysis , making it possible to monitor phytoplankton at a short temporal scale and obtain accurate information about its dynamics. For data analysis, a manual clustering is usually performed a posteriori: data are displayed on histograms and scatterplots, and group discrimination is made by drawing and combining regions (gating). The purpose of this study is to provide greater objectivity in the data analysis by applying a nonman-ual and consistent method to automatically discriminate clusters of particles. In other words, we seek for partitioning methods based on the optical fingerprints of each particle. As the CytoSense is able to record the full pulse shape for each variable, it quickly generates a large and complex dataset to analyze. The shape, length, and area of each curve were chosen as descriptors for the analysis. To test the developed method, numerical experiments were performed on simulated curves. Then, the method was applied and validated on phytoplankton cultures data. Promising results have been obtained with a mixture of various species whose optical fingerprints overlapped considerably and could not be accurately separated using manual gating. ' 2011 International Society for Advancement of Cytometry
Databáze: OpenAIRE
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