Abstract
Flow cytometry is an appropriate technique for the investigation and monitoring of phytoplankton (algae), providing quick, semi-automatic single-cell analysis. However, to use flow cytometry in phytoplankton research routinely, an objective and automated i.e. computer-supported data analysis is demanded. For this reason, in a pilot study a sequence of different steps of cluster analysis has been developed, including model-based and hierarchical clustering, as well as the concept of cores and weighting of observations and parameters. A successful application of the method is demonstrated for a snapshot of a sample of Lake Müggelsee in Berlin (Germany).
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Simon, U., Mucha, HJ., Brüggemann, R. (2005). Model-Based Cluster Analysis Applied to Flow Cytometry Data. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_9
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DOI: https://doi.org/10.1007/3-540-26981-9_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23221-6
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