Model-Based Cluster Analysis Applied to Flow Cytometry Data
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).
KeywordsGreen Alga Bayesian Information Criterion Algal Cell Radial Basis Function Neural Network Determinant Criterion
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- BODDY, L., MORRIS, C.W., WILKINS, M.F., AL-HADDAD, L., TARRAN, G.A., JONKER, R.R., and BURKILL, P.H. (2000): Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data. Marine Ecology Progress Series, 195, 47–59.Google Scholar
- HOFSTRAAT, J.W., ZEIJL VAN, W.J.M., VREEZE DE, M.E.J., PEETERS, J.C.H., PEPERZAK, L., COLIJN, F., and RADEMAKER, T.W.M. (1994): Phytoplankton monitoring by flow cytometry. Journal of Plankton Research 16(9), 1197–1224.Google Scholar
- MACQUEEN, J.B. (1967): Some Methods for Classification and Analysis of Multi-variate Observations. In: L. Lecam and J. Neyman (Eds.): Proc. 5th Berkeley Symp. Math. Statist. Prob., Vol. 1. Univ. California Press, Berkeley, 281–297.Google Scholar
- MUCHA, H.-J, SIMON, U, and BRÜGGEMANN, R. (2002): Model-based Cluster Analysis Applied to Flow Cytometry Data of Phytoplankton. Weierstraß-Institute for Applied Analysis and Stochastic, Technical Report No. 5. http://www.wias-berlin.de/.Google Scholar
- STEINBERG, C.E.W. and BRÜGGEMANN, R. (1998): Integrity of limnic ecosystems. In: J.A. Van de Kraats (Eds.): Let the Fish Speak: The Quality of Aquatic Ecosystems as an Indicator for Sustainable Water Management. EURAQUA: Fourth Technical Report, Koblenz, 89–101.Google Scholar
- WARD, J.H. (1963): Hierarchical Grouping Methods to Optimise an Objective Function. JASA, 58, 235–244.Google Scholar