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Model-Based Cluster Analysis Applied to Flow Cytometry Data

  • Ute Simon
  • Hans-Joachim Mucha
  • Rainer Brüggemann
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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).

Keywords

Green Alga Bayesian Information Criterion Algal Cell Radial Basis Function Neural Network Determinant Criterion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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
  2. CHISOLM, S.W, OLSON, R.J, ZETTLER, E.R., GOERICKE, R., WATER-BURY, J.B., and WELSHMEYER, N.A. (1988): A novel free-living prochlorophyte abundant in the oceanic euphotic zone. Nature, 334, 340–343.CrossRefGoogle Scholar
  3. FRALEY, C. and RAFTERY, A.E. (2002): Model-based Clustering, Discriminant Analysis, and Density Estimation. Journal, of the American Statistical Association, 97, 458, 611–631.MathSciNetCrossRefGoogle Scholar
  4. 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
  5. 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
  6. 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
  7. 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
  8. WARD, J.H. (1963): Hierarchical Grouping Methods to Optimise an Objective Function. JASA, 58, 235–244.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Ute Simon
    • 1
  • Hans-Joachim Mucha
    • 2
  • Rainer Brüggemann
    • 1
  1. 1.Leibniz-Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
  2. 2.Weierstraß-Institute of Applied Analysis and StochasticBerlinGermany

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