Abstract
Flow cytometry (FC) devices count and measure cells in fluids in an automated procedure. In this paper we present our work in progress on the clustering of FC data. We compare standard clustering algorithms such as K-means, Ward’s clustering, etc., to the more advanced approach of sequential superparamagnetic clustering (SSC). We found Ward’s hierarchical clustering to perform best regarding internal cluster validation measures, while SSC yielded the best results based on the visual inspection of the clustering results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boddy, L., Wilkins, M.F., Morris, C.W.: Pattern recognition in flow cytometry. Cytometry 44(3), 195–209 (2001)
Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. Reports of the Faculty of Mathematics and Informatics. Delft University of Technology, Fac., Univ. (1987)
Legány, C., Juhász, S., Babos, A.: Cluster validity measurement techniques. In: Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, AIKED 2006, pp. 388–393. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point (2006)
Mandy, F.F.: Twenty five years of clinical flow cytometry: Aids accelerated global instrument distribution. Cytometry Part A 58(1), 55–56 (2004)
Ott, T., Kern, A., Steeb, W.H., Stoop, R.: Sequential clustering: tracking down the most natural clusters. Journal of Statistical Mechanics: Theory and Experiment 2005(11), P11014 (2005)
Pomati, F., Jokela, J., Simona, M., Veronesi, M., Ibelings, B.W.: An automated platform for phytoplankton ecology and aquatic ecosystem monitoring. Environmental Science Technology 45, 9658–9665 (2011)
Pomati, F., Kraft, N.J.B., Posch, T., Eugster, B., Jokela, J., Ibelings, B.W.: Individual cell based traits obtained by scanning flow-cytometry show selection by biotic and abiotic environmental factors during a phytoplankton spring bloom. PLoS ONE 8(8), e71677 (2013)
Urano, N., Nomura, M., Sahara, H., Koshino, S.: The use of flow cytometry and small-scale brewing in protoplast fusion: Exclusion of undesired phenotypes in yeasts. Enzyme and Microbial Technology 16(10), 839–843 (1994)
Ward, J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Glüge, S., Pomati, F., Albert, C., Kauf, P., Ott, T. (2014). The Challange of Clustering Flow Cytometry Data from Phytoplankton in Lakes. In: Mladenov, V.M., Ivanov, P.C. (eds) Nonlinear Dynamics of Electronic Systems. NDES 2014. Communications in Computer and Information Science, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-319-08672-9_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-08672-9_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08671-2
Online ISBN: 978-3-319-08672-9
eBook Packages: Computer ScienceComputer Science (R0)