The challange of clustering flow cytometry data from phytoplankton in lakes

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.