ANALYZING MULTIVARIATE FLOW CYTOMETRIC DATA IN AQUATIC SCIENCES

Flow cytometry has recently been introduced in aquatic ecology. Its un ique feature is to measure several optical characteristics simultaneou sly on a large number of cells. Until now, these data have generally b een analyzed in simple ways, e.g., frequency histograms and bivariate scatter diagrams, so that the multivariate potential of the data has n ot been fully exploited. This paper presents a way of answering ecolog ically meaningful questions, using the multivariate characteristics of the data. In order to do so, the multivariate data are reduced to a s mall number of classes by clustering, which reduces the data to a cate gorical variable. Multivariate pairwise comparisons can then be perfor med among samples using these new data vectors. The test case presente d in the paper forms a time series of observations from which the new method enables us to study on the temporal evolution of cell types.