Open AccessAnalyzing multivariate flow cytometric data in aquatic sciences
Author(s) -
Demers Serge,
Kim Junhyong,
Legendre Pierre,
Legendre Louis
Publication year - 1992
Publication title -
cytometry
Language(s) - English
Resource type - Journals
eISSN - 1097-0320
pISSN - 0196-4763
DOI - 10.1002/cyto.990130311
Subject(s) - multivariate statistics , categorical variable , pairwise comparison , multivariate analysis , bivariate analysis , computer science , cluster analysis , histogram , feature (linguistics) , measure (data warehouse) , hierarchical clustering , data mining , statistics , artificial intelligence , mathematics , image (mathematics) , machine learning , linguistics , philosophy
Flow cytometry has recently been introduced in aquatic ecology. Its unique feature is to measure several optical characteristics simultaneously on a large number of cells. Until now, these data have generally been analyzed in simple ways, e. g., frequency histograms and bivariate scatter diagrams, so that the multivariate potential of the data has not been fully exploited. This paper presents a way of answering ecologically meaningful questions, using the multivariate characteristics of the data. In order to do so, the multivariate data are reduced to a small number of classes by clustering, which reduces the data to a categorical variable. Multivariate pairwise comparisons can then be performed among samples using these new data vectors. The test case presented 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.