ANALYZING MULTIVARIATE FLOW CYTOMETRIC DATA IN AQUATIC SCIENCES
ANALYZING MULTIVARIATE FLOW CYTOMETRIC DATA IN AQUATIC SCIENCES
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DOI：
10.1002/cyto.990130311
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发表时间：
1992-01-01
期刊：
影响因子：
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通讯作者：
LEGENDRE, L
中科院分区：
文献类型：
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作者：
DEMERS, S;KIM, J;LEGENDRE, L
流式细胞术最近被引入水生生态学。它的独特之处在于可以同时测量大量细胞的多种光学特性。到目前为止，这些数据通常以简单的方式进行分析，例如，频率直方图和双变量散点图，因此数据的多变量潜力尚未得到充分利用。本文提出了一种方法来回答生态意义的问题，利用数据的多元特征。为了做到这一点，通过聚类将多变量数据减少到少量的类，这将数据减少到分类变量。然后可以使用这些新的数据向量在样本之间进行多变量成对比较。本文提出的测试用例形成了一个时间序列的观察，新方法使我们能够研究细胞类型的时间演变。
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.