Peisach M
Neurotoxicology. 1983 Fall;4(3):48-68.
In the search for correlation between elements or among the analyzed samples, much time and effort can be devoted to the examination of multielemental data when the elements are taken two at a time, especially when the number of analyzed elements is relatively large. By representing the samples as points in an n-dimensional space, in which the coordinates are proportional to the elemental content, an overall view of all the available information may be obtained by visualization of the interrelationships among the points through several of the multivariate analysis methods that have been described. Such visualization is a useful step in the classification of samples into groups, particularly so when no a priori group criteria existed. The true significance of the grouping often becomes evident when other known information is superimposed on the multielemental analysis plots. Indeed, though it is not an essential part of the evaluation process, the importance of visualization of multiparameter data has been stressed most strongly(10), because in this way, the area for further intensive statistical analysis can be clearly demarcated.
在寻找元素之间或分析样本之间的相关性时，当每次取两个元素来检查多元素数据时，尤其是当分析元素的数量相对较大时，可能会花费大量的时间和精力。通过将样本表示为n维空间中的点，其中坐标与元素含量成比例，可以通过几种已描述的多变量分析方法来可视化这些点之间的相互关系，从而获得所有可用信息的总体视图。这种可视化是将样本分类成组的有用步骤，特别是在不存在先验分组标准的情况下。当将其他已知信息叠加到多元素分析图上时，分组的真正意义往往变得明显。事实上，虽然可视化不是评估过程的必要部分，但多参数数据可视化的重要性已得到最强烈的强调(10)，因为通过这种方式，可以清楚地划定进一步进行深入统计分析的领域。
