Gomes P, Cassanas G, Bingham C, Halberg F, Lakatua D, Haus E, Uezono K, Ueno M, Matsuoka M, Kawasaki T
Prog Clin Biol Res. 1987;227B:521-32.
The technique of principal component (PC) analysis (PCA) of multivariate observations is a method that allows dimension reduction of multivariate data for further analysis. It is here introduced as a means of selecting chronobiologically important variables that can be further studied by an analysis of variance. The use of PCA is illustrated for a study of major temporal sources of human endocrine variability. Contributions to temporal variability by seven steroidal and six nonsteroidal hormones are compared in samples available at 100-min intervals for 24 hr in three seasons for each of three clinically healthy individuals: an adolescent woman, a menstrually cycling woman, and a postmenopausal woman. On an individualized basis, it is ascertained that the first principal component, a new variable, is primarily determined by steroids and that PCA can single out variables displaying interseasonal (circannual) differences validated as statistically significant by a subsequent analysis of variance. The variables here scrutinized and identified as contributing to the PC, however, need not all differ with statistical significance along the scale of the seasons. The steroids contributing the first principal component are DHEA-S and an estrogen in all three individuals studied, cortisol and aldosterone in two of them, and 17-OH progesterone in one case.
多变量观测的主成分（PC）分析技术是一种可对多变量数据进行降维以便进一步分析的方法。本文将其作为一种选择具有重要时间生物学意义变量的手段进行介绍，这些变量可通过方差分析作进一步研究。主成分分析在一项关于人类内分泌变异性主要时间来源的研究中得到了应用。对三名临床健康个体（一名青春期女性、一名处于月经周期的女性和一名绝经后女性）在三个季节中每隔100分钟采集一次的样本进行分析，比较了七种甾体激素和六种非甾体激素对时间变异性的贡献。在个体层面上，可以确定第一主成分这一新变量主要由甾体激素决定，并且主成分分析能够挑选出那些在后续方差分析中被验证具有统计学显著意义的呈现季节间（年周期）差异的变量。然而，在此处经过仔细审查并确定对主成分有贡献的变量，并不一定都在季节尺度上具有统计学显著差异。对第一主成分有贡献的甾体激素在所有三名研究对象中均包括硫酸脱氢表雄酮（DHEA-S）和一种雌激素，在其中两名研究对象中包括皮质醇和醛固酮，在一名研究对象中包括17-羟孕酮。
