【作者】 陈晨； 张志敏； 欧阳梅兰； 刘鑫波； 易伦朝； 梁逸曾；

【Author】 Chen Chen;Zhimin Zhang;Meilan Ouyang;Xinbo Liu;Lunzhao Yi;Yizeng Liang;College of Chemistry and Chemical Engineering, Research Center of Modernization of Chinese Medicines, Central South University;

【机构】 中南大学化学化工学院中药现代化研究中心；

【摘要】 代谢组学研究中的数据大多通过核磁共振气相色谱质谱联用或液相色谱质谱联用等分析仪器所获得,所得的这些数据的结构通常都是高维复杂的。本文将新近提出的质心收缩正则判别分析法(SCRDA)用于代谢组学数据分析。通过对类内协方差矩阵的正则化估计,SCRDA可以解决线性判别分析法(LDA)存在的奇异性问题,此外用收缩估计进行变量的选择。通过一个模拟数据集和两个真实的复杂代谢组学数据集进行分析,与正交偏最小二乘判别分析(OPLS-DA),惩罚线性判别分析法(PLDA)和最近收缩质心法(NSC)进行了比较。所得结果表明SCRDA在变量选择和分类预测方面优于其他方法。此外,SCRDA筛选出的生物标记物和生化研究结果一致。更多还原

【Abstract】 In this study, a recent method named shrunken centroids regularized discriminant analysis(SCRDA) has been introduced and applied in the exploration of complex metabolomics dataset. By regularizing the estimate of the within-class covariance matrix, SCRDA can deal with the singularity issue of linear discriminant analysis(LDA). Then a shrinkage estimator is applied to perform variable selection. The method presented is illustrated through one simulated dataset and two complex metabolomics datasets. OPLS-DA and two similar statistical methods penalized linear discriminant analysis(PLDA) and nearest shrunken centroids(NSC) are used for comparisons. The results illustrate that SCRDA has some desirable abilities in variable selection, classification and prediction. Moreover, the biomarkers identified by SCRDA are further demonstrated to be in accordance with the biochemical research.更多还原