Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies.
Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies.
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DOI：
10.1016/j.chemolab.2011.08.009
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发表时间：
2011-12-15
期刊：
影响因子：
--
通讯作者：
Kennedy MA
中科院分区：
文献类型：
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作者：
Goodpaster AM;Kennedy MA
目前，在代谢组学研究的主成分分析或偏最小二乘分析得分曲线图中，没有标准的指标被用来量化集群分离或确定集群分离是否具有统计学意义。缺乏这样的测量方法使得几乎不可能比较独立的或实验室间的研究，并可能导致代谢组学文献中的混乱，因为作者基于对表现出边缘分离的计分曲线图的目测和定性检查来假定地识别代谢物以区分样本类别。虽然以前的论文讨论了在主成分分析得分曲线图中对聚类分离的量化，但没有一篇论文主张常规地使用分离的定量测量，该量化测量由对聚类分离是否具有统计学意义的标准和严格的评估来支持。在这里，我们考虑了在主成分分析和偏最小二乘法得分图中群体质心分离的量化和统计学意义。马氏距离被用来量化群质心之间的距离，并对数据计算与F-统计量相关的两样本Hotling的T2检验，然后应用F-检验来确定群分离是否具有统计学意义。我们使用包含不同程度分离的四个数据集来演示该方法的价值，范围从没有明显视觉聚类分离的组到没有视觉聚类重叠的组。广泛采用这种具体指标来量化和评价主成分分析和偏最小二乘法分类分类的统计意义，将有助于使代谢组数据的报告标准化。
Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures makes it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported by a standard and rigorous assessment of whether or not the cluster separation is statistically significant. Here quantification and statistical significance of separation of group centroids in PCA and PLS-DA scores plots are considered. The Mahalanobis distance is used to quantify the distance between group centroids, and the two-sample Hotelling's T2 test is computed for the data, related to an F-statistic, and then an F-test is applied to determine if the cluster separation is statistically significant. We demonstrate the value of this approach using four datasets containing various degrees of separation, ranging from groups that had no apparent visual cluster separation to groups that had no visual cluster overlap. Widespread adoption of such concrete metrics to quantify and evaluate the statistical significance of PCA and PLS-DA cluster separation would help standardize reporting of metabonomics data.