PCA as a practical indicator of OPLS-DA model reliability.
PCA as a practical indicator of OPLS-DA model reliability.
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DOI：
10.2174/2213235x04666160613122429
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Powers R
中科院分区：
文献类型：
--
作者：
Worley B;Powers R
主成分分析(PCA)和潜在结构的正交投影判别分析(OPLS-DA)是强大的统计建模工具，基于来自核磁共振、MS或其他分析仪器的高维光谱测量，提供了对试验组之间的分离的洞察。然而，如果在未经验证的情况下使用这些工具，调查人员可能会得出统计上不可靠的结论。这种危险对于偏最小二乘法(偏最小二乘法)和开放最小二乘法尤其真实，它们积极地迫使实验组之间的分离。因此，当PCA无法揭示组分离时，OPLS-DA经常被用作替代方法，但这种做法非常危险。如果没有严格的验证，OPLS-DA很容易产生统计上不可靠的组分离。在核磁共振数据集上进行了PCA组分离和OPLS-DA交叉验证度量的蒙特卡罗分析，在得分空间中具有统计学意义的分离。将线性递增的高斯噪声添加到每个数据矩阵中，然后构建和验证主成分分析和OPLS-DA模型。随着噪声的增加，主成分分析得分-组间空间距离迅速减小，OPLS-DA交叉验证统计量同时恶化。还观察到估计载荷(增加的噪声)和真实(原始)载荷之间的相关性降低。虽然OPLS-DA模型的有效性随着噪声的增加而降低，但分数空间中的组分离基本不受影响。在主成分分析和OPLS-DA交叉验证度量的蒙特卡罗分析结果的支持下，我们为从PCA和OPLS-DA模型进行可靠推断提供了实用的指南和交叉验证的建议。
Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation. A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models. With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected. Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.