Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer.
Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer.
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DOI：
10.1016/j.aca.2010.11.040
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发表时间：
2011-02-07
影响因子：
6.2
通讯作者：
Raftery D
中科院分区：
文献类型：
--
作者：
Gu H;Pan Z;Xi B;Asiago V;Musselman B;Raftery D
核磁共振波谱和质谱学是代谢组学中最常用的两种分析工具，它们的互补性使它们的结合特别有吸引力。一种联合分析方法可以提高提供可靠的方法来检测由疾病、毒性等引起的体液或组织中代谢谱变化的可能性。本文采用1H核磁共振波谱和直接实时分析(DART)-MS对乳腺癌患者和健康对照的血清样品进行代谢组学分析。核磁共振数据的主成分分析(PCA)表明，第一主成分(PC1)分数可以用于区分癌症和正常样本。然而，即使DART-MS可以提供丰富和信息丰富的代谢谱，在DART-MS数据的PCA计分图中也没有观察到这种明显的聚集。使用改进的多变量统计方法，对DART-MS数据进行正交信号校正(OSC)处理的偏最小二乘法(PLS)重新评估，其中回归中的Y矩阵设置为来自核磁共振数据分析的PC1评分值。这种方法和使用核磁共振数据的第一个潜在变量的方法类似，显著改善了疾病样本和正常样本之间的分离，并可以从DART-MS中提取与乳腺癌相关的代谢谱。新的方法允许疾病分类在连续统上表达，而不是二进制标度，因此更好地代表了疾病和健康分类。通过这种方法结合MS和核磁共振获得的改善的代谢谱可能有助于实现更准确的疾病检测，并对疾病机制和生物学有更多的了解。
Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, 1H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.
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DOI：
10.1016/j.chemolab.2006.04.021
发表时间：
2006-12-01
影响因子：
3.9
作者：
Anderssen, Endre;Dyrstad, Knut;Martens, Harald
通讯作者：
Martens, Harald
影响因子：
7.4
作者：
Cody, RB;Laramée, JA;Durst, HD
通讯作者：
Durst, HD
影响因子：
7.4
作者：
Gu, Haiwei;Chen, Huanwen;Cooks, R. Graham
通讯作者：
Cooks, R. Graham
影响因子：
2.9
作者：
Feng, JH;Li, XJ;Nie, YX
通讯作者：
Nie, YX
影响因子：
1.8
作者：
Nicholson, JK;Lindon, JC;Holmes, E
通讯作者：
Holmes, E