Park Youngja H, Kong Taewoon, Roede James R, Jones Dean P, Lee Kichun
1College of Pharmacy, Korea University, Sejong, 30019 South Korea.
2Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
BioData Min. 2019 Feb 4;12:4. doi: 10.1186/s13040-019-0191-2. eCollection 2019.
Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.
We present a dimensionality-reduction based approach termed 'biplot correlation range (BCR)' that uses biplot correlation analysis with direct orthogonal signal correction and PLS to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes.
Using a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that a statistical method by false discovery rate or statistical total correlation spectroscopy can hardly find in complex data analysis for predictive health and personalized medicine.
分析方法可用于以具有成本效益的方式获取广泛的代谢信息，以实现个性化医疗，但疾病风险和诊断大多依赖基于错误发现率和相关性统计原则的单个生物标志物。由于复杂生物系统中存在功能冗余和多层调节，单个生物标志物虽然有用，但在疾病特征描述方面存在固有局限性。数据降维和判别分析工具，如主成分分析（PCA）、偏最小二乘法（PLS）或正交偏最小二乘法（O-PLS），提供了分离代谢表型的方法，但没有为选择作为代谢表型贡献因素的组水平代谢物提供统计依据。
我们提出了一种基于降维的方法，称为“双标图相关范围（BCR）”，该方法使用双标图相关分析结合直接正交信号校正和PLS，以提供对代谢表型有贡献的代谢标志物的组水平选择。
使用复杂生物系统中经常出现的模拟多层系统，我们展示了所提出方法与基于错误发现率和相关性的现有方法相比的可行性和优越性。为了在实际数据集中演示所提出的方法，我们使用基于液相色谱-质谱联用的代谢组学来确定野生型（WT）小鼠和硫氧还蛋白-2转基因（TG）小鼠肝脏线粒体中存在的代谢物谱。我们使用BCR根据类身份方向上增加的分数选择判别变量。结果表明，BCR提供了一种方法，能够以一种在预测健康和个性化医疗的复杂数据分析中，基于错误发现率的统计方法或统计全相关光谱法几乎无法找到的方式，识别对类分离有贡献的代谢物。
