优化代谢组学光谱替换
Optimizing Metabonomic Spectral Replacement
摘要：
代谢组学是对疾病、药物或毒素引起的生物化学扰动的指纹图谱的测量，近年来已成为各个领域特别是药物毒性指征的主要研究焦点。采用两种类型的技术（以首字母NMR和MS而闻名），并且都以光谱的形式产生大量数据。复杂的统计模型，称为模式识别技术，通常用于总结和分析这些多维数据。然而，在毒理学试验期间施用的化合物的强烈信号干扰了这些模型。所谓“光谱置换”就是用控制光谱中相应区域的信号来代替这些信号，从而消除这些信号。随后缩放被替换的区域。然而，这种缩放不能精确测量，并且经常导致被替换信号的积分强度的高估。在这里，提出了一种新的协议，它提供了一个准确的估计替换区域。
英文摘要：
Metabonomics, the measure of the fingerprint of biochemical perturbations caused by disease, drugs or toxins, recently has become a major focus of research in various areas especially indications of drug toxicity. Two types of technology (known by the initials NMR and MS) are employed and both produce massive data in form of spectra. Sophisticated statistical models, known as pattern recognition techniques, are commonly applied for summarizing and analyzing these multidimensional data. However, strong signals from compounds that are administered during toxicological trials interfere with these models. So called 'spectral replacement' is a method to eliminate these signals by replacing them with the signals in their corresponding regions in control spectrum. The replaced regions are subsequently scaled. However, this scaling is not accurately measured and often results in overestimation of integrated intensity of the replaced signals. Here, a novel protocol is proposed which provides an accurate estimation of the replaced regions.
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