An Approach for Metabonomics Data Analysis Based on Orthogonal Signal Correction Partical Least Square Discriminate Analysis

Guoliang Xu, Bingtao Li, Qiyun Zhang, Xilan Tang, Hongnin Liu, Bin Nie, Riyue Yu
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引用次数: 2

Abstract

Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex, which is hard to summarize and virsualize without appropriate tools. The use of chemometric tools, such as orthogonal signal correction(OSC), principal component analysis (PCA), Partial least squares to latent structure discriminant analysis (PLS-DA), make the data dimensionality reduction and interpretation much easier. Here we showed an system method based on PCA, OSC-PLS-DA for metabonomic data analysis; Furthermore, U-plot, as a visualized tool, combined with independent samples T test, were used for the biomarkers discovery. As an example, dataset from RZ water extract administrated rats urine collected by LC/MS/MS was used to demonstrate this method. As a result, U-plot based on OSC-PLS-DA was proved to be an effective, time saving tool for data interpretation and biomarkers discovery.
基于正交信号校正粒子最小二乘判别分析的代谢组学数据分析方法
代谢组学或代谢分析实验产生的数据集变得越来越复杂,如果没有适当的工具,很难进行总结和可视化。使用化学计量学工具,如正交信号校正(OSC)、主成分分析(PCA)、偏最小二乘到潜在结构判别分析(PLS-DA),使数据降维和解释变得更加容易。本文提出了一种基于PCA、OSC-PLS-DA的代谢组学数据分析系统方法;此外,U-plot作为可视化工具,结合独立样本T检验,用于生物标志物的发现。以采用LC/MS/MS采集的RZ水提液给药大鼠尿液数据为例,对该方法进行了验证。因此,基于OSC-PLS-DA的U-plot被证明是一种有效的、节省时间的数据解释和生物标志物发现工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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