Vettukattil Riyas
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Postboks 8905, MTFS, 7489, Trondheim, Norway,
Methods Mol Biol. 2015;1277:123-36. doi: 10.1007/978-1-4939-2377-9_10.
Recent advances in metabolic profiling techniques allow global profiling of metabolites in cells, tissues, or organisms, using a wide range of analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). The raw data acquired from these instruments are abundant with technical and structural complexity, which makes it statistically difficult to extract meaningful information. Preprocessing involves various computational procedures where data from the instruments (gas chromatography (GC)/liquid chromatography (LC)-MS, NMR spectra) are converted into a usable form for further analysis and biological interpretation. This chapter covers the common data preprocessing techniques used in metabonomics and is primarily focused on baseline correction, normalization, scaling, peak alignment, detection, and quantification. Recent years have witnessed development of several software tools for data preprocessing, and an overview of the frequently used tools in data preprocessing pipeline is covered.
代谢谱分析技术的最新进展使得能够使用多种分析技术，如核磁共振（NMR）光谱法和质谱（MS），对细胞、组织或生物体中的代谢物进行全面分析。从这些仪器获取的原始数据在技术和结构上非常复杂，这使得从统计学角度提取有意义的信息变得困难。预处理涉及各种计算程序，其中将仪器数据（气相色谱（GC）/液相色谱（LC）-MS、NMR光谱）转换为可用于进一步分析和生物学解释的形式。本章涵盖了代谢组学中常用的数据预处理技术，主要侧重于基线校正、归一化、缩放、峰对齐、检测和定量。近年来，已经出现了几种用于数据预处理的软件工具，本文还将概述数据预处理流程中常用的工具。
