Data Treatment for LC-MS Untargeted Analysis.

Q4 Biochemistry, Genetics and Molecular Biology

Methods in molecular biology Pub Date : 2025-01-01 DOI:10.1007/978-1-0716-4334-1_5

Mar Garcia-Aloy, Johannes Rainer, Pietro Franceschi

引用次数: 0

Abstract

Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted experiments require complex bioinformatic strategies to extract information from the experimental data. Here we discuss the "data preprocessing," the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation.

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LC-MS非靶向分析的数据处理。

液相色谱-质谱（LC-MS）非靶向实验需要复杂的生物信息学策略来从实验数据中提取信息。这里我们讨论“数据预处理”，这是对原始数据执行的一组过程，以生成数据矩阵，该矩阵将成为后续统计分析的起点。数据预处理是知识提取的关键步骤，为了使任何非靶向代谢组学研究的输出最大化，应该仔细控制和优化数据预处理。

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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来源期刊

Methods in molecular biology Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

2.00

自引率

0.00%

发文量

3536

期刊介绍： For over 20 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by-step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice.