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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
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Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted
Bo Li1, Jing Tang1, Qingxia Yang1
1Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
Scientific Reports
|December 14, 2016
Summary
Untargeted metabolomics requires data normalization. This study compared 16 methods, finding VSN, Log Transformation, and PQN performed best for LC/MS data, and offers an online tool for method selection.
Area of Science:
- Metabolomics
- Analytical Chemistry
- Bioinformatics
Background:
- Untargeted metabolomics is crucial for biological insights.
- Experimental and technical variations hinder differential metabolic feature identification.
- Data-driven normalization is essential before feature selection in LC/MS data.
Purpose of the Study:
- To comprehensively compare the performance of 16 normalization methods for LC/MS metabolomics data.
- To evaluate the sample size dependence of these normalization methods.
- To provide an accessible online tool for method selection.
Main Methods:
- Performed a comprehensive comparison of 16 normalization methods.
- Categorized methods based on normalization performance across various sample sizes.
- Developed an interactive web tool for evaluating normalization method performance.
Main Results:
- VSN, Log Transformation, and PQN demonstrated superior normalization performance.
- The Contrast method consistently underperformed across all tested datasets.
- Methods were grouped into three tiers based on their efficacy.
Conclusions:
- VSN, Log Transformation, and PQN are recommended for LC/MS metabolomics data normalization.
- The developed web tool aids researchers in selecting appropriate normalization strategies.
- This comparative analysis provides guidance for improving metabolomics data quality.
