Journal
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 413, Issue 13, Pages 3479-3486Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00216-021-03294-8
Keywords
Normalization; Interbatch correction; Scaling; Liquid chromatography-mass spectrometry
Funding
- Russian Science Foundation [18-75-10097]
- Russian Science Foundation [18-75-10097] Funding Source: Russian Science Foundation
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Different data normalization methods in liquid chromatography-mass spectrometry data processing were compared, with autoscaling demonstrating the most effective reduction in interbatch variation.
Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.
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