ArticleAnalytical and bioanalytical chemistry2021
Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies.
Article in Analytical and bioanalytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 24 citations in OpenAlex.
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- COVID-19 Infection during Pregnancy: Disruptions in Lipid Metabolism and Implications for Newborn Health.International journal of molecular sciences · 2023Article
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- AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications.Cell reports. Physical science · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
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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Registered trials
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