ArticleScientific reports2020
Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software.Metabolomics : Official journal of the Metabolomic Society · 2023Pooled it
- Metabolic Signatures of Youth Exposure to Mixtures of Per- and Polyfluoroalkyl Substances: A Multi-Cohort Study.Environmental health perspectives · 2023Pooled it
- Best practices for using biological data in psychological research.Journal of mood and anxiety disorders · 2026Review
- Integrated workflow for univariate and multivariate evaluation of batch correction reliability.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Differential metabolomic effects of progestagen-based estrus synchronisation in South African Dohne Merino ewes.Tropical animal health and production · 2026Article
- Prediagnostic Plasma Metabolite Profiles and Prediction of Hepatocellular Carcinoma Risk: The Multiethnic Cohort.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Glucose-dependent spatial and temporal modulation of oligodendrocyte progenitor cell proliferation via ACLY-regulated histone acetylation.Nature neuroscience · 2026Article
- Untargeted metabolomics reveals organism specific biomarkers of carbapenem resistance in Klebsiella pneumoniae and Escherichia coli.NPJ systems biology and applications · 2026Article
- Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.Sensors (Basel, Switzerland) · 2026Review
- Article
- Optimising the choice of normalisation method for use in machine-learning classification of human blood plasma ambient ionisation mass spectra.International journal of mass spectrometry · 2026Article
- Practicing Data Science in Interactive Notebooks.Methods in molecular biology (Clifton, N.J.) · 2026Review
- LC-MS Data Processing Using xcms.Methods in molecular biology (Clifton, N.J.) · 2026Article
- The impact of spectral data pre-processing on the assessment of red wine vintage through spectroscopic methods.Journal of the science of food and agriculture · 2025Article
- mzQuality: An Open-Source Software Tool for Quality Monitoring and Reporting of Targeted Mass Spectrometry Measurements.Journal of the American Society for Mass Spectrometry · 2025Article
- Cerebrospinal fluid metabolomics in autistic regression reveals dysregulation of sphingolipids and decreased β-hydroxybutyrate.EBioMedicine · 2025Article
- Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.Nature protocols · 2025Article
- Article
- metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.Metabolites · 2024Article
- Mass spectrometry in cerebrospinal fluid uncovers association of glycolysis biomarkers with Alzheimer's disease in a large clinical sample.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography-Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized quality control (QC) plasma samples and from real biological studies, the new method resulted in feature tables with better consistency, as well as better down-stream analysis results. The method can be a useful addition to the tools available for large studies involving multiple batches. The method is available as part of the apLCMS package. Download link and instructions are at https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/ .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.