Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing

• Authors: Qin Liu, Tongji University; Douglas Walker, Emory University; Karan Uppal, Emory University; Zihe Liu, Tongji University; Chunyu Ma, Emory University; ViLinh Tran, Emory University; Shuzhao Li, Emory University; Dean Jones, Emory University; Tianwei Yu, Emory University
• Journal Title: SCIENTIFIC REPORTS
• Volume: Volume 10, Number 1
• Publisher: NATURE PUBLISHING GROUP | 2020-08-17, Pages 13856-13856
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1038/s41598-020-70850-0
• Subjects: Health Sciences, Radiology; Health Sciences, Public Health
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; SPECTROMETRY DATA; MASS; ANNOTATION; EXTRACTION; ALIGNMENT; CHROMATOGRAPHY

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/.