title: A modified data normalization method for GC-MS-based metabolomics to minimize batch variation.
abstract: The goal of metabolomics data pre-processing is to eliminate systematic variation, such that biologically-related metabolite signatures are detected by statistical pattern recognition. Although several methods have been developed to tackle the issue of batch-to-batch variation, each method has its advantages and disadvantages. In this study, we used a reference sample as a normalization standard for test samples within the same batch, and each metabolite value is expressed as a ratio relative to its counterpart in the reference sample. We then applied this approach to a large multi-batch data set to facilitate intra- and inter-batch data integration. Our results demonstrate that normalization to a single reference standard has the potential to minimize batch-to-batch data variation across a large, multi-batch data set.
authors.affiliation: Department of Biochemistry, Interdisciplinary Plant Group, Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO 65211 USA.
authors.forename: Mingjie
authors.initials: M
authors.lastname: Chen
authors.affiliation: Department of Biochemistry, Interdisciplinary Plant Group, Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO 65211 USA.
authors.forename: R Shyama Prasad
authors.initials: RS
authors.lastname: Rao
authors.affiliation: Department of Biochemistry, Interdisciplinary Plant Group, Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO 65211 USA.
authors.forename: Yiming
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Regulatory Science, DuPont Experimental Station, Route 141 and Henry Clay Road, Delaware, 19880 USA.
authors.forename: Cathy Xiaoyan
authors.initials: CX
authors.lastname: Zhong
authors.affiliation: Department of Biochemistry, Interdisciplinary Plant Group, Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO 65211 USA.
authors.forename: Jay J
authors.initials: JJ
authors.lastname: Thelen
keywords: Batch-to-batch variation; Maize; Metabolomics; Normalization; Reference sample