A Modified Data Normalization Method for Gc-Ms-Based Metabolomics to Minimize Batch Variation

Mingjie Chen,R Shyama Prasad Rao,Yiming Zhang,Cathy Xiaoyan Zhong,Jay J Thelen

SpringerPlus（2014）

Department of Biochemistry

Cited 43|Views5

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.

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Key words

Maize,Batch-to-batch variation,Metabolomics,Normalization,Reference sample

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