Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data

Abstract

In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography -mass spectrometry (LC-MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies.

Promises and pitfalls of untargeted metabolomics

“…These include the use of replicate samples or QC’s that can be run throughout different intervals of a batch (Dunn et al 2011; Wehrens et al 2016), and in one method, used in a serially dilute form throughout to test for signal linearity of different compounds (Kouassi Nzoughet et al 2017). A variety of processing tools have also been developed to also deal with issues of signal and retention time drift, as well as batch effects and outliers that can plague data analysis, as discussed in the post-analytical section of the review (Salerno et al 2017; Thonusin et al 2017).…”

Discrimination of the Geographical Origin of Soybeans Using NMR-Based Metabolomics

“…Standardized area normalization divides the intensity of each metabolite by a constant concentration of the internal standard compound, allowing the measurement of the contribution of each metabolite to the spectrum [ 52 ]. Because internal standards added prior to extraction can monitor and correct the intensity drift that might occur during extraction and instrument analysis, standardized area normalization can reduce the difference in extraction efficiency between samples [ 53 , 54 ]. Meanwhile, the Pareto scaling method emphasizes weak peaks with high biological relevance and reduces the effect of intense peaks, thereby reducing the effect of noise variables more than the UV scaling method [ 55 ].…”

Data normalization strategies in metabolomics: Current challenges, approaches, and tools

“…50 MetaboDrift is an Excel-based tool to visually evaluate and correct for intensity drift in a multibatch LC-MS metabolomics dataset. 51 The tool enables drift correction based on either QC samples analyzed throughout the batches or using QC sampleindependent methods. NormalizeMets (https://cran.r-project.org/web/ packages/NormalizeMets/) is a tool available within Microsoft Excel and as a freely available standalone R software for comparative evaluation of different normalization methods.…”