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Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data

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High-Throughput Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1978))

Abstract

Metabolomics is the science of characterizing and quantifying small molecule metabolites in biological systems. These metabolites give organisms their biochemical characteristics, providing a link between genotype, environment, and phenotype. With these opportunities also come data challenges, such as compound annotation, missing values, and batch effects. We present the steps of a general pipeline to process untargeted mass spectrometry data to alleviate the latter two challenges. We assume to have a matrix with metabolite abundances, with metabolites in rows and samples in columns. The steps in the pipeline include summarizing technical replicates (if available), filtering, imputing, transforming, and normalizing the data. In each of these steps, a method and parameters should be chosen based on assumptions one is willing to make, the question of interest, and diagnostic tools. Besides giving a general pipeline that can be adapted by the reader, our goal is to review diagnostic tools and criteria that are helpful when making decisions in each step of the pipeline and assessing the effectiveness of normalization and batch correction. We conclude by giving a list of useful packages and discuss some alternative approaches that might be more appropriate for the reader’s data.

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Acknowledgments

D.R., S.J., and K.K. were supported by NIH/NHLBI Grant Number P20 HL113445. D.R., D.G., and K.K. were supported by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535. H.P.-L. was supported by training grant T15 LM009451. Contents are the authors’ sole responsibility and do not necessarily represent official NIH views.

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Authors and Affiliations

  1. PPD, Wilmington, NC, USA

    Dominik Reinhold

  2. Computational Bioscience Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Harrison Pielke-Lombardo

  3. Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA

    Sean Jacobson

  4. Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Debashis Ghosh & Katerina Kechris

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  1. Dominik Reinhold
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  2. Harrison Pielke-Lombardo
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  3. Sean Jacobson
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  4. Debashis Ghosh
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  5. Katerina Kechris
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Correspondence to Katerina Kechris .

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  1. Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, CO, USA

    Angelo D'Alessandro

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Reinhold, D., Pielke-Lombardo, H., Jacobson, S., Ghosh, D., Kechris, K. (2019). Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data. In: D'Alessandro, A. (eds) High-Throughput Metabolomics. Methods in Molecular Biology, vol 1978. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9236-2_20

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  • DOI: https://doi.org/10.1007/978-1-4939-9236-2_20

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