An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Amanda O Shaver; Brianna M Garcia; Goncalo J Gouveia; Alison M Morse; Zihao Liu; Carter K Asef; Ricardo M Borges; Franklin E Leach 3rd; Erik C Andersen; I Jonathan Amster; Facundo M Fernández; Arthur S Edison; Lauren M McIntyre

doi:10.3389/fmolb.2022.930204

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Front Mol Biosci. 2022 Nov 9:9:930204. doi: 10.3389/fmolb.2022.930204. eCollection 2022.

Authors

Amanda O Shaver^{1

2}, Brianna M Garcia^{2

3}, Goncalo J Gouveia^{2

4}, Alison M Morse⁵, Zihao Liu⁵, Carter K Asef⁶, Ricardo M Borges⁷, Franklin E Leach 3rd^{2

8}, Erik C Andersen⁹, I Jonathan Amster³, Facundo M Fernández⁶, Arthur S Edison^{1

2

4}, Lauren M McIntyre^{5

10}

Affiliations

¹ Department of Genetics, University of Georgia, Athens, GA, United States.
² Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States.
³ Department of Chemistry, University of Georgia, Athens, GA, United States.
⁴ Department of Biochemistry, University of Georgia, Athens, GA, United States.
⁵ Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States.
⁶ School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States.
⁷ Walter Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
⁸ Department of Environmental Health Science, University of Georgia, Athens, GA, United States.
⁹ Department of Molecular Biosciences, Northwestern University, Evanston, IL, United States.
¹⁰ University of Florida Genetics Institute, University of Florida, Gainesville, FL, United States.

Abstract

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Keywords: batch effects; experimental design; mass spectrometry; meta-analysis; model organism; nuclear magnetic resonance spectroscopy; untargeted metabolomics.

Abstract

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