Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Reisetter, Anna; Muehlbauer, Michael; Bain, James; Nodzenski, Michael; Stevens, Robert; Ilkayeva, Olga; Metzger, Boyd; Newgard, Christopher; Lowe, William; Scholtens, Denise
• Is Part Of: BMC bioinformatics. Volume 18:Issue 1(2017)
• Page Start: 1
• Page End: 17
• Publication Date: 2017-12
• Subjects: Metabolomics -- Non-targeted -- Gas chromatography/mass spectrometry -- GC/MS -- Normalization -- Batch effects; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-017-1501-7
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Metabolomics offers a unique integrative perspective for health research, reflecting genetic and environmental contributions to disease-related phenotypes. Identifying robust associations in population-based or large-scale clinical studies demands large numbers of subjects and therefore sample batching for gas-chromatography/mass spectrometry (GC/MS) non-targeted assays. When run over weeks or months, technical noise due to batch and run-order threatens data interpretability. Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and, notably, failure to address batch-specific truncation of low abundance compounds. Results To curtail technical noise and make GC/MS metabolomics data amenable to analyses describing biologically relevant variability, we propose mixture model normalization (mixnorm) that accommodates truncated data and estimates per-metabolite batch and run-order effects using quality control samples. Mixnorm outperforms other approaches across many metrics, including improved correlation of non-targeted and targeted measurements and superior performance when metabolite detectability varies according to batch. For some metrics, particularly when truncation is less frequent for a metabolite, mean centering and median scaling demonstrate comparable performance to mixnorm. Conclusions When quality control samples are systematically included in batches, mixnorm is uniquely suited to normalizing non-targeted GC/MS metabolomics data due to explicit accommodation of batch effects, run order and varying thresholds of detectability. Especially in large-scale studies, normalization is crucial for drawing accurate conclusions from non-targeted GC/MS metabolomics data.