We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data.
- Authors
Reisetter, Anna C.; Muehlbauer, Michael J.; Bain, James R.; Nodzenski, Michael; Stevens, Robert D.; Ilkayeva, Olga; Metzger, Boyd E.; Newgard, Christopher B.; Lowe Jr., William L.; Scholtens, Denise M.
- 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.
- Subjects
METABOLOMICS; BATCH processing; GAS chromatography/Mass spectrometry (GC-MS); LIQUID chromatography-mass spectrometry; NUCLEAR magnetic resonance
- Publication
BMC Bioinformatics, 2017, Vol 18, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/s12859-017-1501-7