Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data.
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.
Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
Anna C
AC
Reisetter
Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Duke University School of Medicine, Durham, NC, 27701, USA.
Michael J
MJ
Muehlbauer
Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Duke University School of Medicine, Durham, NC, 27701, USA.
James R
JR
Bain
Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
Michael
M
Nodzenski
Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Duke University School of Medicine, Durham, NC, 27701, USA.
Robert D
RD
Stevens
Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Duke University School of Medicine, Durham, NC, 27701, USA.
Olga
O
Ilkayeva
Department of Medicine, Division of Endocrinology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
Boyd E
BE
Metzger
Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Duke University School of Medicine, Durham, NC, 27701, USA.
Christopher B
CB
Newgard
Department of Medicine, Division of Endocrinology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
William L
WL
Lowe
Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. dscholtens@northwestern.edu.
Denise M
DM
Scholtens
D005260:Female; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods* / Q000592:standards; D006801:Humans; D007231:Infant, Newborn; D055432:Metabolomics / Q000379:methods* / Q000592:standards; D008954:Models, Biological; D011247:Pregnancy; D011786:Quality Control
Batch effects; GC/MS; Gas chromatography/mass spectrometry; Metabolomics; Non-targeted; Normalization