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.
| Original language | English (US) |
|---|---|
| Article number | 84 |
| Journal | BMC bioinformatics |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 2 2017 |
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Keywords
- Batch effects
- GC/MS
- Gas chromatography/mass spectrometry
- Metabolomics
- Non-targeted
- Normalization
ASJC Scopus subject areas
- Structural Biology
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics
Cite this
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Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data. / Reisetter, Anna C.; Muehlbauer, Michael J.; Bain, James R.; Nodzenski, Michael; Stevens, Robert D.; Ilkayeva, Olga; Metzger, Boyd E.; Newgard, Christopher B.; Lowe, William L.; Scholtens, Denise M.
In: BMC bioinformatics, Vol. 18, No. 1, 84, 02.02.2017.Research output: Contribution to journal › Article
TY - JOUR
T1 - Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data
AU - Reisetter, Anna C.
AU - Muehlbauer, Michael J.
AU - Bain, James R.
AU - Nodzenski, Michael
AU - Stevens, Robert D.
AU - Ilkayeva, Olga
AU - Metzger, Boyd E.
AU - Newgard, Christopher B.
AU - Lowe, William L.
AU - Scholtens, Denise M.
PY - 2017/2/2
Y1 - 2017/2/2
N2 - 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.
AB - 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.
KW - Batch effects
KW - GC/MS
KW - Gas chromatography/mass spectrometry
KW - Metabolomics
KW - Non-targeted
KW - Normalization
UR - http://www.scopus.com/inward/record.url?scp=85011342616&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011342616&partnerID=8YFLogxK
U2 - 10.1186/s12859-017-1501-7
DO - 10.1186/s12859-017-1501-7
M3 - Article
C2 - 28153035
AN - SCOPUS:85011342616
VL - 18
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
IS - 1
M1 - 84
ER -