Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data.
BMC Bioinformatics
; 18(1): 84, 2017 Feb 02.
Article
em En
| MEDLINE
| ID: mdl-28153035
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Metabolômica
/
Cromatografia Gasosa-Espectrometria de Massas
Tipo de estudo:
Prognostic_studies
Limite:
Female
/
Humans
/
Newborn
/
Pregnancy
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article