Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.
Anal Chem
; 78(2): 567-74, 2006 Jan 15.
Article
en En
| MEDLINE
| ID: mdl-16408941
A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Grasas de la Dieta
/
Análisis de los Mínimos Cuadrados
/
Interpretación Estadística de Datos
/
Lípidos
/
Obesidad
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
País/Región como asunto:
Europa
Idioma:
En
Revista:
Anal Chem
Año:
2006
Tipo del documento:
Article
País de afiliación:
Países Bajos
Pais de publicación:
Estados Unidos