From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS

Electrophoresis

Volumn 34, Issue 19, 2013, Pages 2812-2826

  Godzien, Joanna ^a,b   Ciborowski, Michal ^a,c   Angulo, Santiago ^a   Barbas, Coral ^a  

^a UNIVERSIDAD SAN PABLO CEU   (Spain)

^b JOHN PAUL II CATHOLIC UNIVERSITY OF LUBLIN   (Poland)

^c MEDICAL UNIVERSITY OF BIALYSTOK   (Poland)

Author keywords

Chemometrics; Data re processing; Data treatment; Outliers detection; Validation

Indexed keywords

BODY FLUIDS; DATA HANDLING; DATA MINING; DATA VISUALIZATION; EXTRACTION;

CHEMOMETRIC TOOLS; CHEMOMETRICES; DATA RE-PROCESSING; DATA TREATMENT; EXAMPLE BASED; HIGH THROUGHPUT TECHNOLOGY; METABOLOMIC STUDY; METABOLOMICS DATA; OUTLIER DETECTION; VALIDATION;

ERRORS;

ROSMARINUS OFFICINALIS EXTRACT;

ANALYTIC METHOD; ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CONTROL STRATEGY; DIABETES MELLITUS; INFORMATION PROCESSING; LC QTOF MS; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; METABOLITE; METABOLOMICS; NONHUMAN; RAT; URINALYSIS;

CHEMOMETRICS; DATA RE-PROCESSING; DATA TREATMENT; OUTLIERS DETECTION; VALIDATION;

ANIMALS; AUTOMATIC DATA PROCESSING; CHROMATOGRAPHY, LIQUID; DIABETES MELLITUS, EXPERIMENTAL; MASS SPECTROMETRY; METABOLOME; METABOLOMICS; RATS;

EID: 84884982446     PISSN: 01730835     EISSN: 15222683     Source Type: Journal    
DOI: 10.1002/elps.201300053     Document Type: Article

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