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Validated and Predictive Processing of Gas Chromatography-Mass Spectrometry Based Metabolomics Data for Large Scale Screening Studies, Diagnostics and Metabolite Pattern Verification
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. (Computational Life Science Cluster (CLiC))
Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
Umeå universitet, Medicinska fakulteten, Institutionen för kirurgisk och perioperativ vetenskap, Idrottsmedicin.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. (Computational Life Science Cluster (CLiC))
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2012 (Engelska)Ingår i: Metabolites, ISSN 2218-1989, Vol. 2, s. 796-817Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern. The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

Ort, förlag, år, upplaga, sidor
Basel: MDPI , 2012. Vol. 2, s. 796-817
Nyckelord [en]
metabolomics, chemometrics, information, large data, GC/MS, curve resolution, diagnosis
Nationell ämneskategori
Kemi Idrottsvetenskap
Identifikatorer
URN: urn:nbn:se:umu:diva-60844DOI: 10.3390/metabo2040796PubMedID: 24957763OAI: oai:DiVA.org:umu-60844DiVA, id: diva2:563689
Tillgänglig från: 2012-11-13 Skapad: 2012-10-31 Senast uppdaterad: 2018-06-08Bibliografiskt granskad

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Thysell, ElinChorell, ElinSvensson, Michael BJonsson, PärAntti, Henrik

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