Biomedical Research Networking Center in Diabetes and Associated Metabolic Disorders

Madrid, Spain

Biomedical Research Networking Center in Diabetes and Associated Metabolic Disorders

Madrid, Spain
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Domingo-Almenara X.,Rovira i Virgili University | Domingo-Almenara X.,Biomedical Research Networking Center in Diabetes and Associated Metabolic Disorders | Perera A.,Polytechnic University of Catalonia | Ramirez N.,Rovira i Virgili University | And 7 more authors.
Journal of Chromatography A | Year: 2015

Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices. © 2015 Elsevier B.V.


Domingo-Almenara X.,Rovira i Virgili University | Domingo-Almenara X.,Biomedical Research Networking Center in Diabetes and Associated Metabolic Disorders | Perera A.,Polytechnic University of Catalonia | Perera A.,CIBER ISCIII | And 4 more authors.
Computer Methods and Programs in Biomedicine | Year: 2016

Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R2 coefficients of determination were in good agreement (R2>0.90) while up to 24 cases exhibited an excellent linear relation (R2>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC×GC-MS. © 2016 Elsevier Ireland Ltd.


Domingo-Almenara X.,Rovira i Virgili University | Domingo-Almenara X.,Biomedical Research Networking Center in Diabetes and Associated Metabolic Disorders | Perera A.,Polytechnic University of Catalonia | Ramirez N.,Rovira i Virgili University | And 3 more authors.
Advances in Intelligent Systems and Computing | Year: 2015

Comprehensive gas chromatography - mass spectromety (GCxGC-MS) has become a promising tool in metabolomics. However, algorithms for GCxGCMS data processing are needed in order to automatically process the data and extract the most pure information about the compounds appearing in the complex biological samples. This study shows the capability of orthogonal signal deconvolution (OSD), a novel algorithm based on blind source separation, to extract the spectra of the compounds appearing in GCxGC-MS samples. Results include a comparison between OSD and multivariate curve resolution - alternating least squares (MCRALS) with the extraction of metabolites spectra in a human serum sample analyzed through GCxGC-MS. This study concludes that OSD is a promising alternative for GCxGC-MS data processing. © Springer International Publishing Switzerland 2015.

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