Author, as appears in the article.: Domingo-Almenara X; Perera A; Ramírez N; Brezmes J
Department: Enginyeria Electrònica, Elèctrica i Automàtica
URV's Author/s: Brezmes Llecha, Jesús Jorge / Domingo Almenara, Xavier / Ramírez González, Noelia
Keywords: Spectrometry Procedures Principal component analysis Phosphoric acid Orthogonal signals Orthogonal signal deconvolution Multivariate curve resolution Multivariant analysis Metabolomics Mass spectrometry Mass fragmentography Lymphatic leukemia Linear relation Lactic acid Jurkat cell line Inositol Independent component analysis Human cell Human Glycerol Gas chromatography/mass spectrometry Gas chromatography-mass spectrometry Gas chromatography Erythritol Data handling Correlation coefficient Controlled study Comprehensive gas chromatography Compound deconvolution Chromatography Chromatographic signals Chromatographic analysis Blind source separation Biological samples Bioinformatics Automation Automated resolution Aspartic acid Article Apoptosis Analytic method Algorithms Algorithm multivariate curve resolution independent component analysis comprehensive gas chromatography compound deconvolution
Abstract: 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.
Thematic Areas: Software Saúde coletiva Psicología Odontología Medicina iii Medicina ii Medicina i Medical informatics Matemática / probabilidade e estatística Interdisciplinar Health informatics General medicine Engineering, biomedical Engenharias iv Engenharias iii Engenharias ii Educação física Computer science, theory & methods Computer science, interdisciplinary applications Computer science applications Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: xavier.domingo@urv.cat noelia.ramirez@urv.cat jesus.brezmes@urv.cat
Author identifier: 0000-0002-7704-8550
Record's date: 2024-11-09
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S0169260715300511
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Computer Methods And Programs In Biomedicine. 130 135-141
APA: Domingo-Almenara X; Perera A; Ramírez N; Brezmes J (2016). Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics. Computer Methods And Programs In Biomedicine, 130(), 135-141. DOI: 10.1016/j.cmpb.2016.03.007
Article's DOI: 10.1016/j.cmpb.2016.03.007
Entity: Universitat Rovira i Virgili
Journal publication year: 2016
Publication Type: Journal Publications
Repositori URV