title: Lipidomic data analysis: tutorial, practical guidelines and applications.
abstract: Lipids are a broad group of biomolecules involved in diverse critical biological roles such as cellular membrane structure, energy storage or cell signaling and homeostasis. Lipidomics is the -omics science that pursues the comprehensive characterization of lipids present in a biological sample. Different analytical strategies such as nuclear magnetic resonance or mass spectrometry with or without previous chromatographic separation are currently used to analyze the lipid composition of a sample. However, current analytical techniques provide a vast amount of data which complicates the interpretation of results without the use of advanced data analysis tools. The choice of the appropriate chemometric method is essential to extract valuable information from the crude data as well as to interpret the lipidomic results in the biological context studied. The present work summarizes the diverse methods of analysis than can be used to study lipidomic data, from statistical inference tests to more sophisticated multivariate analysis methods. In addition to the theoretical description of the methods, application of various methods to a particular lipidomic data set as well as literature examples are presented.
authors.affiliation: Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles väg 2, SE-171 77 Stockholm, Sweden.
authors.forename: Antonio
authors.initials: A
authors.lastname: Checa
authors.affiliation: Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, Barcelona 08034, Spain.
authors.forename: Carmen
authors.initials: C
authors.lastname: Bedia
authors.affiliation: Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, Barcelona 08034, Spain. Electronic address: joaquim.jaumot@idaea.csic.es.
authors.forename: Joaquim
authors.initials: J
authors.lastname: Jaumot
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D002853:Chromatography, Liquid / Q000379:methods; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D016018:Least-Squares Analysis; D050356:Lipid Metabolism; D008055:Lipids / Q000032:analysis*; D000069550:Machine Learning; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D013058:Mass Spectrometry / Q000379:methods; D025341:Principal Component Analysis
chemical_list: D008055:Lipids
keywords: Chemometrics; Classification; Data analysis; Exploration; Lipidomics; Statistics