Lipidomic data analysis: Tutorial, practical guidelines and applications
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.
| Main Authors: | , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Elsevier
2015-07-23
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| Subjects: | Chemometrics, Statistics, Classification, Exploration, Data analysis, Lipidomics, |
| Online Access: | http://hdl.handle.net/10261/128099 http://dx.doi.org/10.13039/501100000780 |
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| Summary: | 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. |
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