Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted experiments require complex bioinformatic strategies to extract information from the experimental data. Here we discuss the "data preprocessing," the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation.
Garcia-Aloy, M.; Rainer, J.; Franceschi, P. (2025). Data Treatment for LC-MS Untargeted Analysis. In: Metabolic Profiling: methods and protocols (editor(s) Deda, O.; Gika, H.G.; Wilson, I.D.). (METHODS IN MOLECULAR BIOLOGY): 91-108. ISBN: 9781071643334 doi: 10.1007/978-1-0716-4334-1_5. handle: https://hdl.handle.net/10449/88475
Data Treatment for LC-MS Untargeted Analysis
Garcia-Aloy, MarPrimo
;Franceschi, Pietro
Ultimo
2025-01-01
Abstract
Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted experiments require complex bioinformatic strategies to extract information from the experimental data. Here we discuss the "data preprocessing," the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation.File | Dimensione | Formato | |
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