Unbiased peak detection in untargeted metabolomics using LC-MS generates an exhaustive list of features or signals from biological samples. Through a data reduction process these features can be converted to a list of meaningful compounds by accounting for artifacts such as naturally occurring isotopes, adduct formations and background ions. Neglecting artifacts may lead to over interpretation of data thus drawing incorrect conclusions and wasting time.
In this webinar, you will learn how to differentiate a compound from a feature to reduce redundancies and accelerate data analysis using Thermo Scientific™ Compound Discoverer™ software. Read more
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Key Learning Objectives
Learn about the challenges of untargeted metabolomics data analysis
Find out how the Compound Discoverer software allows you to accelerate data processing by converting features to meaningful compounds
Discover how the data reduction process can remove redundancies and increase confidence in data analysis
