Features or Compounds? A Data Reduction Strategy for Untargeted Metabolomics Analyses to Generate Meaningful Data

[Orbitrap_SciLib]

Amanda Souza, Metabolomics Program Manager Ralf Tautenhahn, Metabolomics Software Product Manager

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.

http://connect.acspubs.org/CENWebinar_Thermo_3_29_18?cta=button_3&elqtrack=true
Thermo Fisher Scientific