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



  March 29, 2018

  USA 11:00 a.m. EDT, 8:00 a.m. PDT, 16:00 BST




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.

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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

Who Should Attend

  • Researchers/ R&D Managers
  • Core laboratory scientists
  • New users in mass spectrometry and metabolomics


Ralf Tautenhahn,
Software Product Manager,
Thermo Fisher Scientific
Amanda Souza,
Metabolomics Program Manager,
Thermo Fisher Scientific


Britt Erikson,
Senior Editor,