Metabolomics presents unique challenges in terms of data acquisition, data processing, data standardization, statistical analyses, and identification of unknowns. Robust and precise analytical methods are essential to metabolomics, but data acquisition is only part of the challenge. It is equally important to develop both software tools and appropriate workflows to extract meaningful data and generate results from metabolomics datasets.
This webinar provides an overview of approaches and software post-data acquisition workflows, including novel tools being used in the detection of statistically significant patterns and identification of underlying compounds and their pathway context. Finally, current approaches in integrating metabolomic data with other multi-omic datasets will also be explored.