A0241
Title: Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification
Authors: Yuhang Xu - Bowling Green State University (United States) [presenting]
Thao Vu - University of Colorado (United States)
Yumou Qiu - Peking University (China)
Abstract: The process of identifying and quantifying metabolites in complex mixtures plays a critical role in metabolomics studies to obtain an informative interpretation of underlying biological processes. Manual approaches are time-consuming and heavily reliant on the knowledge and assessment of nuclear magnetic resonance (NMR) experts. A shifting-corrected regularized regression method is proposed, automatically identifying and quantifying metabolites in a mixture. A detailed algorithm is also proposed to implement the proposed method. Using a novel weight function, the proposed method can detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better in identifying and quantifying metabolites in a complex mixture. This approach uses experimental and biological NMR mixtures to demonstrate real data applications.
