Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification. (10th May 2022)

• Record Type: Journal Article
• Authors: Vu, Thao; Xu, Yuhang; Qiu, Yumou; Powers, Robert
• Is Part Of: Biostatistics. Volume 24:Number 1(2023)
• Page Start: 140
• Page End: 160
• Publication Date: 2022-05-10
• Subjects: Chemical shift -- NMR metabolomics -- Regularized regression -- Spectral data; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxac015
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: 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. We propose a shifting-corrected regularized regression method, which identifies and quantifies metabolites in a mixture automatically. A detailed algorithm is also proposed to implement the proposed method. Using a novel weight function, the proposed method is able to detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better with regard to the identification and quantification of metabolites in a complex mixture. We also demonstrate real data applications of our method using experimental and biological NMR mixtures.