Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification.
Biostatistics
; 24(1): 140-160, 2022 12 12.
Article
em En
| MEDLINE
| ID: mdl-36514939
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. 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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
/
Metabolômica
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
Biostatistics
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos