Pathway testing for longitudinal metabolomics. (26th March 2021)

• Record Type: Journal Article
• Authors: Ebrahimpoor, Mitra; Spitali, Pietro; Goeman, Jelle J.; Tsonaka, Roula
• Is Part Of: Statistics in medicine. Volume 40:Number 13(2021)
• Page Start: 3053
• Page End: 3065
• Publication Date: 2021-03-26
• Subjects: global test -- joint latent process -- longitudinal analysis -- mixed model -- pseudo likelihood; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8957
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : We propose a top‐down approach for pathway analysis of longitudinal metabolite data. We apply a score test based on a shared latent process mixed model which can identify pathways with differentially progressing metabolites. The strength of our approach is that it can handle unbalanced designs, deals with potential missing values in the longitudinal markers, and gives valid results even with small sample sizes. Contrary to bottom‐up approaches, correlations between metabolites are explicitly modeled leveraging power gains. For large pathway sizes, a computationally efficient solution is proposed based on pseudo‐likelihood methodology. We demonstrate the advantages of the proposed method in identification of differentially expressed pathways through simulation studies. Finally, longitudinal metabolite data from a mice experiment is analyzed to demonstrate our methodology.