Pathway testing for longitudinal metabolomics

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.

Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high‐dimensional data

“…For the MLPMM, we can compute the predicted random effects and by adapting the formulas provided by Ebrahimpoor et al 15 to the MLPMM of Equation ( 1 ) as follows: where , is the equivalent of with as entries, is the random‐effects design matrix associated to in ( 1 ), , , , and where I denotes identity matrices and “all‐ones” matrices (ie, matrices whose entries are all equal to 1) of dimension .…”

“…For the MLPMM, we can compute the predicted random effects û si = (û s0i , û s1i ) and bsi = ( b1si , … , br s si ) by adapting the formulas provided by Ebrahimpoor et al 15 to the MLPMM of Equation ( 1) as follows:…”

Multiomic characterization of disease progression in mice lacking dystrophin

“…A separate characterization of each of these omic datasets has been presented in previous publications [34][35][36][37][38][39]. Muscle RNA-seq showed that a considerable number of genes were differentially expressed between WT mice and the three dystrophic groups [34,36].…”

Urinary Metabolomic Profile of Youth at Risk of Chronic Kidney Disease in Nicaragua

“…We used a Holm-Bonferroni adjusted P-value for the difference in pathway-clustered metabolites between groups using the GlobalTest. 22 Pathways with both an effect score $0.1 and a Holm adjusted P-value ,0.05 were identified as key features differentiating groups. Within key pathways, metabolites reported at levels different than expected through a GlobalTest (P , 0.05) and as important to the metabolic pathways (importance score $0.1) were identified as key individual metabolites.…”