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... Show moreWe 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. Show less
The main objective of this thesis is to improve the understanding of the role of helminth infections on the human gut microbiome and consequently on the human immune responses. We utilized the... Show moreThe main objective of this thesis is to improve the understanding of the role of helminth infections on the human gut microbiome and consequently on the human immune responses. We utilized the observations from a household randomized controlled trial in a helminth endemic area in Indonesia, where the microbiome data is the main outcome of interest. The microbiome data obtained via high throughput sequencing possess a compositional structure. Therefore, each bacteria category correlates with each other. In addition, the repeated measurement of the microbiome data imposes an extra correlation within bacteria category over time. We first developed a new statistical methodology to model the effect of infection as well as deworming on microbiome composition by taking into account the compositional nature of microbiome data as well as the correlation due to repeated measurements. Secondly, to model the effect of helminth infection on the gut microbiome composition and immune responses, a joint model is developed. This model is able to estimate the correlation between microbiome composition and immune responses. Show less
