The Composition of a statistical data-driven Workflow for untargeted Metabolomics Studies: Complexity and Applications

Abstract

The aim of the thesis was to develop and apply a statistical data driven workflow for untargeted metabolomics studies. The present work further deals with metabolomics and challenges of metabolomics studies, with strong focus on methodology. One challenge is the big amount of data deriving from the metabolomics studies; the statistical data driven workflow enables to handle this big amount of data by distinguishing between important and non-important information, depending on the particular scientific question (medical, biological). It consists of 15 steps in total including filtering-steps, drift correction via quantile regression and consecutively statistical analysis of the processed data. The workflow has been developed and constantly optimized with data from pre-clinical and clinical untargeted metabolomics studies. The largest and most important studies were used as representative examples and are described in the following. The main outcomes of the thesis are: -Implementation of the statistical data driven workflow works for pre-clinical and clinical untargeted LC-MS Metabolomics studies, with several adaptions depending on the scientific question -Successful application of the drift correction via quantile regression on a very large data set (

Datum der Bewilligung	2016
Originalsprache	Englisch
Gradverleihende Hochschule	Medizinische Universität Graz
Betreuer/-in	Andrea Berghold (Betreuer*in) & Harald Sourij (Betreuer*in)