Studies with high dimensional omics data Pathways, networks and statistical analysis

The development of high throughput technologies has moved biomedical research into the age of omics. By tracking molecules such as DNAs, RNAs, proteins and metabolites, scientists can have better understanding of human diseases. However, it is challenging to translate the large volume of omics data into knowledge. This thesis discussed the use of statistical approaches to decipher omics data. In this thesis, various of statistical approaches were applied to study phenotypes including inflammation, hypercholesterolemia and type 2 diabetes. We argue that statistical analysis is a construction process with a series of component decisions. The components in statistical analyses include data, prior knowledge as well as statistical models. In this thesis, we also discussed other challenges in omics data analysis, such as missing observations, batch effects and the choice of statistical models.