On multivariate statistical methods for omics data analysis

In biomedical research, it has become common to collect data that measures biological functions in different biological levels of an organism. On the biomolecular level, this means that cells and tissues can be described by data gathered from different biomolecular domains, such as by data from the genome, transcriptome or proteome. By collecting such multi modular data, it is hoped that biological processes in cells and tissues can be better modeled and understood. Ultimately, this knowledge can help to better understand health and disease in the whole organism itself. This thesis discusses some of the statistical methods that aim to integrate this type of multi modular biomolecular data.