My main research focus at JRC-COMBINE was on the relation of gene expression data from different sources, aiming to position samples from newly performed experiments in the gene expression space spanned by publicly available datasets. The main idea of this big data approach was to utilize shared physiological processes to relate differential expression patterns using robust statistical methods to account for the biological heterogeneity, lab dependent effects, and technical noise. In many biological and medical research fields, such as stem cell research, drug development or analysis of disease status, it is important to integrate data from different sources, such as cell lines, in vitro cultures from primary cells or clinical biopsies. Data integration has the possibility to combine the knowledge derived from different experiments, providing a bigger picture surrounding the new data and improving the interpretation of results. The classical microarray gene expression analyses started with the integration on a single gene level, e.g. by interpreting differential gene expression in newly performed experiments using knowledge from gene annotation databases. These analyses were then extended to sets of genes, corresponding to specific biological functionalities, pathways or genomic locations. The gene set analysis summarizes the information of several genes, providing a broader view on the gene expression changes with better interpretability in terms of intracellular pathways and functionalities. A further step into this direction is a whole genome based comparison of phenotypical changes, linking the gene expression changes in the newly performed experiments to gene expression patterns that are associated with specific tissues, clinical parameters, or changes in the cellular environment. This direct link between samples from different sources can help bridging the gap between wet lab experiments and clinics eventually improving the efficiency and success of drug development processes.
|01/2011 - 05/2015||PhD student at AICES Graduate School, RWTH Aachen University|
|04/2009 - 11/2010||M.Sc. in Mathematics in Life Sciences, University of Applied Sciences Koblenz|
|03/2005 - 03/2009||Diploma in Biomathematics, University of Applied Sciences Koblenz|
Michael Lenz (Schuppert Group)
Michael Lenz is now a postdoctoral researcher at Maastricht Centre for Systems Biology, Maastricht University