Multivariate Data Analysis

In modern life science research, omics data is generated at a fast pace. These data are multivariate by nature and have to be analyzed using multivariate data analysis tools. The challenges are the heterogeneity of the data sets, the high-dimensional nature and the different levels of measurement scales. New multivariate analysis tools are needed to tackle these challenges to discover meaningful patterns in (multiple) genomic data sets

Fusion of data and information

Measurements are performed on a complex system probing parts A, B and C of that system. The resulting data blocks X1, X2 and X3 contain mixed variation which has to be separated in sources (red/green is common; yellow is distinct; grey is irrelevant variation and noise). These quantified sources are then used to reconstruct the system.