Scientific problems are rarely answered by direct comparison between A and B; next, we want to learn about the impact of parameter 1, 2, and 3 and further, stratify our patient groups according to shared properties and differences observed in our data. These are constructive and necessary steps when complex phenomena are the objective of the studies, e.g. a high volume of multivariate biomedical data are analyzed to give information about cause and diagnosis of a disease, an individual‘s prognosis and potentially, the responsiveness to a special medication.
As expected, complex phenomena are mirrored by the complexity of the respective multi-scale, highly interconnected and condition-dependent data. It is a real challenge to visually inspect all relevant data, decide which level of simplification is suitable to clearly reveal patterns in the data and points towards new insights. Especially, multidimensional data patterns are difficult to recognize when encoded into two dimensions. Therefore, the real artistry lies in the extraction of relevant information by applicable analytical methods and the explorative visualization of the results.