Avoiding the formation of unwanted clusters of similar elements when dividing data into groups is of great importance for the analysis of medical data. Psychologists and computer scientists from Heinrich Heine University Dusseldorf (HHU) developed a new method to solve this “anticlustering” problem in 2020. Together with researchers from the University of California, San Francisco (UCSF), they have now developed an extension, which is important for analysis of high-throughput sequencing data and more. The researchers describe their new tool in the context of an application to the chronic disease endometriosis in the journal Cell Reports Methods.
Ensuring appropriate allocation: Researchers develop anticlustering method for sequencing analysis
