Visual analytics for spatially resolved omics data at single cell resolution: methods & applications

The deeper understanding of an organism's pathology is important for developing treatments. Over centuries of systematic research, clinical researchers have demonstrated that the more information they acquire about the cellular properties and their organisation in the tissue, the better they can understand an organism's functionality and disease progression. Over the last years, the advent of high-resolution imaging techniques have provided researchers with novel single-cell information, which empower researchers to precisely characterize the cells and explore how they are distributed in the tissue. However, the extraction of useful biological insights from the analysis of such novel and complex data, where experts do not know the intrinsic characteristics of the data nor the patterns they want to identify, requires an exploratory data analysis approach. Hence, the aim of this dissertation is the development of an end-to-end pipeline for the analysis of these highly multiplexed cellular images; from the preprocessing of the raw data over the exploration of cellular patterns and their association to clinical characteristics.LUMC / Geneeskund