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... Show moreThe 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. Show less
Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To... Show moreSpatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities. Show less
