PUB - Publikationen an der Universität Bielefeld

Motivation: Bioimaging techniques rapidly develop towards higher resolution and dimension. The increase in dimension is achieved by different techniques such as multi-tag fluorescence imaging, MALDI imaging or Raman imaging, which record for each pixel a N dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBI) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this paper we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multi-tag fluorescence imaging TIS (Toponome Imaging System). The MBI show FOV in tissue sections from a colon cancer study and we compare tissue from normal / healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as MCEP (Molecular Co-Expression Phenotypes) and provides a structural basis for a sophisticated multi-modal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE’s applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary).