@article{HuehneKesslerFuerstbergeretal.2018, author = {H{\"u}hne, Rolf and Kessler, Viktor and F{\"u}rstberger, Axel and Werle, Silke D. and Platzer, Matthias and S{\"u}hnel, J{\"u}rgen and Lausser, Ludwig and Kestler, Hans A.}, title = {3D Network exploration and visualisation for lifespan data}, volume = {19 (2018)}, pages = {390}, journal = {BMC Bioinformatics}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {https://doi.org/10.1186/s12859-018-2393-x}, year = {2018}, abstract = {Background The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. Results Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. Conclusion Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields.}, language = {en} } @article{MuellerLausserWilhelmetal.2020, author = {M{\"u}ller, Andr{\´e} and Lausser, Ludwig and Wilhelm, Adalbert and Ropinski, Timo and Platzer, Matthias and Neumann, Heiko and Kestler, Hans A.}, title = {A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data}, volume = {15}, journal = {Advances in Data Analysis and Classification}, number = {2}, publisher = {Springer}, address = {Berlin}, issn = {1862-5355}, doi = {https://doi.org/10.1007/s11634-020-00416-5}, pages = {463 -- 480}, year = {2020}, abstract = {Visualising data as diagrams using visual attributes such as colour, shape, size, and orientation is challenging. In particular, large data sets demand graphical display as an essential step in the analysis. In order to achieve comprehension often different attributes need to be displayed simultaneously. In this work a comprehensible bivariate, perceptually optimised visualisation scheme for high-dimensional data is proposed and evaluated. It can be used to show fold changes together with confidence values within a single diagram. The visualisation scheme consists of two parts: a uniform, symmetric, two-sided colour scale and a patch grid representation. Evaluation of uniformity and symmetry of the two-sided colour scale was performed in comparison to a standard RGB scale by twenty-five observers. Furthermore, the readability of the generated map was validated and compared to a bivariate heat map scheme.}, language = {en} }