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Cagatay Turkay, Julius Parulek, Helwig Hauser (University of Bergen, Bergen, Norway)Datasets with a large number of dimensions per data item, such as hundreds or more, are posing new challenges for both computational and visual analysis. Especially in genomic research, gene expression datasets where expression values for thousands of genes are recorded for a small number of samples are highly common. Our dual analysis model proposes the joint interactive visual analysis of high dimensional datasets with respect to their dimensions as well as with respect to the actual data items. In the context of gene expression data, we suggest the analysis of both the genes and the samples in parallel in linked visualizations. This novel perspective brings up new opportunities for the analysis of gene expression data. We also present a sample use case to investigate cancer sub-types in the data made available by The Cancer Genome Atlas Project.