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Kay Nieselt, Albert Pritzkau, Andreas Lehrmann (Center for Bioinformatics Tübingen, Tübingen, Germany; )When analyzing high-dimensional expression data one challenge is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties that become visible in the new coordinate system and which have been hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects in the data need to be applied. We have developed a stringent framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in antibiotic producing bacteria.