Expression analysis of ~omics data using microarrays has become a standard procedure in the life sciences.
However, microarrays are subject to technical limitations and errors, which render the data gathered likely to
be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even
shown when the data is visualized using for example clustered heatmaps. Yet, this is highly useful when trying
not to omit data that is "good enough" for an analysis, which otherwise would be discarded as too unreliable
by established conservative thresholds. Our approach addresses this shortcoming by first identifying the margin
above the error threshold of uncertain, yet possibly still useful data. It then displays this uncertain data in
the context of the valid data by enhancing a clustered heatmap. We employ different visual representations for
the different kinds of uncertainty involved. Finally, it lets the user interactively adjust the thresholds, giving
visual feedback in the heatmap representation, so that an informed choice on which thresholds to use can be
made instead of applying the usual rule-of-thumb cut-offs. We exemplify the usefulness of our concept by giving
details for a concrete use case from our partners at the Medical University of Graz, thereby demonstrating our
implementation of the general approach.