Before undertaking any statistical data analysis, it is fundamental to understand the nature of the measurements being made. For gene expression microarrays, it is not widely appreciated that the measurements are fluorescent intensities while the biological investigations are concerned with target concentrations. What is the relationship between these intensities and corresponding concentrations? The first part of the talk will explore this question, using data from spike-in experiments. Next, to provide insight into complex data and the resultant analyses, graphical displays can play a useful, central role. In particular, visualisation of gene expression profiles can potentially contribute to the identification of co-regulated genes and gene function. This will be illustrated using a well-studied leukaemia data set. Finally, the use of gene expression data to find "signature/s" that predict disease progression and/or outcome will be discussed using a well-known breast cancer data set, and some novel approaches to finding stable signatures will be proposed.