We have developed a methodology that allows integration of microarray data and meta information within a visualization in order to guide the investigator during data exploration and analysis. A simple mathematical framework is introduced that uses scoring functions to map meta information to relevance ratings of genes. To explore the potential of this framework we extended the traditional heatmap with new features to graphically represent the relevance ratings. These ratings are visualized by an additional color gradient, by scaling the vertical height of matrix rows, by rearranging rows or by inserting new columns into the heatmap. This visualization is called an enhanced heatmap. We have applied our approach to microarray data of the Saccharomyces cerevisiae cell cycle, complemented with supplemental data that we both derived from the microarray data itself and retrieved from public databases. Using these data we demonstrate how this visualization concept can be efficiently used to identify certain features of genes and to detect inconsistencies in the data. Thus, the investigator has the possibility to get an overview of data from various sources and at the same time can gain a deeper insight into the structure of the combined data. The concept is not restricted to heatmaps, and can be used to extend further visualization techniques, such as profile plots. We found that our method is a powerful tool to integrate supplemental data into microarray visualizations and that it increases the efficiency of visual data exploration, which is a fundamental part of microarray data analyses.
Data Preprocessing and Reducing for Microarray Data Exploration and Analysis