Spotfire 3.1 includes a heat map visualization, which is similar to a table or cross table except it shows colors instead of numbers and is space constrained to show all the information at once. When used with proper color management and sorted by attribute or by clustering results, it provides a powerful overview of your data. Heat maps used with clustering algorithms became very popular in biological research for visualizing massive microarray datasets to help decipher the relationship between genomic structure and function. But the Spotfire 3.1 heat map is flexible enough to visualize virtually any kind of data to uncover relationships and trends.
At first, the heat map can be a bit intimidating since it won’t necessarily reveal any interesting features of the data.
But by simply sorting by a column you can begin to see how the other columns generally relate to the sorted column.
The Spotfire 3.1 can automatically sort itself into groups of similar rows. The heat map is tightly integrated with a hierarchical clustering algorithm, and also has a special cluster visualization/navigation feature called a dendrogram to help navigate the results of the clustering.
Further, the clustering results can be navigated in a way that integrates with other visualizations. Here, the analyst can drag the pruning line (red dotted line) of the dendrogram to define the cutoffs for cluster groups, and that cutoff can dynamically drive (for example) the trellising of a line chart to reveal how the clusters appear in profile view.
The 3.1 heat map isn’t only for genomics research. It can be configured all the same ways as a cross table and help you find patterns in any numeric data. Here is an example of finding company stocks that behaved similarly over time:
Finally, the powerful coloring capabilities of Spotfire 3.1 open up many possibilities for using the heat map. You can create independent color schemes for different heat map columns, and even create your own dynamic coloring schemes based on custom expressions.
