In the latest version of Gitools, version 2.1, we have improved the clustering of heatmaps. Here we explain in detail on how to perform and interpret the hierarchical clustering result – and why it is a bit different than the rest.
Two years ago I attended the VIZBI 2012 to present our latest work on and with Gitools, a tool we use to explore interactive heatmaps of genomics data. The days spent in Heidelberg were fruitful and refreshing and after all also were an inspiration to write the review about Visualizing multidimensional cancer genomics data.
Heat-maps are graphical representations of data where values in a matrix are represented following a color scale. This way of representing data has proven to be a very intuitive and useful to visualize biological data. With large and complex data being generated in biology and specially in Cancer Genomics, static heat-maps are a limited option for data exploration. Instead we need to be able to analyze data in an interactive way in order to be able to extract knowledge from it.
A while ago we published a review about multidimensional cancer genomics data visualization in Genome Medicine. There, we focused on effective and common visualization techniques for exploring oncogenomics data and we discussed a selection of tools that allow researchers to effectively visualize multidimensional oncogenomics datasets. Since our research field is constantly evolving we thought we could share an update of the tools and links.
A challenge to all cancer genomic studies is to visually explore the generated complex data in a meaningful way to extract relevant knowledge. We have written a review on that topic which has been published last week in Genome Medicine.