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.
We have been preparing a new version of Gitools with many improvements, amongst which there is a new IGV search, the use of categorical scales and new data aggregation methods that can be used to annotate the heatmap.
Gitools is an interactive heatmap viewer which can also perform various analysis over the data. Heatmaps in Gitools can be multidimensional, with various values per cell, which is very practical for cancer genomics data analysis and visualization (read more).
Let us introduce the new features step by step.
A typical cancer genomics project nowadays screens the cancer genome, epigenome and transcriptome of a cohort of patients and identifies various types of alterations: Copy Number changes, Somatic Mutations, Gene Expression changes and others. This is the case of projects framed within The Cancer Genome Atlas or the International Cancer Genomics Consortium, as well as many others. Each of these types of alterations is represented in different data formats and it remains a challenge to integrate them to get a unified view of the process of alterations that leads to tumorigenesis. In Gitools it is possible to explore and analyze multi-value matrices in the form of interactive heatmaps, making it possible to work with various data dimensions at once. Read the rest of this entry »