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.
New data visualization features (screenshot)
IGV search (1 in screenshot)
Integrative Genomics Viewer (IGV) is a very powerful genomic browser that provides a complementary visualization of multidimensional genomics data to that of interactive heatmaps. Often it is useful to explore this type of data with the two tools at the same time. With that idea in mind we did a first step to integrate the two types of views. Using the IGV interface for other applications to send commands, Gitools can now locate any position within the human chromosome in your opened instance of IGV. In the screenshot we have the focus on the gene CPM (with the Ensembl id ENSG00000135678). If you click the link “Locate Id in genomic viewer (IGV)” in the left details panel, Gitools will locate the gene within IGV. If you have many genes selected, then Gitools will tell IGV to show them in split screen view.
Display name in color labels (2 in screenshot)
As of version 1.7.0 it is possible to directly display the label name if you choose to add a header of the type Colored Labels from annotations to your heatmap. You can show the names by checking the “Show cluster names” box when adding a new header from annotations.
Adding data header from aggregated values (3 in screenshot)
A new type of header can be added now to columns or rows: Aggregated heatmap from matrix data. In the screenshot we can see at (3) the mean expression values, calculated with the mean aggregation method for each row. The aggregation of the values can be calculated for the whole row, just for some selected columns or according to column annotations, as it is the case in the screenshot. Note that even though the mean expression values are shown in the rows annotations, the main heatmap is displaying alteration events.
The aggregated values are represented by the color scale (chosen by the user) and optionally by value text labels. We can see that, in the highlighted gene, the expression mean between the classical and mesenchymal subtype of the glioblastoma brain tumour differ substantially.
Categorical scale and scale drawing (4 in screenshot)
We have added a new color scale to Gitools: the categorical scale. It is designed to visualize categorical data, as shown in the screenshot. Each different data value is assigned to a color, which can be set by the user. The way that the color scales are drawn has been redesigned to be more intuitive.
Gitools has a new command interface similar to the one that of the IGV. It is now possible to programmatically connect to Gitools and load new data with annotation files. Check the documentation page
for more information.
Local sorting: It is now possible to sort a subset of columns and rows. Just select the rows you want to sort, and choose by Data -> Sort by... your sorting method applying to the rows. Remember that if you have some rows selected and sort the columns, only the selected rows will be considered to sort all columns.
New aggregation methods: Additionally to the existing aggregation methods (Mean, Sum, Absolute Sum, Multiplication and Sum of logarithms) we added four new methods which all can be used for the new data header from aggregated values. The new methods are Standard deviation, Variance, Minimum value and Maximum value.
Color scale memory: Gitools remembers to what data dimension which color scale has been selected. After having actively selected or modified a color scale, this very color scale will be selected after switching back to the data dimension.
Save analysis: When you perform an analysis “on the fly” in Gitools by selecting the menu Analysis -> Your analysis the save button on the top will be activated to let you save the analysis data and results to the hard disk.
Exploring multiple cancer genomics alterations with gitools
Visualizing mutually exclusive alteration patterns in cancer with gitools
Gitools published in plos one