Visualization and analysis of microarray and gene ontology data with treemaps

1. Visualization and analysis of microarray and gene ontology data with treemaps Eric H Baehrecke, Niem Dang, Ketan Babaria and Ben Shneiderman Presenter: Priya

2. Outline of the presentation • Background • Demo • Results and Discussion • Summary

3. Background

4. Challenges in visualization in Bioinformatics • Nature of data • Limited computer monitor views of data complexity • Limitations of typical browser presentation restrict data access • Restrictions in viewing both qualitative (gene families or biological function) and quantitative (such as RNA level, p-value) information simultaneously. • Need for an ideal platform to visualize multiple attributes simultaneously while allowing dynamic queries of data in the context of the GO classification.

5. Previous programs • Visualize and query microarray data Spotfire Genespring • Studies of GO FatiGO GoMiner MAPPFinder GoSurfer Severe lack in the ability to see patterns and obtain results on demand.

6. The solution – Treemap • Treemaps facilitate visualization of both hierarchical and quantitative information. • Treemaps are a space-filling visualization technique for hierarchical structures • Show attributes of leaf nodes by size and color-coding • Fills a critical void for genome researchers who want to integrate and query GO information with various quantitative data

7. Demo

8. Treemap Video Tutorial • http://www.cs.umd.edu/hcil/treemap/doc4.1/toc.html • http://www.cs.umd.edu/hcil/treemap/doc4.1/Video/TotalWithBuffer.html

9. Results and Discussion

10. Treemaps enable visual overviews of complex genome data with details on demand

11. Treemap allows access to data details without leaving the overview of the data

12. Display regions and facts • The data display and query window on the left • The details of selected node on the top right • The control panel on the bottom right • Data display and query window uses area to convey quantitative information • One of the greatest strengths of treemaps, is that they provide an overview of the data while allowing details-on-demand with rapid updates

13. Overviews of genome data can be rapidly obtained using Treemaps

14. Tools that facilitate visualization and queries of genome data • Size and color are two attributes that can be used to display quantitative differences in data using treemaps. • Labels can also be assigned to different gene attributes. • Users can zoom in and zoom out details on an area of interest.

15. Size, color, zoom

16. Color, size, label Demo • http://www.cs.umd.edu/hcil/treemap/doc4.1/Video/ColorSizeLabelAttribute.html

17. Zoom

18. Zoom Demo • http://www.cs.umd.edu/hcil/treemap/doc4.1/Video/Zooming.html

19. Treemaps allows users to query data in the context of the entire GO classification with little loss of time

21. Filters allow Treemap users to rapidly identify genes of interest based on quantitative attributes.

22. Hide Filters

23. Filter Demo • http://www.cs.umd.edu/hcil/treemap/doc4.1/Video/Filtering.html

24. Genes can be displayed in distinct categories based on quantitative attributes in Treemap

25. Useful research features • Genome researchers require rapid access to details about genes such as map position within the genome, nucleotide and protein sequence, and literature published to name a few examples. • Treemap 4.0 was adapted to contain a direct link to organism-specific websites within the main window of the lower right control panel. • Any queried file that is selected while holding the control key will be saved to a tab-delimited file that can then be used in other software such as hierarchical clustering.

26. Summary – The Best Part of Treemap • Available open source code • Excellent documentation • Well-defined User Interface • Helps make sense of the flood of information contained in the microarray data • Increases the chances of understanding interesting patterns