1. Exploring and Presenting Results
Laura Biggins v

2. I have my results… what next?

3. I have my results… what next?
1. Interpret and explore 2. Presenting results

4. Interpreting and exploring results
How can the results be displayed so that I can interpret and explore them most easily?
Understanding the functional terms (incl GO hierarchy)
Finding relevant information amongst the masses (GOslim, redundant terms, clustering)
Presenting results
How should I present my results?
What information should I include?
2 main things here
- How should we presenting results in a way that makes sense – graphically or textually – how to display it and what information should be included – what is relevant?
- How should results be presented to ourselves – how can they be displayed so that we can interpret them? Assuming we’ve got a large table of results. The issue here is selecting relevant information – condensing
Small lists
Let’s think about what makes sense to display
If you’re comparing between 2 datasets and showing a comparison between one or two categories

5. Interpreting and exploring results
Sometimes tables may be sufficient .

6. …but large tables are often difficult to interpret

7. Overlaps in gene lists Gene ontology is hierarchical general Parent
Root ontology terms 1 2 3
general
specific
Parent
Child

8. Overlaps in gene lists
Most genes are associated with more than one GO term
Many annotation sources
e.g. G:profiler - Gene Ontology terms, biological pathways, regulatory motifs of transcription factors and microRNAs, human disease annotations and protein-protein interactions

9. Exploring overlaps

10. Exploring hierarchy - Panther
Interactive pie charts to explore the lower level categories – nice for exploring if you’re interested in some of the categories.
This is separate to the overrepresentation test

11. Exploring ontology structure - GOrilla
Good thing about GOrilla – shows the hierarchy of the GO terms
Also a way of presenting results
cbl-gorilla.cs.technion.ac.il/

12. Exploring ontology structure - GOrilla
But – if lots of info, difficult to interpret
cbl-gorilla.cs.technion.ac.il/

13. g:profiler - filtering

14. DAVID – functional annotation clustering
david clusters by overlapping genes

15. GOrilla - REVIGO
Gorilla does have an option that allows the results to be displayed in REVIGO.

16. GOrilla - REVIGO

17. Reducing redundancy Use a filtering tool
Use a GOslim – may lose detail
Use a clustering tool
Select non-redundant terms yourself – be consistent
P-value filter, top x number of categories, largest categories, most enriched
Could be worth trying a GOslim

18. Displaying results What information should I include?
How should I present my results?

19. What information should be included?
Choosing what to display

20. Figure examples - bad
From panther – ugly and not ok – large categories may have much larger numbers

21. Figure examples – better bar charts
The number of genes should not be displayed.
DAVID Enrichment score = the geometric mean (in -log scale) of member's p-values in a corresponding annotation cluster, is used to rank their biological significance. Thus, the top ranked annotation groups most likely have consistent lower p-values for their annotation members.

22. Figure examples - table
If you want to include more information than can be easily interpreted on a graph, include a table.
How to select key terms to put in the table.
What numbers to include?

23. Figure examples - GOplot
R package to display results from ontology analyses
Circle area is proportional to the number of genes
Area of circle is proportional to the number of genes

24. Figure examples - GOplot

25. ClueGo App within Cytoscape
ClueGO integrates GO terms as well as pathways
Creates a functionally organized GO/pathway term network

26. Summary Explore the data – use common sense
Do not try and plot absolutely everything
Choose a method to deal with redundant terms
Think about what you’re plotting and whether it makes sense
Do not be afraid of including tables

27. Practical