1. Presenting Results Laura Biggins laura.biggins@babraham.ac.uk v1.0 1

2. 2 I have my results in a table… what next? Plot everything?

3. Artefacts 3 Artefacts in the data can be caused by a whole myriad of reasons during any stage from library preparation to the final step of the analysis where the gene lists are produced. RNA-seq – transcript length, expression level Ribosomal, cytoskeleton, extracellular, secreted multi-mapping reads – multi vs single ribosomal, translation Bisulphite – CpG density GC content – low and high GC fragments are underrepresented in libraries Location, average copy number Starting population of cells – remember to include background Completely random genes….

4. Differential power RNA-seq – transcript length, expression level Bisulphite – CpG density Non-random distribution – CpG density 4

5. Mapping – multi-mapping – genome Splice variants – Analysis at transcript vs gene level 5

6. Copy number variation 6

7. Categories to be wary of ribosomal cytoskeleton extracellular secreted translation glycoprotein 7

8. Beware… 8 GC < 0.35

9. 9 GC > 0.6

10. 10 All genes on chr 2, 8, 13

11. 11 No of transcripts > 4 Random sets of 1000 genes put through DAVID

12. Artefacts – checking your gene list 12 Make sure background is appropriate Be suspicious of some ontology categories – Ribosomal, cytoskeleton, extracellular, secreted, translation http://www.bioinformatics.babraham.ac.uk/shiny/gene_screen/ gene_screen – Shiny app to check for obvious differences in target genes compared to background population

13. What next? 13

14. Figure examples 14

15. Figure examples 15

16. GO graph 16 Genes are often annotated with many functions

17. Displaying Results 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? 17

18. Interpreting and Exploring Results How can the results be displayed so that I can interpret them most easily? Understanding the functional categories – GOrilla – hierarchical map – Panther - interactive pie charts Reducing redundancy – DAVID – clusters of similar functions – REVIGO - semantic similarity – GOslims 18

19. GOrilla 19 cbl-gorilla.cs.technion.ac.il/

20. Panther 20

21. GOrilla 21 cbl-gorilla.cs.technion.ac.il/

22. Exploring Results How can the results be displayed so that I can interpret them most easily? Understanding the functional categories – Gorilla – hierarchical map – Panther - interactive pie charts Reducing redundancy – DAVID – clusters of similar functions – REVIGO - semantic similarity – GOslims 22

23. GOrilla 23 cbl-gorilla.cs.technion.ac.il/

24. Exploring results 24

25. Reducing redundancy 25 http://revigo.irb.hr/

26. Reducing redundancy 26

27. Reducing redundancy 27

28. Reducing redundancy Use a clustering tool Use a GOslim – various versions available, may lose the interesting detail Select non-redundant terms yourself – be consistent – P-value filter, top x number of categories, largest categories, most enriched 28

29. What information should be included? 29

30. Figure examples 30

31. Figure examples 31

32. Figure examples 32

33. Summary Beware of artefacts – if something looks too good to be true it probably is…. Remember your background population 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 33

34. Exercise 2 34

35. 35

36. 36

37. Panther plots 37