Using effective visual aids is important for getting across your message when describing data. This can be in a presentation, poster or paper. This talk goes through some basic design tips that can help your visual aids look professional and work effectively. Written for the Enabling Excellence ETN. https://eetraining.wordpress.com/

1. Visual Aids
Phil Ewels
Using visual aids effectively
phil.ewels@scilifelab.se

2. What is a visual aid?
Figures Talk slides
Posters

3. What is a
Figures
Talk slides
Posters
• Helps your audience understand
• Often simple
• Interesting
» Humour / Different / Interactive
good visual aid?

4. What is a
Figures
Talk slides
Posters
good visual aid?
• Target audience / Content focus
• Good impressions / Visual aids
• Data visualisation / Workshop

5. Planning
• Target audience
» One size does not fit all
» Be sympathetic - put yourselves in their shoes
» Aim for the lowest common denominator
• Content planning
» Plan from the top down, not from the details up
» Write down an outline before you start
» Think about an “elevator pitch”

6. Elevator Pitch
distillation
of idea
all work
core concept

7. Elevator Pitch
distillation
of idea
all work
core concept
• Paper abstracts
• Graphical ToCs
• Talk poster / focus
• Coffee chat

8. Best / Worst Examples

9. Phil Ewels - Challenging samples for NGS / 20
Sample Setup
9
SciLifeLab ID Library Prep Starting amount Sequenced Reads
P1102_101 Manual 1000 ng 38,995,594
P1102_102 Manual 1000 ng 37,663,274
P1102_103 Manual 1000 ng 39,666,722
P1102_104 Manual 500 ng 35,332,272
P1102_105 Manual 200 ng 40,568,034
P1102_106 Manual 50 ng 47,044,650
P2011_1005 NeoPrep Run 1 25 ng 93,316,971
P2011_1006 NeoPrep Run 1 25 ng 115,648,988
P2011_1007 NeoPrep Run 1 25 ng 118,489,187
P2013_1004 NeoPrep Run 2 25 ng 72,128,476
P2013_1005 NeoPrep Run 2 25 ng 62,774,142

10. Phil Ewels - Challenging samples for NGS / 20
Sample Setup
10
Sample Library Prep Starting amount (ng) Sequenced Reads (M)
1 Manual
2 Manual
3 Manual
4 Manual
5 Manual
6 Manual
7 NeoPrep Run 1
8 NeoPrep Run 1
9 NeoPrep Run 1
10 NeoPrep Run 2
11 NeoPrep Run 2 25
25
25
25
25
50
200
500
1000
1000
1000
0 30 60 90 120

11. Visual Design

12. Introduction
• Don’t underes-mate the impact of your first few slides
• Fonts and visual presenta-on immediately set the
tone for your audience
• Anchor your work in the context of your audience’s
work
• Go slow - everyone will thank you for it
• This includes not using too much content
• Try not to read every bullet point from the screen -
talk around your slides instead
• Don’t put all of your bullets up at once, the
audience will read them instead of listening to you
• Now is the perfect -me to use a visual aid
NO

13. Introduction
• Don’t underestimate the impact of your first few slides
• Fonts and visual presentation immediately set the
tone for your audience
• Anchor your work in the context of your audience’s
work
• Go slow - everyone will thank you for it
• This includes not using too much content
• Try not to read every bullet point from the screen -
talk around your slides instead
• Don’t put all of your bullets up at once, the
audience will read them instead of listening to you
• Now is the perfect time to use a visual aid

14. Visual Design
• Visual design is important
• Visual design is easy
» Clear message
» Focussed
» Easy to read and interpret
» Honest and true reflection of the data
• Fonts. Colours. Layout.

15. Fonts
Body text
Figures
Presentations

16. Sans-serif Fonts
WARNING
serif fonts WARNING
serif fonts

17. Fonts
• Pick a font and stick to it
• Avoid MS defaults
» https://www.google.com/fonts
• Make use of font weights
Open sans
Lato
Roboto
(Arial)
Bold
Medium
Regular
Light
Thin
Hairline
Cambria
Calibri

18. Every time you use Comic Sans,
Faye will punch this adorable little bunny.
comic sans criminal.com

20. Colour Palettes

21. Google Material Design Guidelines
Set of guidelines about design
Aimed for app developers
Includes some nice colour palettes
Lots of good stuff about design theory
https://www.google.com/design/spec/style/color.html

22. https://coolors.co

23. Data Visualisation

24. Choosing a plot
• What type of graph best represents the
argument that you’re trying to make
• Which data are necessary
Relationship
Comparison
Composition
Distribution

25. What are you trying to show?
Distribution
Relationship / Comparison / Composition / Distribution

26. What are you trying to show?
Relationship
Relationship / Comparison / Composition / Distribution

27. What are you trying to show?
Composition
Relationship / Comparison / Composition / Distribution

28. What are you trying to show?
Comparison
Relationship / Comparison / Composition / Distribution

29. What are you trying to show?

30. What are you trying to show?

31. What are you trying to show?
• Distributions are not
relevant
• Only the median
value is needed
• Trends difficult to
compare
• Easier comparison

32. Making Comparisons
• Fast, accurate
judgement
» Length
» Slope
• Medium judgement
» Colour
» Patterns (eg. grouping)
• Slow, inaccurate
judgement
» Angle
» Area
» Text

33. Making Comparisons
• Length & Slope
» Bar / column charts
» Box plots
» Line graphs
• Colour
» Heatmaps
• Patterns
» Scatter plots
• Angle
» Pie charts
• Area
» Venn diagrams
• Text
» Tables
» Plot labels

34. Making Comparisons
1
2
3
4
5
6
7

35. Making Comparisons
1
2
3
4
5
6
7
0
0.5
1
1.5
2
1 2 3 4 5 6 7

36. Making Comparisons
1
2
3
4
5
6
7
• Angles are bad for comparison
• Legend is disassociated from plot
• Requires colour link for series identification
0
0.5
1
1.5
2
1 2 3 4 5 6 7

37. Making Comparisons

38. Making Comparisons

39. Keep it simple

40. Keep it simple
0.0
0.5
1.0
1.5
2.0
1 2 3 4 5 6 7
Series1 Series2
• Use of 3D was arbitrary
• No need for colour and texture

41. Aspect Ratio
• Elements that need accurate aspect ratios:
» Images
» Text
» Anything circular
» Axes with comparable units

42. Aspect Ratio

43. Aspect Ratio

44. Aspect Ratio

45. -6
-4
-2
0
2
4
6
8
10
12
14
-6 -4 -2 0 2 4 6 8 10 12 14
log10FPKM
log10 FPKM
Aspect Ratio
-6
-4
-2
0
2
4
6
8
10
12
14
6 -4 -2 0 2 4 6 8 10 12 14
log10FPKM
log10 FPKM

46. Features of a good plot
• Minimalistic
• Suitable plot type
• Big and clear
• Attractive (avoid defaults)
• The test - can you draw it from memory?
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7
0.0
0.5
1.0
1.5
2.0
2.5
1
2
3
4
5
6
7

47. Using a graph
• Hold audience focus
» Talk through your data
» Don’t show everything at once
• Use layering for complex plots
» Progressively add data

48. Colour

49. Colour
• Colour can be used to:
» Highlight specific data
» Group categories of data
» Encode quantitative values
• The more selective you
are with colour, the
greater its effect
0.0
0.2
0.5
0.7
0.9
1 2 3 4 5 6
0.0
0.2
0.5
0.7
0.9
1 2 3 4 5 6

50. Colour Scales
• Represent quantitative data
• Sequential
• Divergent
• Categorical

51. Colour Sensitivity

52. Colour Sensitivity
Humans do not perceive the spectrum evenly
Spectrum is a bad colour scheme with which to encode data

53. Bad Defaults
Jet
Luminescence
afmhot
Luminescence
YlOrRd
Luminescence
not even
not even

54. Scale types
Categorical Sequential

55. Scale types
Sequential (should be categorical)
Which category is this?

56. Colour Blindness
• Common in Northern European men
• Colour schemes such as Magenta – Green
designed to be colour blind friendly
22% chance at least one colour blind
3 NE male
reviewers

57. Colour Blindness
Normal Vision Protanopia

58. Colour Blindness
Normal Vision Protanopia

59. Color Brewer
http://colorbrewer2.org
Sequential Divergent Categorical

60. Summary

61. Summary
• Decide your key points early
» Build around target audience
• Show only what you need to
» Bullets, not prose
» Suitable graphs
» Avoid defaults
• Use data as a visual aid
colorbrewer2.org
google.com/fonts
flaticon.com
coolors.co
Adobe
Illustrator
Inkscape
(free)

62. Credits
Course written by Phil Ewels. Some material developed
whilst working at the Babraham Institute in Cambridge,
UK. Now working at the National Genomics Infrastructure,
part of the Science for Life Laboratory in Stockholm,
Sweden.
Find more at http://phil.ewels.co.uk