1. Scientific Figure Designv
Simon Andrews, Anne Segonds-Pichon, Boo Virk, Jo Montgomery

2. Figures are the way your science is presented to an audience
Before we start, I’d like you to have a look at this graph; talk to the person next to you about its pitfalls

3. What this course covers…
Theory of data visualisation
Why do some figures work better than others?
Applying theory to common plot types
Ethics of data representation
Using graphic design
Editing bitmap images in GIMP
Vector editing and compositing in Inkscape

4. What this course doesn’t cover…
How to draw graphs in specific programs
R Introduction
Statistics with R
Statistics with GraphPad
Plotting with R/ggplot

5. Timetable Morning Coffee Afternoon Coffee Introduction
Data Visualisation Theory
Coffee
Data Representation Practical
Plots and ethics talk
Design theory talk
Afternoon
GIMP Tutorial
GIMP Practical
Coffee
Inkscape Tutorial
Inkscape Practical
Final practical

6. Data Visualisation Process
Collect Raw Data
Process and Filter Data
Clean Dataset
Exploratory Analysis
Generate Conclusion
Clean Dataset
Exploratory Analysis
Generate Conclusion

7. Exploratory visualisation
Understand your data
Multiple ways to present and summarise
Crude representations
Interactive
Not intended for final publication
Can be adapted for publication

9. Reference visualisation
Using your data as a resource
Allows users to look up data of interest
Tabular / Configurable
Interactive

11. Illustrative visualisation
Intended to convey a specific point
Carefully chosen subset of data
Optimised presentation
Good design
Used for figures in papers

13. What makes a good figure?
Has a clear message
Helps to tell a story
Adds to the text, and links to it
Is focused
Don’t confuse one message with another
Is easy to interpret correctly
Good data visualisation
Good design
Is an honest and true reflection of the data

14. The theory of data visualisation
Simon Andrews, Phil Ewels

15. Data Visualisation
A scientific discipline involving the creation and study of the visual representation of data whose goal is to communicate information clearly and efficiently to users.
Data Visualisation is both an art and a science.

17. ISBN-10:

18. Data Viz Process Collect Raw Data Process and Filter Data
Clean Dataset
Exploratory Analysis
Generate Visualisation
Generate Conclusion

19. A data visualisation should…
Show the data
Not distort the data
Summarise to make things clearer
Serve a clear purpose
Link to the accompanying text and statistics

20. Different representations have common elements

21. Graphical Representations
Basic questions
How are you going to turn the data into a graphical form (weight becomes length etc.)
How are you going to arrange things in space
How are you going to use colours, shapes etc. to clarify the point you want to make

22. Marks and Channels Marks Channels Geometric primitives
Lines
Points
Areas
Used to represent data sets
Channels
Graphical appearance of a mark
Colour
Length
Position
Angle
Used to encode data

23. Figures are a combination of marks and channels
1 Mark = Rectangle
1 Channel = Length of longest side
1 Mark = Circle segment
1 Channel = Angle
1 Mark = Diamond shape
2 Channels = X position, Y position
1 Mark = Circle
4 Channels:
X position
Y position
Area
Colour

24. Golden Rules Effectiveness Expressiveness
Encode the most important information with the most effective channel
Expressiveness
Match the properties of the data and channel

25. Types of channel Quantitative Qualitative Position on scale Length
Angle
Area
Colour (saturation)
Colour (lightness)
Qualitative
Spatial Grouping
Colour (hue)
Shape

26. Colour Technical representations of colour
Red + Green + Blue (RGB)
Cyan + Magenta + Yellow + Black (CMYK)
Perceptual representation of colour
Hue + Saturation + Lightness (HSL)

27. HSL Representation Hue = Shade of colour = Qualitative
Saturation = Amount of colour = Quantitative
Lightness = Amount of white = Quantitative
Humans have no innate quantitative perception of hue but we have learned some (cold – hot, rainbow etc.)
Our perception of hue is not linear

28. Types of channel Quantitative Qualitative Position on scale Length
Angle
Area
Colour (saturation)
Colour (lightness)
Qualitative
Spatial Grouping
Colour (hue)
Shape

29. Data Types Quantitative Ordered Categorical
Height, Length, Weight, Expression etc.
Ordered
Small, Medium, Large
January, February, March
Categorical
WT, Mutant1, Mutant2
GeneA, GeneB, GeneC

30. Golden Rules Effectiveness Expressiveness
Encode the most important information with the most effective channel
Expressiveness
Match the properties of the data and channel

31. Golden Rules Effectiveness Expressiveness
Encode the most important information with the most effective channel
Expressiveness
Match the properties of the data and channel

32. Effectiveness of quantitation2X 7X
4.5X
1.8X
16X
3.4X

33. Quantitation Perception

34. Golden Rules Effectiveness Expressiveness
Encode the most important information with the most effective channel
Expressiveness
Match the properties of the data and channel

35. Most Quantitative Representations
Good quantitation
Bar chart
Stacked bar chart with common start
Stacked bar chart with different starts
Pie charts
Bubble plots (circular area)
Rectangular area
Colour (luminance)
Colour (saturation)
Poor quantitation

36. Discriminability
If you encode categorical data are the differences between categories easy for the user to perceive correctly?

37. Qualitative Discrimination
How many colours can you discriminate?

38. Qualitative Discrimination
How many (fillable) shapes can you discriminate?
Can combine with colour, but need to maintain similar fillable areas

39. Qualitative Discrimination
Can combine with colour, but need to maintain similar fillable areas

40. Separability
The effectiveness of a channel does not always survive being combined with a second channel.
There are large variations in how much two different channels interfere with each other
Trying to put too much information on a figure can erode the impact of the main point you’re trying to make

41. Separability There is no confusion between the two channels
Larger points are easier to discriminate than smaller ones
We tend to focus on the area of the shape rather than the height/width separately
Humans are very bad at separating combined colours

42. Popout A distinct item immediately stands out from the others
Triggered by our low level visual system
You don’t need to actively look at every point (slow!) to see it

43. Popout (find the red circle)

44. Popout
Speed of identification is independent of the number of distracting points

45. Popout
Colour pops out more than shape

46. Popout Mixing channels removes the effect (Find the red circle)

47. Use of space
Where you want a viewer to focus on specific subsets of data you can help their perception by using the layout or highlighting of data to draw their attention to the point you’re making

48. Grouping

49. Grouping
Exon CGI Intron Repeat

50. Ordering
Is a monkey heavier than a dog?

51. Containment / Linking
Wild Type
Mutant

52. Validation Always try to validate plots you create
You have seen your data too often to get an unbiased view
Show the plot to someone not familiar with the data
What does this plot tell you?
Is this the message you wanted to convey?
If they pick multiple points, do they choose the most important one first?

53. General Rules No unnecessary figures One point per figure
Does a graphical representation make things clearer?
Would a table be better?
One point per figure
Design each figure to illustrate a single point
Adding complexity compromises the effectiveness of the main point
No absolute reliance on colour
Figures should ideally still work in black and white
Colour should help perception

54. Making effective use of common plot types
Anne Segonds-Pichon
Simon Andrews
Phil Ewels

55. Types of plot Things you can illustrate

56. Plot Properties Exploration, Presentation or both? Effectiveness
Scalability
Options
Potential Problems

57. Distributions

58. Histograms / Density Plots
Exploration or Presentation
Effectiveness
Scalability
Both
Good
Poor

59. Histogram Options / Problems
Bin Size
Too few categories
Too many categories
Discrete Data

60. Box Plots Exploration or Presentation Effectiveness Scalability
Cutoff = Q1 – 1.5*IQR
Median
Maximum
Interquartile Range (IQR): 50% of the data
Lower Quartile (Q1) 25th percentile (1st quartile)
Outlier
Upper Quartile (Q3) 75th percentile (3rd quartile)
Minimum
Exploration or Presentation
Effectiveness
Scalability
Presentation
Good

61. BoxPlot Problems Assumes a large, normally distributed dataset
Misleading plots from small or non-normal datasets
In most cases there are better alternatives

62. Bean Plots Exploration or Presentation Effectiveness Scalability Both
Beans (Individual data points)
Data Density
Sample mean
Global mean
Exploration or Presentation
Effectiveness
Scalability
Both
Good
Good / Intermediate

63. BoxPlot vs Beanplot
Bimodal
Uniform
Normal

64. Comparisons

65. Stripcharts Exploration or Presentation Effectiveness Scalability Both
Good
Poor

66. Barplot Exploration or Presentation Effectiveness Scalability
Good

67. Barplot Options Selection of suitable confidence measures
Standard error
Standard deviation

68. Barplot Problems
Setting a suitable baseline

69. Barplot Options / Problems
Dealing with ratio data

70. Confidence Interval Plots
Exploration or Presentation
Effectiveness
Scalability
Presentation
Good

71. Relationships

72. Line Graphs Exploration or Presentation Effectiveness Scalability Both
Good
Poor

73. Line Graph Problems Discrete Data Implies interpolation
Can be useful for exploration
Shouldn’t use for presentation

74. Scatterplots Exploration or Presentation Effectiveness Scalability
Both
Good
Intermediate

75. Scatterplot Options / Problems
Large Data
Equality of Axes

76. Composition

77. Pie Charts Exploration or Presentation Effectiveness Scalability Both
Intermediate
Poor

78. Stacked Bar Charts Exploration or Presentation Effectiveness
Scalability
Both
Good / Intermediate
Intermediate

79. Stacked Bar Chart Options
Scaling and Ordering

80. Heatmaps Exploration or Presentation Effectiveness Scalability Both
Poor
Excellent

81. HeatMap Options
Clustering

82. HeatMap Options Colours
Turns quantitative differences into categorical

83. Simon Andrews, Anne Segonds-Pichon
Ethics of data representation
Simon Andrews, Anne Segonds-Pichon

84. Data Visualisation Process
Collect Raw Data
Process and Filter Data
Clean Dataset
Exploratory Analysis
Two parts of the process where visualisation is important. They have different requirements and will need different visualisations.
Generate Visualisation
Generate Conclusion

85. when it comes to data visualisation?
What is Ethics
when it comes to data visualisation?
The figure/graph/image should show what is actually happening and not what you want to happen.
Different ways of being unethical:
not exploring/getting to know the data well enough,
misusing your chosen graphical representation.
deliberately showing the data in a misleading manner,
choosing the ‘most representative’ image/experiment.

86. Is my plot ethical?
Would a reader come to a different conclusion if they could see the details of the data which were omitted from the plot?

87. Advertising and politics are built on unethical data representation.

88. Not exploring/getting to know the data well enough
One experiment: change in the variable of interest between CondA to CondB.
Data plotted as a bar chart.

89. Not exploring/getting to know the data well enough
Five experiments: change in the variable of interest between 3 treatments and a control.
Data plotted as a bar chart.
Comparisons:
Treatments vs. Control
p=0.001
Exp3
Exp4
Exp1
Exp5
Exp2
p=0.04
p=0.32

90. Choosing the wrong axis/scale
Example: increase in salary in the last term.

91. Choosing the y-axis/scale
Be careful with Linear vs. logarithmic scale.

92. Choosing the y-axis/scale
Inappropriate use of a log scale can artificially minimise differences
Linear scale
Logarithmic scale

93. Choosing the y-axis/scale
Logarithmic axis should be used for:
Logarithmically spaced values
Lognormal data

94. Simply Cheating: Manipulating images ‘Playing’ too much with contrast
“Adjusting the contrast/brightness of a digital image is common practice and is not considered improper if the adjustment is applied to the whole image.
Adjusting the contrast/brightness of only part of an image is improper, however, and this practice can usually be spotted by someone scrutinizing a file.”
Original
Brightness and Contrast Adjusted
Brightness and Contrast Adjusted Too Much:
Oversaturation

95. Manipulating images: Cutting gels
Simply Cheating:
Manipulating images: Cutting gels
Presenting bands out of context
Juxtaposing two lanes that were not next to each other in an original gel is common practice when preparing figures from hard copy photographs of the gel, and is acceptable manipulation if the figure is digital. Taking a band from one digital image and placing it in a lane in another is improper manipulation, which can usually be spotted by someone scrutinizing a file.
‘Rebuilding’ a gel
from several cuts

96. Image Manipulation can be detected
/JCI28824

97. Is my plot ethical?
Would a reader come to a different conclusion if they could see the details of the data which were omitted from the plot?

98. Design Theory v2018-11 Boo Virk Simon Andrews boo.virk@babraham.ac.uk

99. Why does good design matter?
Good design makes a great first impression
Good design makes for effective communication
Good design keeps the reader engaged
Art Palvanov (

100. Elements of design Contrast Alignment Space Colour Symmetry Repetition
Proximity
Size

101. Proximity – Find logical and visually appealing ways to structure panels
Which figures logically group together?
Are there sub-groups which should be connected?
Is there a logical flow to the ordering?
Is the layout balanced?

102. Alignment: Some arrangements are more visually appealing than others

103. We like symmetrical ordered layouts
Nutritional Immunology and Molecular Medicine Laboratory (2012) Modeling H. pylori using ENISI and Cell Designer

104. We like regular radial arrangements
A panoramic view of acute myeloid leukemia
Sai-Juan Chen, Yang Shen & Zhu Chen
Nature Genetics 45, 586–587 (2013)

105. Without symmetry we should consider visual weight
Bold Outline
Strong
Colour
Size
Variation
O’Callaghan CA (2000) Molecular basis of human natural killer cell recognition of HLA-E (human leucocyte antigen-E) and its relevance to clearance of pathogen-infected and tumour cells, Clinical Science 99, (9–17)
Greenblum S (2012) Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease, PNAS vol. 109 no. 2

106. Alignment: We are sensitive to aligned edges, even when they are separated50
100
150
200
Control
Treatment A
Treatment B 20 40 60 80
100
120 1 2 3 4 5 6
Day
Control
Treatment A
Treatment B
Control
Treatment A
Treatment B
Dead

107. Use a grid to help align disparate parts of a figure
Control
Treatment A
Treatment B
200
150
100 50 20 40 60 80
100
120 1 2 3 4 5 6
Day
Control
Treatment A
Treatment B
Control
Treatment A
Treatment B
Dead

108. Leave space between elements of figures

109. Colour can be an essential or optional part of any figure

110. Colour can have multiple uses
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
Try to make figures work in black and white

111. Sparing use of colour is most effective
Which is most effective at conveying your message?

112. Don’t invent your own colour schemes
Colorbrewer2.org

113. Use an appropriate colour scheme+
Sequential
Run between two values
Typically two main colours
Divergent
Diverging from a central value to a min and a max
Typically three colours
Categorical
Colours have no intrinsic ordering - +

114. If possible try to consider colour blind users
Affects 1:12 men and 1:200 women worldwide
“If a submitted manuscript happens to go to three male reviewers of Northern European descent, the chance that at least one will be colour blind is 22 percent.”

115. You can see how well your figure works for colour blind people
Gradients are easy to change
Categorical colours are very limited
Basic interpretability in black and white is ideal
Normal colour vision
Protanopia

116. When overlaying information, make sure you have sufficient contrast
Poor contrast
Good contrast
Poor contrast
Good contrast
Vibrating colour
Busy background

117. Add overlays to increase contrast
Poor contrast
Good contrast

118. Keep text and fonts simple
All fonts for figures should use sans serif fonts
All text in figures should be black or white
sans-serif
serif
Wild type
Knockout
Wild type
Knockout

119. Keep text horizontal

120. Keep text horizontal Numbers are small, text is big
All graphs still work when rotated 90o

121. Make sure appropriate labels are added
Each axis is labelled
Quantitative axes have units
Colour scheme is explained
Point shapes are explained
You need enough annotation that the figure is understandable on its own.

122. Make sure all text is legible at the final printed size
6 point font is the smallest you can comfortably read
(just over 2mm height on paper)

123. When resizing be aware of what can and cannot have its aspect ratio changed
Things that always need to maintain their aspect ratios:
Images
Text
Circular objects
Axes with comparable units X 

124. When resizing be aware of what can and cannot have its aspect ratio changedX 

125. Simpler figures are easier to interpret

126. Simpler figures are easier to interpret

127. Consistency across figures makes interpretation easier
Same colour/marker for same group
Size of comparable figures should be the same
Positions of axis titles and labels
Font styles and sizes
Order: If presented ‘Sample A’ and then ‘Sample B’, maintain this throughout

128. Elements of design Contrast Alignment Space Colour Symmetry Repetition
Proximity
Size