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[PDF] Designing more effective scientific figures - Bioinformatics Training

scientific figures (II) Aiora Zabala Principles of figure design Elements of a A Brief Guide to Designing Effective Figures for the Scientific Paper Advanced 

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Designing more efffective

scientiific ifigures (II)

Aiora Zabala

PhD Environment. VTP Graphic Design

az296, aiora.zabala@gmail.com 12

3Structure of this course

Why ifigure design?

Principles of ifigure design

Elements of a ifigure

Colour & ethicsGimp - bitmap (e.g. jpg)

Setting up a canvas

Layers and importing ifiles

Editing colour

Export formats and qualities

4Inkscape - vectorial (e.g. pdf)

Document properties

Create & manipulate objects

Composition

Import & export for publicationDealing with complexity

Choosing the right ifigure

Typography

Composition & layoutPRACTICALTHEORYMorning

Afternoon

Key ingredients: the tools

Elements: marks

and channels Data

Points, lines, areas

Colour

TypographyComposition

Grid and alignments

Balance

Hierarchy and focus

Elements: Marks and channels

Marks (geometric

primitives): used to represent data

Channels control the

graphical appearance of marks: used to encode data, can be combined

Images from Munzner

Identity channels: categorical/

qualitative attributesTypes of channel

Magnitude channels: ordered/

quantitative attributes

Images from Munzner

Types of channel (continued)

Rolandi et al 2011

Efffectiveness of each channel:

Quantitation perception

The perceived magnitude of

sensory channels follows a power law: S = INDepending on the N of a given type of sensation, its perception is magniified (e.g. colour saturation) or compressed (e.g. brightness)

Image from Munzner 2015

Choosing the type of ifigure

Text, table or ifigure?

-Text: one or two numbers -Table:

Exact numerical values

Small datasets (a ifigure may

be best avoided if it has low data density)

When the data presentation

requires many localised comparisonsTreatment 10.01

Treatment 20.13

Treatment 30.30

12300.050.10.150.20.250.30.35

Slide from Babraham BioinformaticsThings you can illustrate

Each ifigure tells a diffferent story

1988
1992
1996
2000
2004
2008
2012
20160

20,000

40,000

60,000

80,000

DemocraticRepublican1,000 votes

1988
1992
1996
2000
2004
2008
2012
20160

20,000

40,000

60,000

80,000

DemocraticRepublican

1,000 votes

2008
2012
20160

20,000

40,000

60,000

80,000

DemocraticRepublican1,000 votes

2008
2012

201658,000

62,000

66,000

70,000

DemocraticRepublican1,000 votesEach ifigure tells a story diffferently

Stripchart - comparison

Only one of the axis is

meaningful

To explore small

datasets (n < 100) and compare categories

The most basic plot

(rarely in publications)

Line chart - relationships

To show a trend of

continuous data (usually over time)

For matched, paired or

repeated data, and for time-series

To tell a story: how data

change, rather than the discrete values of the data

Carter 2013

Bar chart - comparison

To compare discrete

quantities of non- continuous data

For presenting results

and emphasise diffferences (not so much to explore)

Carter 2013

Bar chart - comparison

The choice of the x axis and of point of reference can afffect how comparisons are perceived

Slide from Babraham Bioinformatics

Bar chart variations

Stacked bar chartNormalised stacked

bar chart

For categorical data; heed the sample size

Pie chart - composition/ proportion

To show relative

proportions of a whole

Not a great idea, 'given

their low data-density and failure to order numbers along a visual dimension' (Tufte)

Alternative:

Polar area chart

Wickham, 2010

Bar chart alternative for comparisons:

Dotchart with conifidence intervals

Focuses attention on the

relative values and their measure of variability, rather than on the absolute values (absolute values are better conveyed using the heights - in a barplot)

Treatment1 2 3 4

Histogram - distribution

To show the distribution of a variable

and the relative frequency of values; to explore the data

Better on big datasets

Estimate of the probability distribution

of the variable

The number of bins (resolution) afffects

the perceived shape of the distribution; the same perceptive distortion can occur when using histograms with discrete data

Boxplot - distribution

Also box-and-whisker plot

Shows the central value,

the extremes, and the area where 50% of the values are located. -Usually median, minimum, maximum, lowest and highest quartiles

Particularly useful to

understand distribution of not-normal dataMedianMaximum

Cutofff = Q3 + 1.5*IQR

Lower

Quartile, Q1

25th percentile

(1st quartile)Outliers Upper

Quartile, Q3

75th percentile

(3rd quartile)

MinimumInterquartile

Range (IQR),

50% of

the data

Boxplot variation: Violin/ Bean plots

To the above, it adds a

stripchart of the actual datapoints

Shows the data

density

To understand the

distribution in more detailBimodalUniformNormal

DistribuitionsA bean= a

'batch' of data Data density mirrored by the shape of the polygonStripchart shows individual data

Image from Babraham Bioinformatics

Scatterplot - relationships

Babraham BioinformaticsTo show the relationship between two continuous variables

Scatterplot - relationships

For high-density data: use

colours or transparencyquotesdbs_dbs14.pdfusesText_20