BM432 Data Visualisation Workshop

Morgan Feeney

University of Strathclyde

Leighton Pritchard

University of Strathclyde

2026-10-21

1. Introduction

Learning Objectives

  • You should be able to critically analyse how data is visualised
  • You should be able to judge a figure’s clarity and potential for misunderstanding
  • You should be able to identify potential sources of bias resulting from the visualisation
  • You should understand how to create effective figures for your own work

2. Critique of Published Scientific Figures

Example 1

Figure 1: Small molecules identified in previous HTS increase GCase activity.

Your Critical Analysis of Example 1

Your Critical Analysis of Example 1

Reasons for the grade

Your Critical Analysis of Example 1

Suggestions for improvement

Critique 1.1

Issue

Dynamite plot: the lower extent of these error bars is not visible.

Solution

Use boxplots or 1D scatterplots

Critique 1.2

Issue

Incomplete presentation of statistical comparisons, e.g is there a difference between A16 and A18 in (B)?

Solution

Present a table of statistical differences instead, or alongside the figure.

Critique 1.3

Issue

Distance between bars makes comparison awkward.

Solution

Place things to be compared by the reader next to each other where possible, to facilitate visual comparison.

Critique 1.4

Issue

The scale on the micrographs (especially in panel C) is too small to read easily.

Solution

Increase the size of the scale relative to the figure so it can be read.

Critique 1.5

Issue

A visual control (bright-field image) is absent.

Solution

In addition to showing DAPI and immunofluorescence images of the cells, include a bright-field micrograph of the cells (no fluorescence).

Critique 1.6

Issue

The colour scheme is misleading because saturation represents different data across figure panels (compare 1 \(\mu\)M A18 in B vs 5 \(\mu\)M in D, and 1 \(\mu\)M A18 in E).

Solution

Be consistent with the visual messaging of colour (hue, saturation, and luminance).

Critique 1.7

Issue

Too many comparisons in B clutter the figure and compress the space available for showing data.

Solution

Present a table of statistical differences instead, or alongside the figure, to reduce clutter.

Critique 1.8

Issue

\(y\)-axis scales vary between panels, so quantitative comparison between panels is difficult.

Solution

Use the same \(y\)-axis scale in all panels to facilitate direct comparison.

Our Critical Analysis

Space for your results

Example 2

Figure 2: Endometriosis-associated macrophages exhibit significant transcriptomic heterogeneity.

Your Critical Analysis of Example 2

Your Critical Analysis of Example 2

Reasons for the grade

Your Critical Analysis of Example 2

Suggestions for improvement

Critique 2.1

Issue

UMAP plots (B, E) are highly manipulable and clustering/placement does not necessarily reflect objective measures.

Solution

Be cautious of over-interpretation of UMAP and other nonlinear dimensionality reduction plots.

Critique 2.2

Issue

Unpleasant clashing (R default) colour choices in (C).

Solution

Use an appropriate colour palette.

Critique 2.3

Issue

The proportion plot in (C) does not give information on absolute number, only proportion/composition.

Solution

A proportional areas plot spanning all clusters would represent both absolute count per group and compositional information.

Critique 2.4

Issue

Heatmap text is too small to read comfortably.

Solution

Present heatmap as a separate figure, or reduce the amount of information in the image.

Critique 2.5

Issue

Heatmap is missing a scale (is purple high and yellow low, or vice versa?)

Solution

Add a scale bar.

Critique 2.6

Issue

The experimental summary in (A) does not indicate order of operations.

Solution

Use arrows to indicate order of steps and/or dataflow.

Critique 2.7

Issue

Text is too small in general to read comfortably.

Solution

Increase font size and/or break up the panels into individual figures.

Critique 2.8

Issue

The figure is crowded and the separation of panels is unclear.

Solution

Use whitespace to guide reader “flow” through the figure and reduce crowding -in particular, cramming (C) under the inset from (B) makes the figure feel very crowded. Alternatively break the panels into multiple figures.

Critique 2.9

Issue

The overall message of the figure is unclear.

Solution

If the intent is just that the macrophages exhibit transcriptional heterogeneity, then (D) is probably sufficient. If other messages are intended, then revise for clarity.

Our Critical Analysis

Space for your results

Example 3

Figure 3: A C. difficile mutant lacking all three YkuD-type Ldts (\(\Delta\)ldt1-3) exhibits wild-type growth, morphology, and 3-3 cross-linking.

Your Critical Analysis of Example 3

Your Critical Analysis of Example 3

Reasons for the grade

Your Critical Analysis of Example 3

Suggestions for improvement

Critique 3.1

Issue

Dynamite plot: the lower extent of these error bars is not visible.

Solution

Use boxplots or 1D scatterplots

Critique 3.2

Issue

No complement of the triple mutant strain - missing data for an essential control?

Solution

Ensure that all controls are presented so that the experimental result can be interpreted properly.

Critique 3.3

Issue

Label obscures part of the image.

Solution

Relocate the label so data is not obscured.

Critique 3.4

Issue

Fluorescence wavelength not specified.

Solution

Include sufficient information that the reader does not need to refer to the main text. Overall a figure legend should provide enough detail for the figure to stand alone, but should not describe results/significance.

Critique 3.5

Issue

Colour schemes/palettes are inconsistent within panel A, and between panels B, D, and E.

Solution

Be consistent with the visual messaging of colour (hue, saturation, and luminance).

Critique 3.6

Issue

Excessive length of figure legend for panel A.

Solution

Break out panel A into its own figure.

Our Critical Analysis

Space for your results

Example 4

Figure 4: Functional characterization and overall structure of Rv1217c–1218c.

Your Critical Analysis of Example 4

Your Critical Analysis of Example 4

Reasons for the grade

Your Critical Analysis of Example 4

Suggestions for improvement

Critique 4.1

Issue

The rifampicin structure is purely decorative.

Solution

Remove unnecessary elements and avoid needless distractions in figures.

Critique 4.2

Issue

Dynamite plot: the lower extent of these error bars is not visible.

Solution

Use boxplots or 1D scatterplots

Critique 4.3

Issue

Colour scheme in (B) doesn’t seem purposeful and doesn’t add anything to the figure.

Solution

Use colour consistently to link or distinguish elements/groups/data, rather than for decoration.

Critique 4.4

Issue

The meaning of the grey regions in (C) and (D) is unclear.

Solution

The implied membrane in (C) and (D) could be labelled as such in the figure. By convention in microbiology, we would assume that the periplasm/extracellular space is “up” and the cytoplasm is “down” in (C) and (D) - but this should really be labelled to avoid any potential confusion.

Critique 4.5

Issue

Colours in (A) difficult to distinguish, especially with the red boxes which seem to skew blue closer to purple.

Solution

Instead of well images, a heatmap with clearer colour distinction could be presented.

Critique 4.6

Issue

Showing two cut-out bands is not an appropriate way to show Western blot data.

Solution

Present the complete blot.

Critique 4.7

Issue

Text in (D) is too small to read easily.

Solution

Break out panel into a separate figure or increase font size.

Our Critical Analysis

Space for your results

3. Summing Up

General Comments - Data Presentation Choices

Issue

Authors do not always choose the best or most appropriate way to present their data (e.g., dynamite plots)

Solution

When making data visualisation choices, consider how you can best present your data transparently to the reader so that they can evaluate your work (e.g., show the raw data).

Be aware of conventions in the field and discuss with colleagues to come to a consensus of the best and most transparent way to present your data.

General Comments - Colour Choices

Issue

Authors do not always choose the best or most appropriate colours when designing data visualisations.

Solution

When choosing colours, try to:

  1. Be consistent (always use the same colour to represent the same group or feature)

  2. Be accessible (be aware of the different types of colourblindness and design appropriately)

  3. Be aesthetically pleasing (choose colours that work well together, or choose well-designed colour palettes)

General Comments - Readability

Issue

Authors do not always make figures large enough or easy to read.

Solution

When designing figures, make sure that they are appropriately sized and accessible (font large enough to read easily at 100% size).

Include enough space between panels of a figure that allows the figures to “breathe” - the eye should flow smoothly from one element of the figure to the next in a natural way.

Try printing out a copy of the figure and look at it from arm’s length to see how it feels in terms of whitespace and use of “real estate” - then make appropriate edits to improve the figure.

General Comments - Message

Issue

Authors sometimes include too much data in a figure, or make a figure overwhelming in other ways.

Solution

When sitting down to make a figure, be sure to define a clear message in the first instance. Present the data that are necessary to convey that message. Remove anything extraneous. (Split into multiple figures if necessary.)

Some Very Insightful Comments From You

  • “The data is presented in a manner that would likely be inaccessible for people without prior experience. A move toward a more palatable/digestible format will facilitate better science communication in the future.”

  • “I think that it is also crucially important to learn how to present data well and responsibly…”

Further Reading