This tutorial took place on Monday Mar 5th 2012 at VIZBI 2012 in Heidelberg Germany.
Jessie Kennedy · We will present fundamental principles of graphic design and visual communication that will help you create more effective interactive and print visualizations. You will learn how the purposeful use of salience, color, consistency and layout can help communicate large data sets and complex ideas with greater immediacy and clarity.
Cydney Nielsen · We will illustrate how these principles were implemented in ABySS-Explorer to visualize genome assemblies, an example to show you ways to apply design ideas to your own project.
Martin Krzywinski · At the end of the tutorial, you will apply what you have learned in an interactive group session in which you will design a figure illustrating a biological process.
Download agenda + participant list
9:30 – 10:15 | 45 min | Jessie Kennedy Principles |
10:15 – 10:25 | 10 min | break |
10:25 – 11:10 | 45 min | Cydney Nielsen Design Process |
11:10 – 11:20 | 10 min | form teams + select figure to critique |
11:20 – 11:30 | 10 min | break |
11:30 – 12:00 | 30 min | Martin Krzywinski Practical — Breakout session download papers |
12:00 – 13:00 | 60 min | team presentations Interactive suggested solutions |
It is not necessary to read the paper from which your figure was selected. I have included the papers only if you are interested in learning about the figure's context.
Effect of resolution on sequence visualization
Principles of effective color selection
Designing effective visualizations in the biological sciences (PSA Genomics Workshop, Seattle, 12 July 2011)
Circos and Hive Plots: Challenging visualization paradigms in genomics and network analysis (PSA Genomics Workshop, Seattle, 12 July 2011)
Designing effective visualizations in the biological sciences (Genome Sciences Center bioinformatics seminar, 26 August 2011)
Drawing Data: Creaing information-rich, informative and appealing figures for publication and presentation (BCCA workshop, 8 Jun 2011)
Behind a great figure is a design principle (BCB Spring Seminar, Iowa State, 27 Feb 2012)
Visualizing Quantitative Information (Genome Sciences Center bioinformatics seminar)
I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player
In many experimental designs, we need to keep in mind the possibility of confounding variables, which may give rise to bias in the estimate of the treatment effect.
If the control and experimental groups aren't matched (or, roughly, similar enough), this bias can arise.
Sometimes this can be dealt with by randomizing, which on average can balance this effect out. When randomization is not possible, propensity score matching is an excellent strategy to match control and experimental groups.
Kurz, C.F., Krzywinski, M. & Altman, N. (2024) Points of significance: Propensity score matching. Nat. Methods 21:1770–1772.
We'd like to say a ‘cosmic hello’: mathematics, culture, palaeontology, art and science, and ... human genomes.
All animals are equal, but some animals are more equal than others. —George Orwell
This month, we will illustrate the importance of establishing a baseline performance level.
Baselines are typically generated independently for each dataset using very simple models. Their role is to set the minimum level of acceptable performance and help with comparing relative improvements in performance of other models.
Unfortunately, baselines are often overlooked and, in the presence of a class imbalance, must be established with care.
Megahed, F.M, Chen, Y-J., Jones-Farmer, A., Rigdon, S.E., Krzywinski, M. & Altman, N. (2024) Points of significance: Comparing classifier performance with baselines. Nat. Methods 21:546–548.
Celebrate π Day (March 14th) and dig into the digit garden. Let's grow something.