VIZBI 2012
Visualization Principles Tutorial
This tutorial took place on Monday Mar 5th 2012 at VIZBI 2012 in Heidelberg Germany.
Introduction
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.
Agenda
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.
Visualization and Design Resources
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)
Blast from the past
Convolutional neural networks
Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman
Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.
The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).
Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.
Background reading
Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Neural network primer
Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon
In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.
Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.
We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Cell Genomics cover
Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.
Designed in collaboration with with Carlos Urzua.
Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).
Browse my gallery of cover designs.
Science Advances cover
My cover design on the 6 January 2023 Science Advances issue depicts DNA sequencing read translation in high-dimensional space. The image showss 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes.
More details about the design.
Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.
Browse my gallery of cover designs.