Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - contact me Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca on Twitter Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Lumondo Photography Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Pi Art Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Hilbertonians - Creatures on the Hilbert Curve
Here we are now at the middle of the fourth large part of this talk.Pepe Deluxeget nowheremore quotes

design: fun


In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017

design + visualization

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

Linux and Genomics: Two Revolutions (USENIX 2004)

Visualization Principles VIZBI Book Chapter

Look for my chapter on visualization principles in the upcoming Visualizing Biological Data — a Practical Guide. This book is being written by VIZBI 2011 participants and edited by Sean O'Donoghue and Jim Procter.

VIEW ALL

news + thoughts

Predicting with confidence and tolerance

Wed 07-11-2018
I abhor averages. I like the individual case. —J.D. Brandeis.

We focus on the important distinction between confidence intervals, typically used to express uncertainty of a sampling statistic such as the mean and, prediction and tolerance intervals, used to make statements about the next value to be drawn from the population.

Confidence intervals provide coverage of a single point—the population mean—with the assurance that the probability of non-coverage is some acceptable value (e.g. 0.05). On the other hand, prediction and tolerance intervals both give information about typical values from the population and the percentage of the population expected to be in the interval. For example, a tolerance interval can be configured to tell us what fraction of sampled values (e.g. 95%) will fall into an interval some fraction of the time (e.g. 95%).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Predicting with confidence and tolerance. (read)

Altman, N. & Krzywinski, M. (2018) Points of significance: Predicting with confidence and tolerance Nature Methods 15:843–844.

Background reading

Krzywinski, M. & Altman, N. (2013) Points of significance: Importance of being uncertain. Nature Methods 10:809–810.

4-day Circos course

Wed 31-10-2018

A 4-day introductory course on genome data parsing and visualization using Circos. Prepared for the Bioinformatics and Genome Analysis course in Institut Pasteur Tunis, Tunis, Tunisia.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Composite of the kinds of images you will learn to make in this course.

Oryza longistaminata genome cake

Mon 24-09-2018

Data visualization should be informative and, where possible, tasty.

Stefan Reuscher from Bioscience and Biotechnology Center at Nagoya University celebrates a publication with a Circos cake.

The cake shows an overview of a de-novo assembled genome of a wild rice species Oryza longistaminata.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Circos cake celebrating Reuscher et al. 2018 publication of the Oryza longistaminata genome.

Optimal experimental design

Tue 31-07-2018
Customize the experiment for the setting instead of adjusting the setting to fit a classical design.

The presence of constraints in experiments, such as sample size restrictions, awkward blocking or disallowed treatment combinations may make using classical designs very difficult or impossible.

Optimal design is a powerful, general purpose alternative for high quality, statistically grounded designs under nonstandard conditions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Optimal experimental design. (read)

We discuss two types of optimal designs (D-optimal and I-optimal) and show how it can be applied to a scenario with sample size and blocking constraints.

Smucker, B., Krzywinski, M. & Altman, N. (2018) Points of significance: Optimal experimental design Nature Methods 15:599–600.

Background reading

Krzywinski, M., Altman, N. (2014) Points of significance: Two factor designs. Nature Methods 11:1187–1188.

Krzywinski, M. & Altman, N. (2014) Points of significance: Analysis of variance (ANOVA) and blocking. Nature Methods 11:699–700.

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments. Nature Methods 11:597–598.