Martin Krzywinski / Genome Sciences Center / Martin Krzywinski / Genome Sciences Center / - contact me Martin Krzywinski / Genome Sciences Center / on Twitter Martin Krzywinski / Genome Sciences Center / - Lumondo Photography Martin Krzywinski / Genome Sciences Center / - Pi Art Martin Krzywinski / Genome Sciences Center / - Hilbertonians - Creatures on the Hilbert Curve
Safe, fallen down this way, I want to be just what I am.Cocteau Twinssafe at lastmore quotes

EMBO Practical Course: Bioinformatics and Genome Analysis, 5–17 June 2017.

design + visualization

VIZBI 2012

Visualization Principles Tutorial

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
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
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.


news + thoughts

Tabular Data

Tue 11-04-2017
Tabulating the number of objects in categories of interest dates back to the earliest records of commerce and population censuses.

After 30 columns, this is our first one without a single figure. Sometimes a table is all you need.

In this column, we discuss nominal categorical data, in which data points are assigned to categories in which there is no implied order. We introduce one-way and two-way tables and the `\chi^2` and Fisher's exact tests.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Tabular data. Nature Methods 14:329–330.

...more about the Points of Significance column

Happy 2017 `\pi` Day—Star Charts, Creatures Once Living and a Poem

Tue 14-03-2017

on a brim of echo,

capsized chamber
drawn into our constellation, and cooling.
—Paolo Marcazzan

Celebrate `\pi` Day (March 14th) with star chart of the digits. The charts draw 40,000 stars generated from the first 12 million digits.

Martin Krzywinski @MKrzywinski
12,000,000 digits of `\pi` interpreted as a star catalogue. (details)

The 80 constellations are extinct animals and plants. Here you'll find old friends and new stories. Read about how Desmodus is always trying to escape or how Megalodon terrorizes the poor Tecopa! Most constellations have a story.

Martin Krzywinski @MKrzywinski
Find friends and stories among the 80 constellations of extinct animals and plants. Oh look, a Dodo guardings his eggs! (details)

This year I collaborate with Paolo Marcazzan, a Canadian poet, who contributes a poem, Of Black Body, about space and things we might find and lose there.

Check out art from previous years: 2013 `\pi` Day and 2014 `\pi` Day, 2015 `\pi` Day and and 2016 `\pi` Day.

Data in New Dimensions: convergence of art, genomics and bioinformatics

Tue 07-03-2017

Art is science in love.
— E.F. Weisslitz

A behind-the-scenes look at the making of our stereoscopic images which were at display at the AGBT 2017 Conference in February. The art is a creative collaboration with Becton Dickinson and The Linus Group.

Its creation began with the concept of differences and my writeup of the creative and design process focuses on storytelling and how concept of differences is incorporated into the art.

Oh, and this might be a good time to pick up some red-blue 3D glasses.

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
A stereoscopic image and its interpretive panel of single-cell transcriptomes of blood cells: diseased versus healthy control.

Interpreting P values

Thu 02-03-2017
A P value measures a sample’s compatibility with a hypothesis, not the truth of the hypothesis.

This month we continue our discussion about `P` values and focus on the fact that `P` value is a probability statement about the observed sample in the context of a hypothesis, not about the hypothesis being tested.

Martin Krzywinski @MKrzywinski
Nature Methods Points of Significance column: Interpreting P values. (read)

Given that we are always interested in making inferences about hypotheses, we discuss how `P` values can be used to do this by way of the Benjamin-Berger bound, `\bar{B}` on the Bayes factor, `B`.

Heuristics such as these are valuable in helping to interpret `P` values, though we stress that `P` values vary from sample to sample and hence many sources of evidence need to be examined before drawing scientific conclusions.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Interpreting P values. Nature Methods 14:213–214.

Background reading

Krzywinski, M. & Altman, N. (2017) Points of significance: P values and the search for significance. Nature Methods 14:3–4.

Krzywinski, M. & Altman, N. (2013) Points of significance: Significance, P values and t–tests. Nature Methods 10:1041–1042.

...more about the Points of Significance column