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
And whatever I do will become forever what I've done.Wislawa Szymborskadon't rehearsemore quotes

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

data visualization + art

Martin Krzywinski @MKrzywinski
To view the art you'll need a pair of red-blue 3D glasses.
The data will stand out—and you will too.

BD Genomics stereoscopic art exhibit — AGBT 2017

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

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Our art exhibit at AGBT 2017 asked new school questions in old school ways.
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski

the art of storytelling in science

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Instead of 'explain, not merely show,' seek to 'narrate, not merely explain.' Krzywinski M & Cairo A (2013) Points of View: Storytelling. Nat. Methods 10:687.

Science cannot move forward without storytelling. While we learn about the world and its patterns through science, it is through stories that we can organize and sort through the observations and conclusions that drive the generation of scientific hypotheses.

With Alberto Cairo, I've written about the importance of storytelling as a tool to explain and narrate in Storytelling (2013) Nat. Methods 10:687. There we suggest that instead of "explain, not merely show," you should seek to "narrate, not merely explain."

Our account received support (Should scientists tell stories. (2013) Nat. Methods 10:1037) but not from all (Against storytelling of scientific results. (2013) Nat. Methods 10:1045).

A good science story must present facts and conclusions within a hierarchy—a bag of unsorted observations isn't likely to engage your readers. But while a story must always inform, it should also delight (as much as possible), and inspire. It should make the complexity of the problem accessible—or, at least, approachable—without simplifications that preclude insight into how concepts connect (they always do).

the story of making science stories

Just like science, explaining science is a process—one that can be more vexing than the science itself!

In science one tries to tell people, in such a way as to be understood by everyone, something that no one ever knew before. But in poetry, it’s the exact opposite.
—Paul Dirac, Mathematical Circles Adieu by H. Eves [quoted]

I have previously written about the process of taking a scientific statement (Creating Scientific American Graphic Science graphics) and turning it into a data visualization or, more broadly, visual story.

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
December 2015. Composition of bacteria in household dust.
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
June 2015. Relationship between genes and traits.
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
September 2014. Similarity of human, Denisovan, chimp, bonobo, and gorilla genomes.

The process of the creation of one of these visual stories is itself a story. A story about how the genome is not a blueprint, a discovery of Hilbertonians, which are creatures that live on the Hilbert curve, how algorithms for protein folding can be used to generate art based on the digits of `\pi`, or how we can make human genome art by humans with genomes. I've also written about my design process in creating the cover for Genome Research and the cover of PNAS. As always, not everything works out all the time—read about the EMBO Journal covers that never made it.

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Cover image accompanying our article on mouse vasculature development. Biology turns astrophysical. PNAS 1 May 2012; 109 (18)
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Cover image accompanying Spark: A navigational paradigm for genomic data exploration. Genome Research 22 (11).
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Pi Day 2014 poster | 132 paths with E=-23 of 64 digits of Pi, sorted by aspect ratio.

Here, I'd like to walk you through the process and sketches of creating a story based on the idea of differences in data and how the story can be used to understand the function of cells and disease.

the difference is in the differences

The visual story is a creative collaboration with Becton Dickinson and The Linus Group and its creation began with the concept of differences. The art was on display at AGBT 2017 conference and accompanies BD's launch of the Resolve platform and "Difference of One in Genomics".

Starting with the idea of the "difference of one", our goal was to create artistic representations of data sets generated using the BD Resolve platform, which generates single-cell transcriptomes, that captured a variety of differences that are relevant in genomics research.

The data art pieces were installed in a gallery style, with data visualization and artistic expression in equal parts.

The art itself is an old school take on virtual reality. Unlike modern VR, which isolates the participants from one another, we chose a low-tech route that not only brings the audience closer to the data but also to each other.

data in the art

The data were generated using the BD Resolve single-cell transcriptomics platform. For each of the three art pieces, we identified a data set that captured a variety of differences.

  1. disease onset—how does gene expression in tumor cells differ from normal cells?
  2. disease progression—as a tumor grows and spreads, how does expression change?
  3. background variation—how does gene expression change between normal cells that perform a different function?
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski

The real surprise and insight is in difference that ultimately advance our thinking (Data visualization: amgibuity as a fellow traveller. (2013) Nat. Methods 10:613-615).

Figuring out which differences are of this kind requires that instead of "What's new?" we ask "What's different?"


news + thoughts

Tree of Emotional Life

Sun 17-02-2019

One moment you're :) and the next you're :-.

Make sense of it all with my Tree of Emotional life—a hierarchical account of how we feel.

Martin Krzywinski @MKrzywinski
A section of the Tree of Emotional Life.

Find and snap to colors in an image

Sat 29-12-2018

One of my color tools, the colorsnap application snaps colors in an image to a set of reference colors and reports their proportion.

Below is Times Square rendered using the colors of the MTA subway lines.

Colors used by the New York MTA subway lines.

Martin Krzywinski @MKrzywinski
Times Square in New York City.
Martin Krzywinski @MKrzywinski
Times Square in New York City rendered using colors of the MTA subway lines.
Martin Krzywinski @MKrzywinski
Granger rainbow snapped to subway lines colors from four cities. (zoom)

Take your medicine ... now

Wed 19-12-2018

Drugs could be more effective if taken when the genetic proteins they target are most active.

Design tip: rediscover CMYK primaries.

More of my American Scientific Graphic Science designs

Ruben et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine Science Translational Medicine 10 Issue 458, eaat8806.

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
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
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
Circos cake celebrating Reuscher et al. 2018 publication of the Oryza longistaminata genome.