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A phylogenetic tree of 235 million sequences in full bloom.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Cover image accompanying our article on mouse vasculature development. Biology turns astrophysical. PNAS 1 May 2012; 109 (18) (PNAS)

Browse my gallery of cover designs.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)

Creating the PNAS Cover

One of my goals in life, which I can now say has been accomplished, is to make biology look like astrophysics. Call it my love for the Torino Impact Hazard Scale.

I was given an opportunity to achieve this goal when Linda Chang from Aly Karsan's group approached me with some microscopy photos of mouse veins. I was asked to do "something" with these images for a cover submission to accompany the manuscript.

When people see my covers, sometimes they ask "How did you do that?" Ok, actually they never ask this. But being a scientist, I'm trained me to produce answers in anticipation of such questions. So, below, I show you how the image was constructed.

The image was published on the cover of PNAS (PNAS 1 May 2012; 109 (18))

1 · Tools

Photoshop CS5, Nik Color Efex Pro 4, Alien Skin Bokeh 2 and a cup of coffee from a Rancilio Silvia.

2 · Source images

Below are a few of the images I had the option to work with. These are mouse embryonic blood vessels, with a carotid artery shown in the foreground with endothelial cells in green, vascular smooth muscle cells in red and the nuclei in blue.

Of course, as soon as I saw the images, I realized that there was very little that I needed to do to trigger the viewer's imagination. These photos were great!

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Mouse carotid arteries .

3 · Memories of Star Trek

Immediately I thought of two episodes of Star Trek (original series): Doomsday Machine and the Immunity Syndrome, as well as of images from the Hubble Telescope.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Enterprise is about to be consumed by a horror tube: a planet killer! (The Doomsday Machine)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Enterprise heads into a giant amoeba. Who eats whom? I'll let you guess. (The Immunity Syndrome)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Orion nebula (M42) as seen by the Hubble telescope. (zoom)

I though it would be pretty easy to make the artery images look all-outer-spacey. They already looked it.

4 · Choosing the centerpiece

And then I saw the image below.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A particularly spectacular image of a mouse carotid artery. I'm thinking 10 on the Torino scale.

5 · Designing the cover

5.1 · Background

The background was created from the two images shown here. The second image was sampled three times, at different rotations.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Images used for background.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Images used for background.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Layer composition for background elements.

The channel mixer was used to remove the green channel and leave only red and blue.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Background elements for PNAS cover image.

5.2 · Middleground

The next layer was composed of what looked like ribbons of blue gas. This was created by sampling the oval shapes from the source images. Here the red channel was a great source for cloud shapes, and this was the only channel that was kept. The hue was shifted to blue and a curve adjustment was applied to increase the contrast.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
First set of middle ground elements, before adjustments.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
First set of middle ground elements, after channel adjustments.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Second set of middle ground elements.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Layer composition for middle ground elements.

When the foreground and middle ground elements were combined, the result was already 40 parsecs away.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Background and foreground elements for PNAS cover image.

5.3 · Foreground

The foreground was created from the spectacular comet-like image of a mouse artery. Very little had to be done to make this element look good. It already looked good.

I applied a little blur using Alien Skin's Bokeh 2 to narrow the apparent depth of field, masked out elements at the bottom of the image and removed some of the green channel. The entire blue channel was removed altogether (this gave the tail of the comet a mottled, flame-like appearance).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Foreground element, before adjustments.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Foreground element, after channel adjustments. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Layer composition for foreground element.

6 · Post-processing

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Initial composition of background, middle ground and foreground elements.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
40% localized application of Nik's Tonal Contrast (Color Efex 4 plugin) to increase structure in red channel.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
50% blend with Nik's Pro Contrast (Color Efex 4 plugin).

And here we have the final image.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Final PNAS cover. Spacey!
news + thoughts

How Analyzing Cosmic Nothing Might Explain Everything

Thu 18-01-2024

Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.

My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
How Analyzing Cosmic Nothing Might Explain Everything. Text by Michael Lemonick (editor), art direction by Jen Christiansen (Senior Graphics Editor), source: SDSS

The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.

Michael Lemonick (editor) explains on the graphic:

“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.

To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.

Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reflects SDSS’s sky coverage.”

voids

Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function Astrophysical Journal 953:46.

Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics 2020:023.

Sloan Digital Sky Survey Data Release 12

constellation figures

Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)

stars

Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).

cosmology

H0 = 67.4 km/(Mpc·s), Ωm = 0.315, Ωv = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).

Error in predictor variables

Tue 02-01-2024

It is the mark of an educated mind to rest satisfied with the degree of precision that the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle

In regression, the predictors are (typically) assumed to have known values that are measured without error.

Practically, however, predictors are often measured with error. This has a profound (but predictable) effect on the estimates of relationships among variables – the so-called “error in variables” problem.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Error in predictor variables. (read)

Error in measuring the predictors is often ignored. In this column, we discuss when ignoring this error is harmless and when it can lead to large bias that can leads us to miss important effects.

Altman, N. & Krzywinski, M. (2024) Points of significance: Error in predictor variables. Nat. Methods 20.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nat. Methods 12:999–1000.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nat. Methods 13:541–542 (2016).

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nat. Methods 16:451–452.

Convolutional neural networks

Tue 02-01-2024

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).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Convolutional neural networks. (read)

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 20:1269–1270.

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

Tue 10-01-2023

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Neural network primer. (read)

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

Mon 16-01-2023

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Our Cell Genomics cover depicts parent-of-origin assignment as a circuit (volume 3, issue 1, 11 January 2023). (more)

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)
Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA
Google whack “vicissitudinal corporealization”
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