π Daylatest newsbuy art
listen; there's a hell of a good universe next door: let's go.e.e. cummingsgo theremore quotes
very clickable
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
City Trees

The trees along this city street,
Save for the traffic and the trains,
Would make a sound as thin and sweet
As trees in country lanes.

And people standing in their shade
Out of a shower, undoubtedly
Would hear such music as is made
Upon a country tree.

Oh, little leaves that are so dumb
Against the shrieking city air,
I watch you when the wind has come,—
I know what sound is there.

— Edna St. Vincent Millay

data visualization + art

Nature Biotechnology Cover

11 April 2022, Issue 40, Volume 4

Konno, N. et al. Deep distributed computing to reconstruct extremely large lineage trees (2022) Nature Biotechnology 40:566–575.

The 2021 π Day art celebrates the digits of π with a forest! Visit the bat cave and underwater ecosystems for the full experience.
Who doesn't want more than just one tree?

1 · The cover

The cover design accompanies the paper by Konno et al., which presents a highly efficient distributed computing method for the reconstruction of evolutionary trees from very large datasets.

The cover is a rearrangement of the very large phylogenetic data set depicted in Figure 2a in the paper. You can browse this data set using the HiView server.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Figure 2. Lineage reconstruction of over 235 million sequences. a. The whole distributed computing history of FRACTAL used to reconstruct the lineage of 235 million sequences generated by PRESUME. Each circle and its child circles in the circle packing diagram represent a parental FRACTAL iteration cycle and its child job cycles. d. A partial representation of the reconstructed lineage of 235,100,199 sequences. Each tree shows a partial lineage determined at each of the distributed computing cycles R2–R6 and B2–B6. The tree diagrams were visualized using Cytoscape 3.7.145. Interactive visualization for the whole distributed computing trajectories and lineage subgraphs reconstructed in corresponding FRACTAL cycles are available on the HiView server. Figure excerpt from Fig 2(a,d) in Konno et al.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
My rearrangement of the phylogenetic tree from Figure 2a in Konno et al.. The style is inspired by Sakura.

2 · Trees from data

The source of inspiration for the cover design came from previous work, where I drew trees from data.

2.1 · Plant data

I receive email. One day, I received this one.

I am a Norwegian biology student that has recently become a huge fan of your data art! It's beautiful, really!

What I wanted to ask you, if it's possible for you to help me a little bit on the way of a project I'm starting.

I have had the pleasure of working with a PhD-student here at the university in Bergen, she has taught me so much, and given me a lot of experience in the field, as well as opened up some big career "doors" for me.

She is supposed to deliver her PhD in November and I want to give here a gift to say thank you for all she has done for me.

My idea is this: some sort of visualization of the data she is using in here PhD. She is studying plant communities in Norway, and I have access to the data (plant heights, carbon in the air, thickness of leafs, number of individuals and so on), but i'm not sure how I can make it look beautiful.

So to clarify, I'm not looking for a diagram that is useful or anything, but just pretty to look at, and that is a memory of all the data she has collected, and worked with for the last 4 years. Maybe a diagram in different colors, depending on what the value is, in just a random order... or something...

So do you have any idea of how I can do this? What program do you use when you make your diagrams?

Understand if you dont have the time to answer this, but thank you anyway for reading, and for all you have created!

—Ruben Thormodsæter

I love Norway and I love people that love people who love science.

Since the dataset was a list of 376 individual plants, each annotated with species/genus and growth parameters such as height, mass and so on, and Ruben wanted something that is “not ... useful or anything” but rather “pretty to look at” based on “all the data she has collected and worked with for the last 4 years”.

I thought it would be both useful and pretty to represent the plant data by ... growing trees — in silico. One way to do this is to use an L-system.

So, here's the data

id,year,site,genus,species,height,mass,thick,plot,drought,plotmass,green
AA86T,2019,Lygra,Erica,tetralix,27,0.02115,0.166,1.3,0,,0.675
AA96C,2019,Lygra,Agrostis,capillaris,9,0.01477,0.107,3.3,90,186.39,0.7025
AA99C,2019,Lygra,Pedicularis,sylvatica,4,0.01806,0.064,2.3,50,80.92,0.695625
AB12C,2019,Lygra,Avenella,flexuosa,6.5,0.03173,0.154,3.3,90,186.39,0.7025
AB17C,2019,Lygra,Carex,pilulifera,10,0.04504,0.154,2.3,50,80.92,0.695625
AB30O,2019,Lygra,Vaccinium,myrtillus,9,0.02605,0.116,2.1,0,111.11,0.636875
...

and here's the final poster.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
376 individual plants across 8 plots.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The back of the poster shows the species for each plant and its unique identifier.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The legend explains the encoding, in which I represent height, thickness, mass, and plot drought level.

2.2 · Digits of `\pi`

I had such a great time with the L-systems that when Pi Day came around, I grew more trees. This time, using the digits of `\pi` to inform branching and sprouting.

So, for 2021 Pi Day, I tried to see the forest through the digits.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
768 digits of `\pi` depicted as a forest of trees grown with an L-system.

3 · Other covers

Browse my gallery of cover designs.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)
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”
{ 10.9.234.151 }