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
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 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.
The source of inspiration for the cover design came from previous work, where I drew trees from 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.
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.
Browse my gallery of cover designs.
Neural network primer
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.
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.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Cell Genomics cover
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.
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.
Science Advances cover
My cover design on the 6 January 2023 Science Advances issue depicts DNA sequencing read translation in high-dimensional space. The image showss 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes.
More details about the design.
Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.
Browse my gallery of cover designs.
Regression modeling of time-to-event data with censoring
If you sit on the sofa for your entire life, you’re running a higher risk of getting heart disease and cancer. —Alex Honnold, American rock climber
In a follow-up to our Survival analysis — time-to-event data and censoring article, we look at how regression can be used to account for additional risk factors in survival analysis.
We explore accelerated failure time regression (AFTR) and the Cox Proportional Hazards model (Cox PH).
Dey, T., Lipsitz, S.R., Cooper, Z., Trinh, Q., Krzywinski, M & Altman, N. (2022) Points of significance: Regression modeling of time-to-event data with censoring. Nature Methods 19.
Music video for Max Cooper's Ascent
My 5-dimensional animation sets the visual stage for Max Cooper's Ascent from the album Unspoken Words. I have previously collaborated with Max on telling a story about infinity for his Yearning for the Infinite album.
I provide a walkthrough the video, describe the animation system I created to generate the frames, and show you all the keyframes
The video recently premiered on YouTube.
Renders of the full scene are available as NFTs.