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Thoughts rearrange, familiar now strange.Holly Golightly & The Greenhornes break flowers

epigenetics: beautiful

Circos at British Library Beautiful Science exhibit—Feb 20–May 26

visualization + design

Creating the Genome Research November 2012 Cover

Martin Krzywinski @MKrzywinski
Cover image accompanying Spark: A navigational paradigm for genomic data exploration. Genome Research 22 (11). (zoom, Genome Research)

The Genome Research cover design takes a fun and illustrative approach to visualization. It's both art and science — in a 4:1 ratio.

The cover image accompanies the article by Cydney Nielsen from our visualization group, describing her Spark tool for visualization epigenetics data.

Nielsen CB, Younesy H, O'Geen H, Xu X, Jackson AR, et al. (2012) Spark: A navigational paradigm for genomic data exploration. Genome Res 22: 2262-2269.

Instead of a literal depiction of output from Spark, the final design presents what appears to be necklaces of the kind of tiles that Spark uses for its visual presentation. I took a chance that Genome Research had a sense of humor. Luckily, they did and accepted the design for the cover.

Colored tiles are playfully suspended on vertical strings to illustrate how Spark, presented in this issue, uses clustering to group genomic regions (tiles) with similar data patterns (colored heatmaps) and facilitates genome-wide data exploration.Genome Research 22 (11)

The image was published on the November 2012 issue of cover of Genome Research.


Illustrator CS5, and a cup (or two) of Galileo coffee from a Rancilio Epoca.

Other Covers

I had two other covers published this year: the PNAS cover accompanied our manuscript about mouse vasculature development and the Trends in Genetics cover was commissioned.

Martin Krzywinski @MKrzywinski
Cover image accompanying our article on mouse vasculature development. Biology turns astrophysical. PNAS 1 May 2012; 109 (18) (zoom, how it was made, PNAS)
Martin Krzywinski @MKrzywinski
Cover image for the human genetics special issue. Trends in Genetics October 2012, 28 (10) (lowres, hires, how it was made, Trends in Genetics)

source of design

Martin Krzywinski @MKrzywinski
To lower this computational barrier, particularly in the early data exploration phases, Spark was developed as an interactive pattern discovery and visualization tool for epigenomic data. (Spark)

Thinking about design ideas for the cover, I looked to the kind of visual motifs that Spark used for inspiration. Immediately the colorful tiles, which represent clustered data tracks, stood out.

Spark's output is very stylized, colorful and high contrast. It was important to preserve this aesthetic in the design. I also wanted to incorporate the idea of clustering in the design, as well as the concept that the clusters represented data from different parts of the genome.

While it was not important to illustrate how Spark organizes and analyzed data explicitly — in fact, I wanted these aspects to be subtle — it was important that the cover illustration had connections to Spark at several levels.


Martin Krzywinski @MKrzywinski
Many genomics techniques produce measurements that have both a value and a position on a reference genome, for example ChIP-sequencing.

Spark was created by Cydney Nielsen, who works with me at the Genome Sciences Center. It is designed to mitigate the difficulties arising from the fact that genome-wide data is typically scattered across thousands of points of interest.

Genome browsers integrate diverse data sets by plotting them as vertically stacked tracks across a common genomic x-axis. Genome browsers are designed for viewing local regions of interest (e.g. an individual gene) and are frequently used during the initial data inspection and exploration phases.

Most genome browsers support zooming along the genome coordinate. This type of overview is not always useful because it produces a summary across a continuous genomic range (e.g. chromosome 1) and not across the subset of regions that are of interest (e.g. genes on chromosome 1). Spark addresses this shortcoming and provides a way to help answer questions like: What are the common data patterns across genes start sites in my data set?

Martin Krzywinski @MKrzywinski
Spark's approach to analysis and display of epigenetic data.

Spark's visualization is driven by clustering data tracks (e.g. ChIP-seq coverage) from across equivalent regions (e.g. gene start sites). The clustered tracks are displayed as heatmaps, with each row being a data track and each column a windowed region of the genome.

early comps

With fond memories of Monte Carlo simulations from my physics days, I set out to simulate some realistic-looking, but entirely synthetic, Spark cluster tiles.

Martin Krzywinski @MKrzywinski
A collection of synthetic Spark tiles, each 7x20.

My first idea was a design which would show these tiles falling, perhaps accumulating on a pile on the ground. Quick prototypes of this idea were disappointing. The tiles appeared flimsy and too complex, while the image was largely empty. I spent several hours messing around with the rotation and pseudo-3D layout, but could not find anything that was satisfying.

Martin Krzywinski @MKrzywinski
Spark tiles, falling.
Martin Krzywinski @MKrzywinski
Early attempt at a design. Meh.

I thought to do this right would require a proper simulation within a 3D system.

refining the design

To address the fact that the tiles felt flimsy and overly complicated and the design lacked depth, I simplified the tile simulation to generate 5x5 tiles. These simpler representations still embodied how Spark displayed data, but did so minimally.

Martin Krzywinski @MKrzywinski
A second attempt at simulating Spark clusters.

To keep with the idea that the clusters come from different regions of the genome, I thought of arranging them along line segments. Unlike the design in which the tiles were falling, this constrained the layout significantly and allowed me to play with the design to make it look like the clusters were draped over it. By casting a light shadow behind each string of tiles, a subtle 3D effect could be achieved while still keeping the design within a plane.

There are 11 orientations of tiles created by rotating a thin square around the vertical axis with a slight forward tilt. There are 5 rotations to the left and right at angles 10, 26, 46, 66 and 80 degrees. The rotation was achieved using Illustrator's Extrude and Bevel 3D filter.

Martin Krzywinski @MKrzywinski
Layout of tiles.
Martin Krzywinski @MKrzywinski
Rotated tiles with Spark clusters.

Martin Krzywinski @MKrzywinski
Flight and Fall by Rachel Nottingham. (artist's site)

The layout and rotation of the tiles was inspired by Flight and Fall by Rachel Nottingham, a mobile of paper birds.

I wanted to keep the layout of the spark tiles pleasant, without being too organized. I find this to be a difficult balance to achieve — natural randomness is deceptively difficult to create by hand.

final image

Four different versions of the design were submitted to Genome Research. I was happiest with the treatment in which the tiles maintained their color and the Spark clusters were projected as tones of white. This designed felt more solid and punchy — I feel like you can reach out and touch one of those strings.

Martin Krzywinski @MKrzywinski
Final Spark cover designs. The top left one was chosen by Genome Research.

news + thoughts

Mind your p's and q's

Sat 29-03-2014

In the April Points of Significance Nature Methods column, we continue our and consider what happens when we run a large number of tests.

Martin Krzywinski @MKrzywinski
Nature Methods Points of Significance column: Comparing Samples — Part II — Multiple Testing. (read)

Observing statistically rare test outcomes is expected if we run enough tests. These are statistically, not biologically, significant. For example, if we run N tests, the smallest P value that we have a 50% chance of observing is 1–exp(–ln2/N). For N = 10k this P value is Pk=10kln2 (e.g. for 104=10,000 tests, P4=6.9×10–5).

We discuss common correction schemes such as Bonferroni, Holm, Benjamini & Hochberg and Storey's q and show how they impact the false positive rate (FPR), false discovery rate (FDR) and power of a batch of tests.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part II — Multiple Testing Nature Methods 11:215-216.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part I — t-tests Nature Methods 11:215-216.

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

Happy Pi Day— go to planet π

Fri 21-03-2014

Celebrate Pi Day (March 14th) with the art of folding numbers. This year I take the number up to the Feynman Point and apply a protein folding algorithm to render it as a path.

Martin Krzywinski @MKrzywinski
Digits of Pi form landmass and shoreline. (details)

For those of you who liked the minimalist and colorful digit grid, I've expanded on the concept to show stacked ring plots of frequency distributions.

Martin Krzywinski @MKrzywinski
Frequency distribution of digits of Pi in groups of 6 up to the Feynman Point. (details)

And if spirals are your thing...

Martin Krzywinski @MKrzywinski
Frequency distribution of digits of Pi in groups of 4 up to digit 4,988. (details)

Have data, will compare

Fri 07-03-2014

In the March Points of Significance Nature Methods column, we continue our discussion of t-tests from November (Significance, P values and t-tests).

We look at what happens how uncertainty of two variables combines and how this impacts the increased uncertainty when two samples are compared and highlight the differences between the two-sample and paired t-tests.

Martin Krzywinski @MKrzywinski
Nature Methods Points of Significance column: Comparing Samples — Part I. (read)

When performing any statistical test, it's important to understand and satisfy its requirements. The t-test is very robust with respect to some of its assumptions, but not others. We explore which.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part I Nature Methods 11:215-216.

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

Circos at British Library Beautiful Science Exhibit

Thu 06-03-2014

Beautiful Science explores how our understanding of ourselves and our planet has evolved alongside our ability to represent, graph and map the mass data of the time. The exhibit runs 20 February — 26 May 2014 and is free to the public. There is a good Nature blog writeup about it, a piece in The Guardian, and a great video that explains the the exhibit narrated by Johanna Kieniewicz, the curator.

Martin Krzywinski @MKrzywinski
Circos at the British Library Beautiful Science exhibit. (about exhibit)
Martin Krzywinski @MKrzywinski
Mailed invitation to the exhibit features my science art. (zoom)

I am privileged to contribute an information graphic to the exhibit in the Tree of Life section. The piece shows how sequence similarity varies across species as a function of evolutionary distance. The installation is a set of 6 30x30 cm backlit panels. They look terrific.

Martin Krzywinski @MKrzywinski
Circos Circles of Life installation at Beautiful Science exhibit at the British Library. (zoom)

Think outside the bar—box plots

Fri 31-01-2014

Quick, name three chart types. Line, bar and scatter come to mind. Perhaps you said pie too—tsk tsk. Nobody ever thinks of the box plot.

Box plots reveal details about data without overloading a figure with a full frequency distribution histogram. They're easy to compare and now easy to make with BoxPlotR (try it). In our fifth Points of Significance column, we take a break from the theory to explain this plot type and—I hope— convince you that they're worth thinking about.

Martin Krzywinski @MKrzywinski
Nature Methods Points of Significance column: Visualizing samples with box plots. (read)

The February issue of Nature Methods kicks the bar chart two more times: Dan Evanko's Kick the Bar Chart Habit editorial and a Points of View: Bar charts and box plots column by Mark Streit and Nils Gehlenborg.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Visualizing samples with box plots Nature Methods 11:119-120.