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Here we are now at the middle of the fourth large part of this talk.Pepe Deluxeget nowheremore quotes


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


visualization + design

Creating the Genome Research November 2012 Cover

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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.

Tools

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 mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
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.

Spark

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
Spark tiles, falling.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
Layout of tiles.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Rotated tiles with Spark clusters.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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 mkweb.bcgsc.ca
Final Spark cover designs. The top left one was chosen by Genome Research.
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news + thoughts

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

Optimal experimental design

Tue 31-07-2018
Customize the experiment for the setting instead of adjusting the setting to fit a classical design.

The presence of constraints in experiments, such as sample size restrictions, awkward blocking or disallowed treatment combinations may make using classical designs very difficult or impossible.

Optimal design is a powerful, general purpose alternative for high quality, statistically grounded designs under nonstandard conditions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Optimal experimental design. (read)

We discuss two types of optimal designs (D-optimal and I-optimal) and show how it can be applied to a scenario with sample size and blocking constraints.

Smucker, B., Krzywinski, M. & Altman, N. (2018) Points of significance: Optimal experimental design Nature Methods 15:599–600.

Background reading

Krzywinski, M., Altman, N. (2014) Points of significance: Two factor designs. Nature Methods 11:1187–1188.

Krzywinski, M. & Altman, N. (2014) Points of significance: Analysis of variance (ANOVA) and blocking. Nature Methods 11:699–700.

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments. Nature Methods 11:597–598.

The Whole Earth Cataloguer

Mon 30-07-2018
All the living things.

An illustration of the Tree of Life, showing some of the key branches.

The tree is drawn as a DNA double helix, with bases colored to encode ribosomal RNA genes from various organisms on the tree.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The circle of life. (read, zoom)

All living things on earth descended from a single organism called LUCA (last universal common ancestor) and inherited LUCA’s genetic code for basic biological functions, such as translating DNA and creating proteins. Constant genetic mutations shuffled and altered this inheritance and added new genetic material—a process that created the diversity of life we see today. The “tree of life” organizes all organisms based on the extent of shuffling and alteration between them. The full tree has millions of branches and every living organism has its own place at one of the leaves in the tree. The simplified tree shown here depicts all three kingdoms of life: bacteria, archaebacteria and eukaryota. For some organisms a grey bar shows when they first appeared in the tree in millions of years (Ma). The double helix winding around the tree encodes highly conserved ribosomal RNA genes from various organisms.

Johnson, H.L. (2018) The Whole Earth Cataloguer, Sactown, Jun/Jul, p. 89