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visualizations
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lunch break
Distractions and amusements, with a sandwich and coffee.
data visualization + art
Genes that make us sick
It is said that for money you can have everything, but you cannot. You can buy food, but not appetite; medicine, but not health; knowledge, but not wisdom; glitter, but not beauty; fun, but not joy; acquaintances, but not friends; servants, but not faithfulness; leisure, but not peace. You can have the husk of everything for money, but not the kernel.
— Arne Garborg
I have recently had the opportunity to contribute to The Objects that Power the Global Economy, a book by Quartz.
The book is about objects that have impact on our world and our lives. "Each chapter of this book examines an object that is driving radical change in the global economy: how we communicate, what we eat, the way we spend our money. The stories are told through global reporting, original photography and illustration by award-winning artists, contributions from business visionaries, data visualization, and interactive features." (Quartz).
the posters
The human genome is shown as a spiral, starting at the top with chromosome 1 and proceeding clockwise. The spiral is formed by 10,087 segments that correspond to 286,000 bases each. Segments that contain genes implicated in disease are indicated by dots, sized by the number of genes. Chromosomes X and Y are not shown.
where disease hides in the genome
My illustration is of the human genome with a focus on the genes that have been implicated in disease.
We have about 30,000 genes and about half of these play some role in disease.
You can peruse what we know about the connection between genetics and illness at the Online Mendelean Inheritance of Man database. For example, a cursory search for "cancer" results in over 3,500 entries.
It's important to realize that these aren't genes that cause disease—its misregulation and mutations in them that are associated with disease (causality is complicated).
the visualization
The illustration shows the genome as a single line, wound in an Archimedean spiral. Chromosomes 1–22 are shown binned into about 10,000 regions along the spiral. Regions that have genes associated with disease are marked with dots—the size of the dot shows how many such genes are found. Each region corresponds to about 286,000 bases.
In about 73% of the 286 kb regions, there are no genes. In about 18%, there is a single gene and in roughly 11% two genes or more.
regions genes
7,321 0
1,812 1
556 2
221 3
85 4
93 5+
Winding the genome up in a spiral creates a compact representation. Squishing a line onto a page can be tricky.
Luckily, space filling curves like the Hilbert curve are very efficient at doing this. I've previously shown the genome along a Hilbert curve for a Scientific American Graphic Science page.
the artwork
In the book, the image is printed on a black background.
news + thoughts
Find and snap to colors in an image
One of my color tools, the colorsnap application snaps colors in an image to a set of reference colors and reports their proportion.
Below is Times Square rendered using the colors of the MTA subway lines.
Colors used by the New York MTA subway lines.
Take your medicine ... now
Drugs could be more effective if taken when the genetic proteins they target are most active.
Design tip: rediscover CMYK primaries.
More of my American Scientific Graphic Science designs
Ruben et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine Science Translational Medicine 10 Issue 458, eaat8806.
Predicting with confidence and tolerance
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%).
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
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.
Oryza longistaminata genome cake
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.
Optimal experimental design
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.
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.