Working with senior graphics editor at Scientific American Jen Christiansen, I have designed three Graphic Science visualizations for the magazine.
The dataset is challenging: expression, correlation and network module membership of 11,000+ genes. Getting it onto one page was an exercise in restraint and calm.
Graphic by Martin Krzywinski.
Source: Gandal M.J. et al. Shared Molecular Neuropathology Across Major Psychiatric Disorders Parallels Polygenic Overlap Science 359 693–697 (2018)
This collaboration with Jeanine Hunnicutt explored differences in household dust bacteria based on the gender and pet status of the occupants.
We have also written about the making of the graphic, for those interested in how these things come together.
Graphic by Martin Krzywinski and Barbara Jeanine Hunnicutt.
Catalogue of bacteria shapes by Barbara Jeanine Hunnicutt.
Source: Barberan A et al. (2015) The ecology of microscopic life in household dust. Proc. R. Soc. B 282: 20151139.
Because sometimes only a network hairball will do.
Graphic by Martin Krzywinski.
Source: Integrative analysis of 111 reference human epigenomes. (2015) Nature 518:317.
A Scientific American blog entry "A Monkey's Blueprint" accompanies this piece. I also have a more detailed description with links to data sources.
This design won a bronze award at Malofiej 23. For more information about Malofiej, see the SA Visual blog entry "There's No Infographic without Info (and other Lessons from Malofiej)".
Graphic by Martin Krzywinski, illustrations by Portia Sloan Rollings.
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.
Krzywinski, M. & Altman, N. (2013) Points of significance: Importance of being uncertain. Nature Methods 10:809–810.
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.
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.
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.
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.