design + visualization

VIZBI 2013 Keynote

Visual Design Principles—Communicating Effectively

This talk happened on Thursday, Mar 21st 2013 at VIZBI 2013 at the Broad Institute in Boston.

How often people speak of art and science as though they were two entirely different things, with no interconnection. An artist is emotional, they think, and uses only his intuition; he sees all at once and has no need of reason. A scientist is cold, they think, and uses only his reason; he argues carefully step by step, and needs no imagination. That is all wrong. The true artist is quite rational as well as imaginative and knows what he is doing; if he does not, his art suffers. The true scientist is quite imaginative as well as rational, and sometimes leaps to solutions where reason can follow only slowly; if he does not, his science suffers. —Isaac Asimov (The Roving Mind)

For more visualization and design resources, see my VIZBI 2012 tutorials, Nature Methods Points of View column, and rant about colors.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca

▲ Do not allow encoding or other design choices to hijaack your message. Each element on the page must meaningfully contribute to your figure.

presentation video

The video will be posted at vizbi.org.

presentation slides

Slides are available as PDF and keynote (zipped).

Martin Krzywinski - VIZBI 2013 Keynote - Visual Design Principles

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A poet is, after all, a sort of scientist, but engaged in a qualitative science in which nothing is measurable. He lives with data that cannot be numbered, and his experiments can be done only once. The information in a poem is, by definition, not reproducible. He becomes an equivalent of scientist, in the act of examining and sorting the things popping in [to his head], finding the marks of remote similarity, points of distant relationship, tiny irregularities that indicate that this one is really the same as that one over there only more important. Gauging the fit, he can meticulously place pieces of the universe together, in geometric configurations that are as beautiful and balanced as crystals. —Lewis Thomas (The Medusa and the Snail: More Notes of a Biology Watcher)

breakout session—making good figures

▲ Sketch notes by the inimitable Francis Rowland from our breakout group. The question was: what do you need to make good figures? (PDF)

small, medium and big data visualization

If you're asking how to visualize big data, first make sure you're doing a good job on small and medium data. Each scale requires good design.

Do not expect to use one way
to tell many stories

Also consider that there is a very large number of combinations of data sets, hypotheses and possible patterns. Because of this, you cannot expect to use one way to tell many stories. There is no Holy Grail of big data visualization. But there are many good questions to ask and practices to follow that make up a process which can help you get there.

▲ Small data visualization. Let's get this right first. (A) Charlebois, R.L. et al Genome Res 14:2469 (2004) (Computing prokaryotic gene ubiquity: rescuing the core from extinction.) (B) Nikaido M et al PNAS 96:10261 1999 (Phylogenetic relationships among cetartiodactyls based on insertions of short and long interspersed elements: Hippopotamuses are the closest extant relatives of whales (C) Harr, B. Genome Res 16:730 (2006) (Genomic islands of differentiation between house mouse subspecies

▲ Medium data visualization. This is what happens when you show the data (a strategy that works for small data), instead of explaining it. Yup, we need to work on this too. (A) Qi X et al. J Biotech 144:43 (2012) (Saturation-Mutagenesis in Two Positions Distant from Active Site of a Klebsiella pneumoniae Glycerol Dehydratase Identifies Some Highly Active Mutants) (B) Alekseyev, M.A. et al. Genome Res 19:943 (2009) (Breakpoint graphs and ancestral genome reconstructions)

▲ Big data visualization. Yes, data sets are growing but are visual and cognitive abilities are not. There are many data sets, each requiring its own approach. You cannot use one way to tell many stories. Lewis SN et al. PLoS ONE 6:e27175 (2011) (Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies)

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news + thoughts

Consequences of missing data

Fri 04-09-2026

Either I've been missing something or nothing has been going on. —Karen Elizabeth Gordon

Missing data are everywhere. Subjects may decline to participate in a survey or fail to answer sensitive questions. A cell culture might fail due to contamination. Instrument failures or mishandling of a sample may lead to missing observations. But why are missing data a problem?

▲ Nature Methods Points of Significance column: Consequences of missing data. (read)

This month, we begin a series of articles about practical and statistical aspects of missing data. We'll see that missing data can increase variability and introduce bias and we will ask whether anything can be done to mitigate these consequences. It turns out that, in the case where we know nothing about the missing subjects, no mitigation is possible. We must accept higher variability and, if we have a suspicion that the missing subjects aren’t completely random, possible bias as well.

Tanujit Dey, T., Lipsitz, S.R., Fitzmaurice, G., Krzywinski, M. & Altman, N. (2026) Points of significance: Consequences of missing data. Nat. Methods 23:1661–1663.

Double Robustness

Mon 04-05-2026

It is not certain that everything is uncertain. —Blaise Pascal

We have already explored how we can mitigate bias caused by confounding variables in observational studies using propensity score (PS) matching (PSM) and propensity score weighting (PSW). However, any statistical model is only as good as its assumptions and, if it is specified incorrectly, it can itself produce biased estimates of the treatment effect.

▲ Nature Methods Points of Significance column: Double Robustness. (read)

This month, we explore double robustness, a powerful statistical concept that provides a valuable “safety net” against the risk of an incorrect model. It offers two opportunities, instead of just one, to obtain a valid estimate of the treatment effect — making it possible to draw credible causal inferences from observational data without having to depend on a single set of modeling assumptions.

Kurz, C.F., Krzywinski, M. & Altman, N. (2026) Points of significance: Double Robustness. Nat. Methods 23:868–869.

Nature Biotechnology cover

Thu 23-04-2026

My cover design on the 7 April 2026 Nature Biotechnology issue shows the dendrogram that represents a cluster of uniquely expressed (or downregulated) genes in human naive stem cells induced from such cells. Within each dendrogram block, the genomic barcode sequence (sampled from Supplementary Table 1) is depicted with a Code 39 barcode. The highlighted barcode is one of those used for cell isolation.

Ishiguro S. et al. A multi-kingdom genetic barcoding system for precise clone isolation (2026) Nature Biotechnology 44:616–629.

▲ My Nature Biotechnology phylogenetic tree cover (volume 44, issue 4, 7 April 2026). (more)

Browse my gallery of cover designs.

▲ A catalogue of my journal and magazine cover designs. (more)

Happy 2026 π Day—
Art for the 5%

Fri 13-03-2026

Celebrate π Day (March 14th) and enjoy the art — but only if you're part of the 5%.

Go ahead, see what you can't see.

▲ 2026 π DAY | Art for the 5%. Shown in the style of Ishihara color test plates, the art is visible only to those with colour blindness. (details)