Lately, I've been making a lot of square things round. So when Rhiannon Macrae, the Editor of Trends in Genetics, requested a Circos-like cover image for the human genetics special edition of the journal, I started drawing circles.
Circos has appeared on covers of journals and books. Some of the images were designed by me and others were drawn from papers published in the issue.
I have a collection of unpublished Circos posters and thought these might be a good starting point. Rhiannon and I narrowed the choice down to the black-and-white design that showed sequenced organisms. We also liked the complex style of a panel of hundreds of Circos images generated with the tableviewer.
The idea would be that the foreground would be more artistic and stylized, while the background was more technical and complex. I have thousands of images available from the tableviewer (e.g. huge 15,129 image matrix).
Rhiannon also wanted to include the quote by Henry David Thoreau, "Nature and human life are as various as our several constitutions. Who shall say what prospect life offers to another?" This reminded me of a similar but more tragic line from Shakespeare's Julius Caesar, "How many ages hence shall this our lofty scene be acted over in states unborn and accents yet unknown!"
In the early comps we played around with the idea of using non-genomics elements in the image, such as coins. We thought that we could use the variety of color and shape of the coins to communicate the idea of genetic diversity. However, after wrestling with how to do this effectively the concept was scrapped — the idea of using coins felt both arcane and arbitrary.
I decided to go with a warm brown color scheme. It's not a color I use a lot of, which makes me think that I should try to do more with it.
Deep brown provides great contrast for saturated colors, though I had to be careful not to make the image look too kitchy with an excess of colour variation. In some of the early comps shown above, two or more different color palettes were used (e.g. grey/red/blue and false color) and this lowered to overall visual cohesion of the image.
It's always a good idea to add variety to design. After all, without any variety we'd be left with a blank page. Ok, so variety is good, but too much variety is very bad, and can make you wish for that blank page again. Think about this: one kind of variety already provides variety! A variety of variety (I run the risk of recursing myself ad infinitum) can not only compete for attention but resonate destructively (that's design-speak for "turn into visual mush").
Everyone liked the combination of bright colors and dark background. This is an approach I favour too, which has worked well on other covers.
Briefly I experimented with various brush and pencil filters to give the image a more hand-drawn and organic look. Most of the illustrations I generate are very digital — blocks of solid colors and high-contrast shapes — and I thought a departure from this look could work in this case. However, like with the coins, this path didn't produce anything productive.
Outliers can degrade the fit of linear regression models when the estimation is performed using the ordinary least squares. The impact of outliers can be mitigated with methods that provide robust inference and greater reliability in the presence of anomalous values.
We discuss MM-estimation and show how it can be used to keep your fitting sane and reliable.
Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. Nature Methods 16:275–276.
Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.
Two-level factorial experiments, in which all combinations of multiple factor levels are used, efficiently estimate factor effects and detect interactions—desirable statistical qualities that can provide deep insight into a system.
They offer two benefits over the widely used one-factor-at-a-time (OFAT) experiments: efficiency and ability to detect interactions.
Since the number of factor combinations can quickly increase, one approach is to model only some of the factorial effects using empirically-validated assumptions of effect sparsity and effect hierarchy. Effect sparsity tells us that in factorial experiments most of the factorial terms are likely to be unimportant. Effect hierarchy tells us that low-order terms (e.g. main effects) tend to be larger than higher-order terms (e.g. two-factor or three-factor interactions).
Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments Nature Methods 16:211–212.
Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments.. Nature Methods 11:597–598.
Celebrate `\pi` Day (March 14th) and set out on an exploration explore accents unknown (to you)!
This year is purely typographical, with something for everyone. Hundreds of digits and hundreds of languages.
A special kids' edition merges math with color and fat fonts.
One moment you're
:) and the next you're
Make sense of it all with my Tree of Emotional life—a hierarchical account of how we feel.