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I have a collection of unpublished Circos posters and thought these might be a good starting point.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.
Choose your own dust adventure!
Nobody likes dusting but everyone should find dust interesting.
Working with Jeannie Hunnicutt and with Jen Christiansen's art direction, I created this month's Scientific American Graphic Science visualization based on a recent paper The Ecology of microscopic life in household dust.
Barberan A et al. (2015) The ecology of microscopic life in household dust. Proc. R. Soc. B 282: 20151139.
A very large list of named colors generated from combining some of the many lists that already exist (X11, Crayola, Raveling, Resene, wikipedia, xkcd, etc).
For each color, coordinates in RGB, HSV, XYZ, Lab and LCH space are given along with the 5 nearest, as measured with ΔE, named neighbours.
I also provide a web service. Simply call this URL with an RGB string.
It is possible to predict the values of unsampled data by using linear regression on correlated sample data.
This month, we begin our column with a quote, shown here in its full context from Box's paper Science and Statistics.
In applying mathematics to subjects such as physics or statistics we make tentative assumptions about the real world which we know are false but which we believe may be useful nonetheless. The physicist knows that particles have mass and yet certain results, approximating what really happens, may be derived from the assumption that they do not. Equally, the statistician knows, for example, that in nature there never was a normal distribution, there never was a straight line, yet with normal and linear assumptions, known to be false, he can often derive results which match, to a useful approximation, those found in the real world.
—Box, G. J. Am. Stat. Assoc. 71, 791–799 (1976).
This column is our first in the series about regression. We show that regression and correlation are related concepts—they both quantify trends—and that the calculations for simple linear regression are essentially the same as for one-way ANOVA.
While correlation provides a measure of a specific kind of association between variables, regression allows us to fit correlated sample data to a model, which can be used to predict the values of unsampled data.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Simple Linear Regression Nature Methods 12:999-1000.
Altman, N. & Krzywinski, M. (2015) Points of significance: Association, correlation and causation Nature Methods 12:899-900.
Krzywinski, M. & Altman, N. (2014) Points of significance: Analysis of variance (ANOVA) and blocking. Nature Methods 11:699-700.
Correlation implies association, but not causation. Conversely, causation implies association, but not correlation.
This month, we distinguish between association, correlation and causation.
Association, also called dependence, is a very general relationship: one variable provides information about the other. Correlation, on the other hand, is a specific kind of association: an increasing or decreasing trend. Not all associations are correlations. Moreover, causality can be connected only to association.
We discuss how correlation can be quantified using correlation coefficients (Pearson, Spearman) and show how spurious corrlations can arise in random data as well as very large independent data sets. For example, per capita cheese consumption is correlated with the number of people who died by becoming tangled in bedsheets.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Association, correlation and causation Nature Methods 12:899-900.
For making probabilistic inferences, a graph is worth a thousand words.
This month we continue with the theme of Bayesian statistics and look at Bayesian networks, which combine network analysis with Bayesian statistics.
In a Bayesian network, nodes represent entities, such as genes, and the influence that one gene has over another is represented by a edge and probability table (or function). Bayes' Theorem is used to calculate the probability of a state for any entity.
In our previous columns about Bayesian statistics, we saw how new information (likelihood) can be incorporated into the probability model (prior) to update our belief of the state of the system (posterior). In the context of a Bayesian network, relationships called conditional dependencies can arise between nodes when information is added to the network. Using a small gene regulation network we show how these dependencies may connect nodes along different paths.
Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of Significance: Bayesian Statistics Nature Methods 12:277-278.
Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of Significance: Bayes' Theorem Nature Methods 12:277-278.