helena * jambor

scientist interested in RNA, genomics and science visualizations

Month: April, 2017

#BarBarPlots

Recently a kickstarter project raised more than 3000 EUR in one month to campaign for banning all wrong usage of bar plots in scientific journals. This demonstrates two important points: a lot of the plots in scientific journals are somewhat misleading, and a growing number of people feel very uneasy about this!

What exactly is wrong about bar plots? Nothing per se, but everything goes wrong if you use a bar plot for statistical data – this kind of plot species is also infamous as the “dynamite plot”. We are talking about the famous vertical or horizontal boxes that often come in a dazzling array of colors or patterns, with big fat black outlines and overly prominent error bars.

Dynamite_vs_DataPlot

Dynamite Plot                                                          Data Plot

 

Are they common? Very much so! My personal survey [i] of “dynamite plots” in scientific journals revealed that on average 30-60% of articles use them in journals covering a wide range of subjects that include physics, meteorology or psychology where authors typically have rigorous training in applied mathematics. The prevalence of dynamite plots increases as we go towards more life science journals, where 50- 70% of articles are accompanied by a dynamite plot showing a statistical summary [ii].

Most of us are completely accustomed to dynamite plots and happily use them, that is, until we see the light. From then on it is impossible to not hate them! Because it is so obvious they are misleading and make reading of the data just harder than necessary! And, as scientists, we aim for clarity and getting information across concisely!

The top reasons to avoid dynamite plots

  • They hide the real distribution of the data. Do all samples cluster closely? Do they form two groups? Or is there one drastic outlier? Generally, we assume a normal distribution of the data around the mean where there might not be one! In my survey of dynamite plots per journal they were more or less normally distributed.
  • They hide the sample size. From the bar plot you would not have known that I probed one issue of Nature, two issues of Cell and four issues of Development! But for judging scientific data knowledge of the sample size is essential for a proper evaluation of the data! Too often we have to search for the n in axis labeling, figure text, the results, or the methods section to finally find this information. And sometimes it is omitted entirely. A clear understanding of sample size in my opinion is also critical for the review process of a paper and should be demanded by the reviewers! Not showing data, or only showing summary data, should be treated equally to cropping Western blot bands!
  • Many different distributions of data can lead to the very Bar! See the Anscombe quartet. Bar plots are not intended to show statistic distributions, they are for absolute numbers. By plotting the real data we also learn more about the biology!

Not quite convinced? Seeing is believing, check out this figure:

tshirt_totebag

(c) Page Piccinini and the #barbarplots campain

For further information watch the video of the kickstarter campaign (British accent and humor alert!) – ideally with your entire lab and a discussion of this seminal paper on wrong usage of bar charts and this survey of their prevalence in biomedical journals!

Practical advice to avoid dynamite plots

  • Plot charts with statistical programing tool R. You have to either learn it, or be really nice to someone who knows it – if your PhD requires 3 boxplots, maybe invest in a friendly relationship with the bioinformatic geek in your department, a couple of coffees go a long way!
  • Learn how to make box plots in excel! (Here and here is how, but its a bit tedious).
  • Can’t be bothered to do either? Use one of the available web tools such as the boxplot maker from the Tyer’s lab or the plot generator from the University of Belgrade.

 

[i] I probed the top10-articles of Nature in July, the three most recent volumes of Science (August), four issues of Development (Vol 138, 1:3-2011 and Jan 2016), and two issues of Cell journal from 2016 (Jan and August). I was very relaxed and gave the benefit of doubt when I wasn’t sure. But I was rigorous when authors mixed right and wrong usage of bar plots. How does this even happen? Mix of co-authors and some know better than others?

[ii] Disclaimer: this does not mean the other articles have great figure design! I saw multiple uses of 3-dimensional pie charts, rainbow color schemes, other instances of unintentional usage of color, incomprehensible spider graphs and 3-dimensional heat maps! Maybe I will devote another blog post to those.

Color-blind people are your audience too!

This article is also on TheNode http://thenode.biologists.com/color-blind-audiences/photo/

Or, please stop mixing green/red

Color is a key aspect of graphic design, but for many years was not relevant for scientific figures that were largely black and white. Falling prices for color print and electronic publishing changed this dramatically and scientists now frequently produce multi-colored figures. Using color functionally is not always straightforward but few rules exist: do not combine red and green!

Already in 1939 Willard Brinton advised his readers to not use red letters on a green background as they become invisible to color-blind people (and are hideous for the rest of us!). [his great book on data visualization is available for free here]. A century later, when browsing through figures in scientific periodical, this message has not reached everyone.

In charts, it is very straightforward to avoid mixing red and green. If you want to use red, combine it with blue or cyan, if you want to use green, combine it with magenta or orange. That way also color blind people can distinguish the data points. A side note: try starting a chart in black and white, and only add color if absolutely essential.

In laser-microscopy green and red fluorophores are widely used, often in combination. But: Simply because a wavelength of your fluorophore is 488nm this does not mean you have to use green for its display! The camera output doesn’t have color anyway, so you are at liberty to choose a suitable lookup table. Why not be color-blind friendly and choose colors visible to your entire audience. Options that still preserve a little information on the wavelength are green/magenta or cyan/red. Again, consider if two black and white images instead of a composite color. In fact, the contrast is usually higher in greyscale which benefits the display of structure details and subtle intensity differences.

*Rm62 RNA in Drosophila egg chambers part of my postdoc project, find more subcellular RNAs on the Dresden Ovary Table.

Helpful tools:

  • Test color-blind visibility for your images here
  • Choose color for categorical, quantitative and diverging data in charts using color-brewer.

Comment suggesting more tools very welcome!