By all means: avoid 3D!
You have so much nice data you want to show, but sadly only one flat piece of paper. Are 3-dimensional graphs a good solution? Quick answer: No, never, ever. Why, I will explain and show a recent example that I worked on.
We often have the trouble of wanting (or having) to show a lot data at once: let’s say the body temperature of mice over time, RNA expression in cell differentiation. If the data points diverge (and are color-coded!) this rapidly results in a highly cluttered graph. As a consequence the audience has to really “read” the data to decide for themselve what the main message is. We also have a problem if the data is similar and partially overlaps. Again, the resulting graph is highly unreadable.
What to do? To avoid such overlap in data points we tend to use 3-dimensional graphs: each data series can then be read individually. However, a 3-dimensional plot create more problems than it solves:
- A reduction that is shown further along the z-axis (green data!) is visually heightened – and consequently cannot be fully appreciated. Vice-versa, if you wanted to show an increase, it would look much more dramatic if shown in the background – both are: misleading!
- It is almost impossible to faithfully read the value of the y-axis correctly. What is the size of the first green peak? I’d have to use a ruler to asess where the peak would cross the y-axis (3rd tick) and then substract the height of where the green baseline crosses the y-axis (0.5 ticks). Quite a lot of work!
Show data individually, dare to show it small, the main point will still be clear! And make use of the power of showing multiples – here the reader has to read axes only once, but can apply this knowledge to all of the individual plots at once!
Note: the resulting picture is not bigger than the orignial and could possibly be further reduced in size while still being fully readable!
PS. To increased clarity I mute the colors of the y-axis and gene-model and show them in grey (there is no need to show each exon in a different color!). I then use color ONLY to highlight the main message: a strong reduction of RNA expression in homozygous mutants. By separating the data into three plots I circumvent the problem of having to show them in individual colors.