Don’t #2: Get greedy with the colorsĭon’t put too much color into your heatmap palette. Abrupt changes between different hues (green to yellow or blue to green) make the values seem significantly distant, while they are actually 0.1–0.2 units far from each other.Ī rainbow color scale is not a good idea for visualizing your data. The values change smoothly, but the colors do not. The rainbow scale also creates the misperception of the magnitude of the data. There’s no clear and consistent direction in such a scale. Another one associates that with orange, or blue. Some of your readers might see yellow as the highest value. One of the problems with the rainbow scale is that people automatically see the brightest color as a peak. No doubt that it’s more attractive than a single-hue color scale. The “rainbow” scale has had its presence on many scientific papers. There are a lot of options available, right? Don’t #1: Use the “rainbow” scale One more: color-blind people can detect the contrast and opacity, regardless of their impairment. Here are a couple of combos that work for a heatmap color scale: So try to avoid such combos and go for a color-blind-friendly heatmap palette. Do #2: Find a color-blind-friendly combinationįive percent of the entire population will thank you! Why limit people from seeing your meaningful visualizations? Color-blind people tend to struggle with the following combos, depending on their conditions: red-green, green-brown, green-blue, blue-gray, blue-purple, green-gray, green-black and light green-yellow. Got a standardized TPM matrix? Go for the diverging scale. A sequential color scale is ideal for showing raw TPM values (all of which are non-negative), while a diverging scale will effectively show standardized TPM values (including those of up-regulated and down-regulated genes).Ī sequential scale is good for showing raw TPM values. Let’s say you want to build a heatmap of gene expression. When a reference value is in the middle of the data range (such as zero or an average value), you should use a diverging color scale, with a neutral color representing the reference value. Just go for the sequential scale when you need to differentiate high values from low values. Scales that use multiple hues are also considered sequential when hues progress in a single direction from one end to another (like the Viridis scale).ĭiverging scales, on the other hand, show color progression in two directions: gradually toning down the first hue from one end to a neutral color at the midpoint, then increasing the opacity of the second hue to the other end of the scale. Sequential scales use the blended progression, typically of a single hue, from the least to the most opaque shades, representing low to high values (an example is the ColorBrewer Blues scale). When it comes to heatmaps, the two most common ranges of colors are sequential and diverging scales. Here’s a couple of dos and don’ts for a heatmap color scale. We really do not want to turn a heatmap into a literal “heat” map in this way, do we? Either it causes confusion or dizziness, or “heat” of annoyance to your readers. Yet, like anything else, the heatmap color scale can backfire when mistakenly chosen. It seems that the shading has breathed such life into those cheerless matrices of plain numbers, making it really easy to compare and spot the differences among groups of interest. Gotta examine the correlation among variables? Get it done with a heatmap. Wanna interpret gene expressions? Draw a heatmap. We see them all over the scientific journals - the shading matrices that convey meaningful stories.
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