Once the data are adequately scaled, it is important to choose a good color palette for the data. Other than being pretty, an ideal color palette should have three (somewhat conflicting) properties: (i) Colorful, spanning as wide a palette as possible so as to make differences easy to see; (ii) Perceptually uniform, so that values close to each other have similar-appearing colors compared with values that are far away, consistently across the range of values; and (iii) Robust to colorblindness, so that the above properties hold true for people with common forms of colorblindness, as well as printing well in grey scale. The default passed to the color argument in heatmaply is viridis, which offers a sequential color palette, offering a good balance of these properties. Divergent color scale should be preferred when visualizing a correlation matrix, as it is important to make the low and high ends of the range visually distinct. A helpful divergent palette available in the package is cool_warm (other alternatives in the package include RdBu, BrBG, or RdYlBu, based on the RColorBrewer package). It is also advisable to set the limits argument to range from -1 to 1.