Drawing Heatmaps in R: Customizing Color Scales and Legends

Heatmaps are a powerful visualization tool for displaying data density or distribution. In R, the heatmap function from the gplots package is commonly used to create heatmaps. However, one common question among users is how to customize the color scale and legend to better suit their needs.

In this article, we will delve into the world of heatmap customization in R, exploring how to restrict the number of colors used, obtain a custom legend, and understand the properties of the heatmap’s color scale.

Overview of Heatmaps

A heatmap is a graphical representation of data where values are represented by colors. The heatmap function in R creates a heatmap from a matrix or dataframe, where each cell represents a value in the dataset.

The default behavior of the heatmap function includes:

• Dendrogram: The rows and columns of the heatmap are rearranged based on similarity to each other.
• Automatic color scale: The color scale is automatically determined based on the range of values in the dataset.

Restricting Color Scales

One common requirement when working with heatmaps is to restrict the number of colors used. This can be achieved by specifying the col argument in the heatmap function, which takes a vector of colors or a color palette.

For example:

# Define a custom color scale using 10 colors
colors <- c("#FF0000", "#FFFF00", "#00FF00", "#0000FF", "#FF00FF",
             "#FFFF99", "#CCFF99", "#66FF66", "#33CC33", "#990099")

# Create a heatmap with the custom color scale
heatmap(cnv_n, col = colors)

In this example, we define a custom color scale using 10 colors and assign it to the col argument. The resulting heatmap will use these 10 colors instead of the default color scale.

Obtaining a Custom Legend

Another common requirement is to obtain a custom legend for the heatmap, which explains the meaning of each color used in the color scale.

In R, you can create a legend using the legend function. To customize the legend, you need to specify the col argument and the corresponding values that correspond to each color in the color scale.

For example:

# Create a custom legend for the heatmap
legend("topright", col = colors, title = "Color Legend",
       xlab = "", ylab = "")

In this example, we create a custom legend using the legend function and specify the col argument to match the color scale used in the heatmap. The resulting legend explains the meaning of each color used in the color scale.

Properties of Heatmap Color Scales

The default color scale used in heatmaps is determined based on the range of values in the dataset. This means that the colors used may not always accurately represent the data, especially if there are many extreme values or outliers.

To understand the properties of heatmap color scales, let’s examine how they work:

1. Automatic Color Scale: The heatmap function automatically determines the color scale based on the range of values in the dataset. This means that the colors used will be determined by the minimum and maximum values in the data.
2. Quantile Method: By default, the heatmap function uses the quantile method to determine the color scale. This involves dividing the data into equal-sized bins and assigning a color to each bin based on its position in the dataset.
3. Number of Colors: The number of colors used in the heatmap can be adjusted by specifying the col argument. More colors generally provide more detailed information, but may also make it harder to interpret the results.

Example Use Case: Heatmap with Multiple Columns

Suppose we want to create a heatmap that displays data from multiple columns. In this case, we can use the heatmap.2 function instead of heatmap, which allows us to specify multiple columns using the col argument:

# Create a heatmap with multiple columns
heatmap.2(cnv_n[, 1:3], col = colors)

In this example, we create a heatmap that displays data from columns 1-3 using the same custom color scale defined earlier.

Conclusion

Customizing heatmaps in R can be achieved by restricting the number of colors used, obtaining a custom legend, and understanding the properties of the heatmap’s color scale. By exploring these options, you can create heatmaps that accurately represent your data and provide meaningful insights for analysis or visualization.

Last modified on 2023-07-23