Creating a Facet Heatmap with ggplot2: A Step-by-Step Guide

Introduction

Heatmaps are an effective way to visualize data where the color represents the intensity or magnitude of a particular value. However, when dealing with large datasets that need to be displayed on multiple facets (e.g., different chromosomes), traditional heatmaps can become cluttered and difficult to interpret. In this article, we will explore how to create a facet heatmap using ggplot2, a popular data visualization library in R.

Problem Statement

The provided Stack Overflow question illustrates the challenge of creating a facet heatmap with ggplot2. The goal is to display a heatmap organized by chromosome, with sample along the x-axis and leftPos along the Y axis. However, traditional heatmaps are not suitable for this task, as they require two columns. To address this, we will use geom_tiles() to create a tile-based representation of the data.

Preparation

To begin, we need to prepare our dataset in a long format. This is necessary because ggplot2 requires data to be in a specific structure to work correctly with facets and heatmaps.

library(ggplot2)
library(reshape2)

# Create a sample dataset
df <- data.frame(
  chr = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
  leftPos = c(4324, 5353, 6632, 1443, 7644, 8886, 1287, 5443, 7668),
  Sample1 = c(434, 63, 543, 25, 74, 23, 643, 93, 33),
  AnotherSample = c(43, 34, 3544, 345, 26, 9, 45, 23, 45),
  EtcSample = c(33, 532, 23, 543, 324, 23, 23, 77, 33)
)

# Convert leftPos to a factor
df$leftPos <- factor(df$leftPos)

# Reshape the data in a long format
df.l <- reshape(df,
                varying = c("Sample1", "AnotherSample", "EtcSample"),
                idvar = "chr",
                v.names = "value",
                timevar = "sample",
                times = c("Sample1", "AnotherSample", "EtcSample"),
                new.row.names = c(1:(3*nrow(df))),
                direction = "long")

Reshaping the Data

The reshape() function is used to transform the data from a wide format to a long format. This is necessary because ggplot2 requires data to be in a specific structure to work correctly with facets and heatmaps.

varying: specifies which columns should be varied (i.e., displayed).
idvar: specifies the column that should remain constant (i.e., the identifier variable).
v.names: assigns a new name to the varied columns.
timevar: assigns a new name to the time-based column.
new.row.names: assigns new row names to the data frame.
direction: specifies whether to reshape in long or wide format.

Now that we have our data in a long format, we can create a facet heatmap using ggplot2.

# Create a facet heatmap
ggplot(df.l, aes(sample, leftPos)) + 
  geom_tile(aes(fill = value)) +
  scale_fill_gradient(low = "white", high = "red") +
  facet_wrap(~ chr) +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )

Explanation

The following code creates a facet heatmap:

aes(sample, leftPos): specifies the aesthetic mapping for the tiles. The sample variable is used on the x-axis, and the leftPos variable is used on the y-axis.
geom_tile(aes(fill = value)): creates the tile-based representation of the data. The fill aesthetic maps to the values in the data frame.
scale_fill_gradient(low = "white", high = "red"): scales the fill colors from white (low) to red (high).
facet_wrap(~ chr): adds a facet for each unique value of the chr variable. This allows us to display multiple heatmaps on the same plot.
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()): removes the grid lines from the facets.

Tips and Variations

Here are some additional tips and variations for creating facet heatmaps with ggplot2:

Customize the tile colors: You can customize the tile colors using scale_fill_gradient(low = "blue", high = "red") or any other color scale you prefer.
Add a legend: If you have multiple heatmaps on the same plot, you may want to add a legend to distinguish between them. Use ggplot() + geom_tile() and add legend = "bottom" or legend = "right".
Use different mapping variables: You can use different mapping variables for the tiles by adding additional aesthetics using aes(). For example, you could map the Sample1 variable to the x-axis.
Rotate the y-axis: If your data has a large range of values on the y-axis, consider rotating it to improve readability. Use coord_flip() to flip the plot.

Conclusion

In this article, we explored how to create a facet heatmap with ggplot2 using tile-based representation. We discussed the importance of reshaping the data in a long format and provided tips for customizing the appearance of the plot.

Last modified on 2023-07-26