ggalign

This package extends ggplot2 by providing advanced tools for aligning and organizing multiple plots, particularly those that automatically reorder observations, such as dendrogram. It offers fine control over layout adjustment and plot annotations, enabling you to create complex, publication-quality visualizations while still using the familiar grammar of ggplot2.

Why use `ggalign`?

ggalign focuses on aligning observations across multiple plots. It leverages the "number of observations" in the vctrs package or NROW() function to maintain consistency in plot organization.

If you’ve ever struggled with aligning plots with self-contained ordering (like dendrogram), or applying consistent grouping or ordering across multiple plots (e.g., with k-means clustering), ggalign is designed to make this easier. The package integrates seamlessly with ggplot2, providing the flexibility to use its geoms, scales, and other components for complex visualizations.

Installation

You can install ggalign from CRAN using:

install.packages("ggalign")

Alternatively, install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("Yunuuuu/ggalign")

Getting Started

The usage of ggalign is simple if you’re familiar with ggplot2 syntax, the typical workflow includes:

Initialize the layout using ggheatmap() or ggstack().
Customize the layout with:
- align_group(): Group layout axis into panel with a group variable.
- align_kmeans(): Group layout axis into panel by kmeans.
- align_order(): Reorder layout observations based on statistical weights or by manually specifying the observation index.
- align_dendro(): Reorder or Group layout based on hierarchical clustering.
Adding plots with ggalign() or ggpanel(), and then layer additional ggplot2 elements such as geoms, stats, or scales.

Basic example

Below, we’ll walk through a basic example of using ggalign to create a heatmap with a dendrogram.

library(ggalign)
set.seed(123)
small_mat <- matrix(rnorm(81), nrow = 9)
rownames(small_mat) <- paste0("row", seq_len(nrow(small_mat)))
colnames(small_mat) <- paste0("column", seq_len(ncol(small_mat)))

# initialize the heatmap layout, we can regard it as a normal ggplot object
ggheatmap(small_mat) +
    # we can directly modify geoms, scales and other ggplot2 components
    scale_fill_viridis_c() +
    # add annotation in the top
    hmanno("top") +
    # in the top annotation, we add a dendrogram, and split observations into 3 groups
    align_dendro(aes(color = branch), k = 3) +
    # in the dendrogram we add a point geom
    geom_point(aes(color = branch, y = y)) +
    # change color mapping for the dendrogram
    scale_color_brewer(palette = "Dark2")

Compare with other ggplot2 heatmap extension

ggalign offers advantages over extensions like ggheatmap by providing full compatibility with ggplot2. With ggalign, you can:

Seamlessly integrate ggplot2 geoms, stats, scales et al. into your layouts.
Align dendrograms even in facetted plots.
Easily create complex layouts, including multiple heatmaps arranged vertically or horizontally.

Compare with ComplexHeatmap

Pros

Full integration with the ggplot2 ecosystem.
Heatmap annotation axes and legends are automatically generated.
Dendrogram can be easily customized and colored.
Flexible control over plot size and spacing.
Can easily align with other ggplot2 plots by panel area.
Can easily extend for other clustering algorithm, or annotation plot.

Cons

Fewer Built-In Annotations: May require additional coding for specific annotations or customization compared to the extensive built-in annotation function in ComplexHeatmap.

More Complex Examples

Here are some more advanced visualizations using ggalign: