Layout Customization • ggalign

The package offers a suite of align_* functions designed to give you precise control over plot layout. These functions enable you to manipulate axis order within the layout and partition along an axis into multiple panels.

Currently, there are four key align_* functions available for layout customization:

align_group: Group and align plots based on categorical factors.
align_reorder: Reorder plots or split axes into different panels.
align_kmeans: Arrange plots by k-means clustering results.
align_dendro: Align plots according to hierarchical clustering or dendrograms.

library(ggalign)
#> Loading required package: ggplot2

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)))

`align_group`

The align_group() function allows you to group rows/columns into separate panels. It doesn’t add any plot area.

ggheatmap(small_mat) +
    hmanno("t") +
    align_group(sample(letters[1:4], ncol(small_mat), replace = TRUE))

By default, the facet strip text is removed. You can override this behavior with theme(strip.text = element_text()). Since align_group() does not create a new plot, the panel title can only be added to the heatmap plot.

ggheatmap(small_mat) +
    theme(strip.text = element_text()) +
    hmanno("l") +
    align_group(sample(letters[1:4], nrow(small_mat), replace = TRUE))

`align_reorder`

The align_reorder() function reorders the rows/columns based on a summary function. Like align_group(), it doesn’t add a plot area.

Here, we reorder the rows based on the means.

ggheatmap(small_mat) +
    hmanno("l") +
    align_reorder(rowMeans)

By default, align_reorder() reorders the rows or columns in ascending order of the summary function’s output (from bottom to top for rows, or from left to right for columns). To reverse this order, you can set decreasing = TRUE:

ggheatmap(small_mat) +
    hmanno("l") +
    align_reorder(rowMeans, decreasing = TRUE)

Some align_* functions accept a data argument. This can be a matrix, a data frame, or even a simple vector, which will be converted into a one-column matrix. If the data argument is NULL, the function will use the layout data, as demonstrated in the previous example. The data argument can also accept a function (purrr-like lambda syntax is supported), which will be applied to the layout data.

Note: All align_* functions treat rows as observations, meaning that NROW() function must return the same number as the parallel layout axis. For heatmap column annotations, the heatmap matrix is transposed before being used. If data is a function, it will be applied to the transposed matrix.

Even for top and bottom annotations, you can use rowMeans() to calculate the mean value across all columns.

ggheatmap(small_mat) +
    hmanno("t") +
    align_reorder(rowMeans)

`align_kmeans`

The align_kmeans() function groups heatmap rows or columns based on k-means clustering. Like the previous functions, it does not add a plot area.

ggheatmap(small_mat) +
    hmanno("t") +
    align_kmeans(3L)

It’s important to note that align_group() and align_kmeans() cannot do sub-grouping. This means they cannot be used if groups already exist.

ggheatmap(small_mat) +
    hmanno("t") +
    align_group(sample(letters[1:4], ncol(small_mat), replace = TRUE)) +
    align_kmeans(3L)
#> Error in `align_kmeans()`:
#> ! `align_kmeans()` cannot do sub-split
#> ℹ Group of layout x-axis already exists

ggheatmap(small_mat) +
    hmanno("t") +
    align_kmeans(3L) +
    align_group(sample(letters[1:4], ncol(small_mat), replace = TRUE))
#> Error in `align_group()`:
#> ! `align_group()` cannot do sub-split
#> ℹ Group of layout x-axis already exists

align_dendro

The align_dendro() function adds a dendrogram to the layout and can also reorder or split the layout based on hierarchical clustering. This is particularly useful for working with heatmap plots.

ggheatmap(small_mat) +
    hmanno("t") +
    align_dendro()

Hierarchical clustering is performed in two steps: calculate the distance matrix and apply clustering. You can use the distance and method argument to control the dendrogram builind process.

There are two ways to specify distance metric for clustering:

specify distance as a pre-defined option. The valid values are the supported methods in dist() function and coorelation coefficient "pearson", "spearman" and "kendall". The correlation distance is defined as 1 - cor(x, y, method = distance).
a self-defined function which calculates distance from a matrix. The function should only contain one argument. Please note for clustering on columns, the matrix will be transposed automatically.

ggheatmap(small_mat) +
    hmanno("t") +
    align_dendro(distance = "pearson") +
    patch_titles(top = "pre-defined distance method (1 - pearson)")

ggheatmap(small_mat) +
    hmanno("t") +
    align_dendro(distance = function(m) dist(m)) +
    patch_titles(top = "a function that calculates distance matrix")

Method to perform hierarchical clustering can be specified by method. Possible methods are those supported in hclust() function. And you can also provide a self-defined function, which accepts the distance object and return a hclust object.

ggheatmap(small_mat) +
    hmanno("t") +
    align_dendro(method = "ward.D2")

The dendrogram can also be used to cut the columns/rows into groups. You can specify k or h, which work similarly to cutree():

ggheatmap(small_mat) +
    hmanno("t") +
    align_dendro(k = 3L)

In contrast to align_group(), align_kmeans(), and align_reorder(), align_dendro() is capable of drawing plot components. So it has a default set_context value of TRUE, meaning it will set the active context of the annotation stack layout. In this way, we can add any ggplot elements to this plot area.

ggheatmap(small_mat) +
    hmanno("t") +
    align_dendro() +
    geom_point(aes(y = y))

The align_dendro() function creates default node data for the ggplot. See ggplot2 specification in ?align_dendro for details. Additionally, edge data is added to the ggplote::geom_segment() layer directly, used to draw the dendrogram tree. One useful variable in both node and edge data is the branch column, corresponding to the cutree result:

ggheatmap(small_mat) +
    hmanno("t") +
    align_dendro(aes(color = branch), k = 3) +
    geom_point(aes(color = branch, y = y))

align_dendro() can also perform clustering between groups, meaning it can be used even if there are existing groups present in the layout:

column_groups <- sample(letters[1:3], ncol(small_mat), replace = TRUE)
ggheatmap(small_mat) +
    hmanno("t") +
    align_group(column_groups) +
    align_dendro(aes(color = branch))

You can reorder the groups by setting reorder_group = TRUE.

ggheatmap(small_mat) +
    hmanno("t") +
    align_group(column_groups) +
    align_dendro(aes(color = branch), reorder_group = TRUE)

You can see the difference by drawing two dendrogram.

ggheatmap(small_mat) +
    hmanno("t") +
    align_group(column_groups) +
    align_dendro(aes(color = branch), reorder_group = TRUE) +
    hmanno("b") +
    align_dendro(aes(color = branch), reorder_group = FALSE)

Session information

sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggalign_0.0.4 ggplot2_3.5.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.5      jsonlite_1.8.8    highr_0.11        dplyr_1.1.4      
#>  [5] compiler_4.4.1    tidyselect_1.2.1  tidyr_1.3.1       jquerylib_0.1.4  
#>  [9] systemfonts_1.1.0 scales_1.3.0      textshaping_0.4.0 yaml_2.3.10      
#> [13] fastmap_1.2.0     ggh4x_0.2.8       R6_2.5.1          labeling_0.4.3   
#> [17] generics_0.1.3    knitr_1.48        tibble_3.2.1      desc_1.4.3       
#> [21] munsell_0.5.1     bslib_0.8.0       pillar_1.9.0      rlang_1.1.4      
#> [25] utf8_1.2.4        cachem_1.1.0      xfun_0.47         fs_1.6.4         
#> [29] sass_0.4.9        cli_3.6.3         pkgdown_2.1.1     withr_3.0.1      
#> [33] magrittr_2.0.3    digest_0.6.37     grid_4.4.1        lifecycle_1.0.4  
#> [37] vctrs_0.6.5       evaluate_1.0.0    glue_1.7.0        farver_2.1.2     
#> [41] ragg_1.3.3        fansi_1.0.6       colorspace_2.1-1  rmarkdown_2.28   
#> [45] purrr_1.0.2       tools_4.4.1       pkgconfig_2.0.3   htmltools_0.5.8.1