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_order
The align_order() function order the rows/columns based
on the summary weights, Like align_group(), it doesn’t add
a plot area.
Here, we order the rows based on the means.
ggheatmap(small_mat) +
hmanno("l") +
align_order(rowMeans)
In addition, we can provide the ordering integer index directly in
the order argument.
my_order <- sample(nrow(small_mat))
print(rownames(small_mat)[my_order])
#> [1] "row3" "row1" "row7" "row6" "row2" "row8" "row9" "row5" "row4"
ggheatmap(small_mat) +
hmanno("l") +
align_order(my_order)
We can also provide the ordering character index.
ggheatmap(small_mat) +
hmanno("l") +
align_order(rownames(small_mat)[my_order])
By default, align_order() 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 reverse = TRUE:
ggheatmap(small_mat) +
hmanno("l") +
align_order(rowMeans, reverse = 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.
It is important to note that all align_* functions
consider the rows as the observations. It means the
NROW(data) must return the same number with the specific
layout axis.
heatmap_layout()/ggheatmap(): for
column annotation, the layout data will be transposed
before using (If data is a function, it will be applied
with the transposed matrix). This is necessary because column annotation
uses heatmap columns as observations, but we need rows.
stack_layout()/ggstack(): the
layout data will be used as it is since we place all plots
along a single axis.
Even for top and bottom annotations, you can use
rowMeans() to calculate the mean value across all
columns.
ggheatmap(small_mat) +
hmanno("t") +
align_order(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 all align_* functions which
define groups must not break the previous established groups. This means
the new groups must nest in the old groups, in this way, usually 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()` disrupt the previously established panel groups of
#> the layout x-axis
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()` disrupt the previously established panel groups of the
#> layout x-axis
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_order(),
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))
You can reorder the dendrogram based on the mean values of the
observations by setting reorder_dendrogram = TRUE.
h1 <- ggheatmap(small_mat) +
hmanno("t") +
align_dendro(aes(color = branch), k = 3, reorder_dendrogram = TRUE) +
ggtitle("reorder_dendrogram = TRUE")
h2 <- ggheatmap(small_mat) +
hmanno("t") +
align_dendro(aes(color = branch), k = 3) +
ggtitle("reorder_dendrogram = FALSE")
align_plots(h1, h2)
align_dendro() can also perform clustering between
groups, meaning it can be used even if there are existing groups present
in the layout, in this way, you cannot specify k or
h:
set.seed(3L)
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 merge the sub-tree in each group by settting
merge_dendrogram = TRUE.
ggheatmap(small_mat) +
hmanno("t") +
align_group(column_groups) +
align_dendro(aes(color = branch), merge_dendrogram = TRUE)
You can reorder the dendrogram and merge simutaneously.
ggheatmap(small_mat) +
hmanno("t") +
align_group(column_groups) +
align_dendro(aes(color = branch),
reorder_group = TRUE,
merge_dendrogram = TRUE
) +
hmanno("b") +
align_dendro(aes(color = branch),
reorder_group = FALSE,
merge_dendrogram = TRUE
)
If you specify k or h, this will always
turn off sub-clustering. The same principle applies to
align_dendro(), where new groups must be nested within the
previously established groups.
ggheatmap(small_mat) +
hmanno("t") +
align_group(column_groups) +
align_dendro(k = 2L)
#> Error in `align_dendro()`:
#> ! `align_dendro()` disrupt the previously established panel groups of
#> the layout x-axis