热图装饰
在制作热图后，每个热图组件的绘图区域仍然保留，因此如何回到原始位置进而添加更多图形呢？看如下示例。
首先生成一个几乎包含所有类型的热图组件的图。list_components()列出热图/注释组件的名称（或视口的名称）。
set.seed(123)
mat = matrix(rnorm(80, 2), 8, 10)
mat = rbind(mat, matrix(rnorm(40, -2), 4, 10))
rownames(mat) = paste0("R", 1:12)
colnames(mat) = paste0("C", 1:10)
ha_column1 = HeatmapAnnotation(points = anno_points(rnorm(10)),
annotation_name_side = "left")
ht1 = Heatmap(mat, name = "ht1", km = 2, column_title = "Heatmap 1",
top_annotation = ha_column1, row_names_side = "left")
ha_column2 = HeatmapAnnotation(type = c(rep("a", 5), rep("b", 5)),
col = list(type = c("a" = "red", "b" = "blue")))
ht2 = Heatmap(mat, name = "ht2", row_title = "Heatmap 2", column_title = "Heatmap 2",
bottom_annotation = ha_column2, column_km = 2)
ht_list = ht1 + ht2 +
rowAnnotation(bar = anno_barplot(rowMeans(mat), width = unit(2, "cm")))
draw(ht_list, row_title = "Heatmap list", column_title = "Heatmap list")
list_components()
## [1] "ROOT" "global"
## [3] "global_layout" "global-heatmaplist"
## [5] "main_heatmap_list" "heatmap_ht1"
## [7] "ht1_heatmap_body_wrap" "ht1_heatmap_body_1_1"
## [9] "ht1_heatmap_body_2_1" "ht1_column_title_1"
## [11] "ht1_row_title_1" "ht1_row_title_2"
## [13] "ht1_dend_row_1" "ht1_dend_row_2"
## [15] "ht1_dend_column_1" "ht1_row_names_1"
## [17] "ht1_row_names_2" "ht1_column_names_1"
## [19] "annotation_points_1" "heatmap_ht2"
## [21] "ht2_heatmap_body_wrap" "ht2_heatmap_body_1_1"
## [23] "ht2_heatmap_body_1_2" "ht2_heatmap_body_2_1"
## [25] "ht2_heatmap_body_2_2" "ht2_column_title_1"
## [27] "ht2_dend_column_1" "ht2_dend_column_2"
## [29] "ht2_column_names_1" "ht2_column_names_2"
## [31] "annotation_type_1" "annotation_type_2"
## [33] "heatmap_heatmap_annotation_2" "annotation_bar_1"
## [35] "annotation_bar_2" "global-column_title_top"
## [37] "global_column_title" "global-row_title_left"
## [39] "global_row_title" "global-heatmap_legend_right"
## [41] "heatmap_legend" "global-annotation_legend_right"
## [43] "annotation_legend"
image
基本上可以通过decorate_*() 函数重新访问上图中的红色区域。
6.1 装饰函数
由于可以通过list_components()获取视窗名称，因此实际上可以通过seekViewport()直接进入视窗。为了摆脱复杂的视窗名称，这些decorate_*()函数提供了一种更友好的方法。
ComplexHeatmap包中有以下装饰函数：
decorate_heatmap_body()decorate_annotation()decorate_dend()decorate_title()decorate_dimnames()-
decorate_row_names()，等同于decorate_dimnames(..., which = "row")。 -
decorate_column_names()，等同于decorate_dimnames(..., which = "column")。 -
decorate_row_dend()，等同于decorate_dend(..., which = "row")。 -
decorate_column_dend()，等同于decorate_dend(..., which = "column")。 -
decorate_row_title()，等同于decorate_title(..., which = "row")。 -
decorate_column_title()，等同于decorate_title(..., which = "column")。
其中，decorate_heatmap_body()和decorate_annotation()经常使用。
对于所有这些功能，它们需要一个热图或注释名称、行/列切片的索引（如果热图被拆分）以及定义如何添加图形的代码块。查看以下示例。
ht_list = draw(ht_list, row_title = "Heatmap list", column_title = "Heatmap list",
heatmap_legend_side = "right", annotation_legend_side = "left")
decorate_heatmap_body("ht1", {
grid.text("outlier", 1.5/10, 2.5/4, default.units = "npc")
grid.lines(c(0.5, 0.5), c(0, 1), gp = gpar(lty = 2, lwd = 2))
}, slice = 2)
decorate_column_dend("ht1", {
tree = column_dend(ht_list)$ht1[[1]]
ind = cutree(as.hclust(tree), k = 2)[order.dendrogram(tree)]
first_index = function(l) which(l)[1]
last_index = function(l) { x = which(l); x[length(x)] }
x1 = c(first_index(ind == 1), first_index(ind == 2)) - 1
x2 = c(last_index(ind == 1), last_index(ind == 2))
grid.rect(x = x1/length(ind), width = (x2 - x1)/length(ind), just = "left",
default.units = "npc", gp = gpar(fill = c("#FF000040", "#00FF0040"), col = NA))
})
decorate_row_names("ht1", {
grid.rect(gp = gpar(fill = "#FF000040"))
}, slice = 2)
decorate_row_title("ht1", {
grid.rect(gp = gpar(fill = "#00FF0040"))
}, slice = 1)
decorate_annotation("points", {
grid.lines(c(0, 1), unit(c(0, 0), "native"), gp = gpar(col = "red"))
})
image
对于由anno_points()和anno_barplot()所创建的注解，anno_boxplot()可以在装饰代码中使用“原生”单元。
6.2 例子
6.2.1 单列热图的条形图
在前面章节中，我们介绍了添加条形图作为单列热图的注释。在这种情况下，热图包含离散值，其中条形图显示每个级别的频率。在以下示例中，我们展示了使用条形图作为注释但用于连续热图的另一种场景。
想象一下，我们正在分析一组基因组区域（例如差异甲基化区域，DMR），并且我们有一个单列热图，显示与假设基因的重叠（通过基因覆盖的 DMR 的分数来衡量，值介于 0 和 1 之间，例如，0.5 的值意味着该 DMR 的 50% 与基因重叠）。如果我们将 DMR 的宽度表示为w和分数记为p，在分数热图之上，我们想添加条形图来显示平均有多少 DMR 被基因覆盖。在这种情况下，我们需要计算由 DMR 的宽度加权的平均分数 ( ∑(w⋅p)/∑w ).
在下面的代码中，我们随机生成了一个分数向量并将其分成两组。我们首先使用anno_empty()在热图顶部分配空白绘图区域，然后使用decorate_annotation()将条形图添加到其中。
library(circlize)
# DMRs
bed = generateRandomBed(nr = 1000)
# fractions
frac = c(runif(400, min = 0.4, max = 1), runif(nrow(bed) - 400, min = 0.2, max = 0.6))
col_fun = colorRamp2(c(0, 1), c("white", "orange"))
# two groups
split = c(rep("group1", 400), rep("group2", nrow(bed) - 400))
# draw the fraction heatmap with an empty annotation
ht = Heatmap(frac, name = "fraction", col = col_fun, width = unit(2, "cm"),
top_annotation = HeatmapAnnotation(barplot = anno_empty(height = unit(4, "cm"))))
ht = draw(ht, row_split = split)
# get the row indices in the two row-groups
ro = row_order(ht)
w = bed[, 3] - bed[, 2]
# the mean weighted fraction in the two groups
p = sapply(ro, function(index) {
sum(w[index]*frac[index])/sum(w[index])
})
# add two bars of `p`
decorate_annotation("barplot", {
pushViewport(viewport(xscale = c(0.5, 2.5), yscale = c(0, max(p)*1.1)))
grid.rect(x = 1:2, y = 0, width = 0.8, height = p, just = "bottom",
gp = gpar(fill = "orange"), default.units = "native")
grid.yaxis()
grid.text("mean fraction", x = unit(-1.5, "cm"),rot = 90, just = "bottom")
popViewport()
})
image
6.2.2 行注释标题
行注释可以连接到热图列表。有时我们需要行注释的标题。通过装饰很容易实现它。
ht_list = Heatmap(matrix(rnorm(100), 10), name = "mat", show_column_dend = FALSE) +
rowAnnotation(foo = anno_barplot(1:10, width = unit(4, "cm"))) +
rowAnnotation(bar = anno_boxplot(matrix(rnorm(100), 10)), width = unit(4, "cm"))
draw(ht_list, padding = unit(c(2, 2, 10, 2), "mm")) # add space for titles
decorate_annotation("foo", {
grid.text("title for barplot", y = unit(1, "npc") + unit(2, "mm"), just = "bottom")
})
decorate_annotation("bar", {
grid.text("title for boxplot", y = unit(1, "npc") + unit(2, "mm"), just = "bottom")
})
decorate_heatmap_body("mat", {
grid.text("title for the heatmap", y = unit(1, "npc") + unit(2, "mm"), just = "bottom")
})
image