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使用ComplexHeatmap简单绘制热图

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ComplexHeatmap其实是一个很全面的R包，它除了可以绘制简单热图还有其他复杂实用的热图，这里主要简单的介绍一下如何用这个R包来绘制简单热图 1. 首先按照http://bioconductor.org/packages/release/bioc/vignettes/ComplexHeatmap/inst/doc/s2.single_heatmap.html 创建一个测试数据

    library(ComplexHeatmap)
    library(circlize)

    set.seed(123)

    mat = cbind(rbind(matrix(rnorm(16, -1), 4), matrix(rnorm(32, 1), 8)),
            rbind(matrix(rnorm(24, 1), 4), matrix(rnorm(48, -1), 8)))

    # permute the rows and columns
    mat = mat[sample(nrow(mat), nrow(mat)), sample(ncol(mat), ncol(mat))]

    rownames(mat) = paste0("R", 1:12)
    colnames(mat) = paste0("C", 1:10)

  1. 可以使用circlize包里面的colorRampl2函数来设定热图的颜色参数，比如下面例子中的(-3,0,3)分别对应green,white,red三种颜色，但当值是大于3或者小于-3时，则也是对应临界点的green和red

     Heatmap(mat, col = colorRamp2(c(-3, 0, 3), c("green", "white", "red")), 
     cluster_rows = FALSE, cluster_columns = FALSE)
 ###对于连续型数据
 Heatmap(mat, col = rev(rainbow(10)))
 ###对于离散型数据
 discrete_mat = matrix(sample(1:4, 100, replace = TRUE), 10, 10)
 colors = structure(circlize::rand_color(4), names = c("1", "2", "3", "4"))
 Heatmap(discrete_mat, col = colors)

  2. name 参数来设置图例的名称，column_title 参数来设置列的名称，row_title 参数来设置行的名称， column_title_side 参数设置title的位置，row_title_rot 参数设置row title的方向（垂直 or 水平）

     Heatmap(mat, name = "foo", column_title = "I am a column title", 
     row_title = "I am a row title"，column_title_side = "bottom"， row_title_rot = 0)

  3. 这个包对于cluster的设定有很多参数，比如cluster_rows，逻辑值，表示是否要对行进行聚类，当其为某个特定值时，则表示对行树杈进行处理；show_column_dend，当为逻辑值时，表示是否要显示列的树杈，当为位置值时，表示显示列的位置，row_dend_side也是同理；column_dend_height 表示树杈的高度

     Heatmap(mat, name = "foo", cluster_rows = FALSE)
 Heatmap(mat, name = "foo", show_column_dend = FALSE)
 Heatmap(mat, name = "foo", row_dend_side = "right")
 Heatmap(mat, name = "foo", column_dend_height = unit(2, "cm"))

 library(dendextend)
 dend = hclust(dist(mat))
 dend = color_branches(dend, k = 2)
 Heatmap(mat, name = "foo", cluster_rows = dend)

  4. cluster的参数除了上述以外，还有对应选择聚类距离的参数，如：clustering_distance_rows，对应行聚类距离的参数，其除了指定某个聚类距离外，还可以调用其他聚类距离相关函数以及自定义函数

     Heatmap(mat, name = "foo", clustering_distance_rows = "pearson")
 Heatmap(mat, name = "foo", clustering_distance_rows = function(m) dist(m))
 Heatmap(mat, name = "foo", clustering_distance_rows = function(x, y) 1 - cor(x, y))

  5. 除了设定cluster距离参数，还可以设定hierarchical cluster的方法，clustering_method_rows；这里跟上述的聚类距离一样，除了可以使用默认的hclust（）函数里面的参数，还可以调用其他函数；

     Heatmap(mat, name = "foo", clustering_method_rows = "single")
 library(cluster)
 Heatmap(mat, name = "foo", cluster_rows = as.dendrogram(diana(mat)),
     cluster_columns = as.dendrogram(agnes(t(mat))))

  6. row_dend_reorder 参数，逻辑值，用于控制对行是否进行排序，默认的开启的；而对于row_order 则可以人为对行进行排序，如下

     Heatmap(mat, name = "foo", cluster_rows = FALSE, cluster_columns = FALSE, 
     row_order = 12:1, column_order = 10:1)

  7. 接下来介绍几个对行名 or 列名，或者行树杈 or 列树杈进行自定义的参数， row_names_side 表示行名的位置，row_dend_side 表示行树杈的位置，列也类似；row_names_gp 参数可以控制行名的大小颜色等；

     Heatmap(mat, name = "foo", row_names_side = "left", row_dend_side = "right", 
     column_names_side = "top", column_dend_side = "bottom")
 Heatmap(mat, name = "foo", row_names_gp = gpar(col = c(rep("red", 4), rep("blue", 8))))
 Heatmap(mat, name = "foo", row_names_gp = gpar(fontsize = 20))

  8. A heatmap can be split by rows. This will enhance the visualization of group separation in the heatmap.The km argument with a value larger than 1 means applying a k-means clustering on rows and clustering is applied on every k-means cluster.其他的分割方式以及排序方式可以查看开头网站里面的介绍

     Heatmap(mat, name = "foo", km = 2)

  9. 最后就是Self define the heatmap body，其中主要是如何运用cell_fun这个可以自定义的函数，网站里有对其详细的解释，这里就说下如何用其来对热图框中显示数字

    Heatmap(mat, name = "foo", cell_fun = function(j, i, x, y, width, height, fill) {
    grid.text(sprintf("%.1f", mat[i, j]), x, y, gp = gpar(fontsize = 10))
})

Summary

按照以上的参数，通过自定义上述参数，做一张自己想要的热图，如：

library(ComplexHeatmap)
library(circlize)
library(dendextend)

set.seed(1)

mat = cbind(rbind(matrix(rnorm(16, -1), 4), matrix(rnorm(32, 1), 8)),
            rbind(matrix(rnorm(24, 1), 4), matrix(rnorm(48, -1), 8)))

# permute the rows and columns
mat = mat[sample(nrow(mat), nrow(mat)), sample(ncol(mat), ncol(mat))]
rownames(mat) = paste0("R", 1:12)
colnames(mat) = paste0("C", 1:10)

# color for dendrogram
dend = hclust(dist(mat))
dend = color_branches(dend, k = 2)

Heatmap(mat,name = "foo", 
        column_title = "This is a column title",
        column_names_side = "top",
        row_title_rot = 90, 
        column_title_rot = 0,
        col = colorRamp2(c(-3, 0, 3), c("green", "white", "red")),
        cluster_rows = dend, 
        row_dend_side = "left",
        row_names_side = "left",
        row_title = c("cluster1","cluster2"),
        column_dend_height = unit(2, "cm"),
        clustering_method_rows = "complete",
        split = 2,
        row_title_gp = gpar(col = c("red", "green")),
#        row_names_gp = gpar(col = c("red", "green")),
        cell_fun = function(j, i, x, y, width, height, fill) {
          grid.text(sprintf("%.1f", mat[i, j]), x, y, gp = gpar(fontsize = 10))
          grid.rect(x = x, y = y, width = width, height = height, gp = gpar(col = "grey", fill = NA))
        }
)
<img width="520" height="360" src="http://www.bioinfo-scrounger.com/data/photo/heatmap.png">

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