Fig2b，分为三部分：
上图为细胞系表达水平的箱线图。中间为热图，显示乳腺癌及其相关生物学过程中预测的抑癌基因和癌基因top50。基于欧氏距离矩阵进行层次聚类。下图是颜色标记不同注释信息。红色(蓝色)标记Moonlight基因得分加(减)的生物过程。特定基因突变的样本数量从白色到深紫色不等。超甲基化DMR显示为蓝色，去甲基化黄色。KM生存预后差的基因标记为粉红色。启动子区域的染色质开放性展示为白色（关闭）—橙色。
猛一看有点复杂┗|｀O′|┛ 嗷~~莫不是要单独画出三部分图再拼接到一起？也不是不行，本小编以前画图就只会用笨方法。。。
后来，我找到了这个神器——ComplexHeatmap。看这个R包的直译就知道啦，它是用来画复杂的热图。那到底有多复杂？小编带你一览庐山真面目。
##bioconductor和github都能安装if (!requireNamespace("BiocManager", quietly = TRUE))install.packages("BiocManager")BiocManager::install("ComplexHeatmap")library(ComplexHeatmap)??ComplexHeatmap
Heatmap(mat)##最基础的热图
Heatmap(mat,row_split = tab[,2],##分类标签width = unit(18, "cm"), height = unit(18, "cm"),##热图主体的大小show_heatmap_legend = FALSE,##是否展示图例cluster_row_slices = FALSE,##去掉虚线row_title_rot = 0,##行标签竖向显示cell_fun = function(j, i, x, y, width, height, fill){grid.text(sprintf("%.1f", mat[i, j]), x, y,gp = gpar(fontsize = 10))},##热图添加数值)##加图例col_fun = colorRamp2(c(-2,0,2), c("blue", "white", "red"))lgd = Legend(col_fun = col_fun, title = "expr_value")draw(lgd,x = unit(24, "cm"), y = unit(25, "cm"),just = c("right", "top"))##数值要做调整
Heatmap(mat,row_split = tab[,2],##分类标签width = unit(18, "cm"), height = unit(18, "cm"),##show_heatmap_legend = FALSE,##是否展示图例cluster_row_slices = FALSE,##去掉虚线row_title_rot = 0,##行标签竖向显示cell_fun = function(j, i, x, y, width, height, fill){grid.text(sprintf("%.1f", mat[i, j]), x, y,gp = gpar(fontsize = 10))},##热图添加数值top_annotation = HeatmapAnnotation(foo = anno_boxplot(mat, height = unit(4, "cm"),gp = gpar(fill = 1:24),##填充颜色box_width = 0.3,##箱子宽度outline = FALSE##是否显示离群点))##将列注释放到热图中)col_fun = colorRamp2(c(-2,0,2), c("blue", "white", "red"))lgd = Legend(col_fun = col_fun, title = "expr_value")draw(lgd,x = unit(26, "cm"), y = unit(23, "cm"),just = c("right", "top"))##数值要做调整
还可以添加不同形式的列注释：
加到左侧left_annotation = rowAnnotation()的块注释anno_block()
加到右侧right_annotation = rowAnnotation()的直方图注释anno_histogram()
Heatmap(mat,row_split = tab[,2],##分类标签width = unit(18, "cm"), height = unit(18, "cm"),##show_heatmap_legend = FALSE,##是否展示图例cluster_row_slices = FALSE,##去掉虚线row_title_rot = 0,##行标签竖向显示cell_fun = function(j, i, x, y, width, height, fill) {grid.text(sprintf("%.1f", mat[i, j]), x, y, gp = gpar(fontsize = 10))},##热图添加数值top_annotation = HeatmapAnnotation(foo = anno_boxplot(mat, height = unit(4, "cm"),gp = gpar(fill = 1:24),##填充颜色box_width = 0.3,##箱子宽度outline = FALSE##是否显示离群点)),##将列注释放到热图中left_annotation = rowAnnotation(foo = anno_block(gp = gpar(fill = 2:3))),right_annotation = rowAnnotation(foo = anno_histogram(mat,gp = gpar(fill = 2:3))),)
右侧密度图注释anno_density()
Heatmap(mat,row_split = tab[,2],##分类标签width = unit(18, "cm"), height = unit(18, "cm"),##show_heatmap_legend = FALSE,##是否展示图例cluster_row_slices = FALSE,##去掉虚线row_title_rot = 0,##行标签竖向显示cell_fun = function(j, i, x, y, width, height, fill) {grid.text(sprintf("%.1f", mat[i, j]), x, y, gp = gpar(fontsize = 10))},##热图添加数值top_annotation = HeatmapAnnotation(foo = anno_boxplot(mat, height = unit(4, "cm"),gp = gpar(fill = 1:24),##填充颜色box_width = 0.3,##箱子宽度outline = FALSE##是否显示离群点)),##将列注释放到热图中##根据特征标签数据框tab来标注right_annotation = rowAnnotation(target = tab$target,expr = tab$expr,mutation=tab$mutation))
Heatmap(mat,row_split = tab[,2],##分类标签width = unit(16, "cm"), height = unit(16, "cm"),##show_heatmap_legend = FALSE,##是否展示图例cluster_row_slices = FALSE,##去掉虚线row_title_rot = 0,##行标签竖向显示cell_fun = function(j, i, x, y, width, height, fill) {grid.text(sprintf("%.1f", mat[i, j]), x, y, gp = gpar(fontsize = 10))},##热图添加数值top_annotation = HeatmapAnnotation(foo = anno_boxplot(mat, height = unit(4, "cm"),gp = gpar(fill = 1:24),##填充颜色box_width = 0.3,##箱子宽度outline = FALSE##是否显示离群点)),##将列注释放到热图中#right_annotation = rowAnnotation(target = tab$target, expr = tab$expr,mutation=tab$mutation),bottom_annotation = HeatmapAnnotation(age = sampletab$age, sex = sampletab$sex,clinical=sampletab$clinical),)col_fun = colorRamp2(c(-2,0,2), c("blue", "white", "red"))lgd = Legend(col_fun = col_fun, title = "expr_value")draw(lgd,x = unit(27, "cm"), y = unit(25, "cm"),just = c("right", "top"))##数值要做调整
