主要操作

使用国内镜像

options("repos"= c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="http://mirrors.ustc.edu.cn/bioc/")

加载包

PS： ComplexHeatmap包的安装需要安装github版本的。很多函数在bioconductor版本没有。

library(ComplexHeatmap)

## Loading required package: grid

## ========================================
## ComplexHeatmap version 2.1.2
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
## 
## If you use it in published research, please cite:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
##   genomic data. Bioinformatics 2016.
## ========================================

library(circlize)

## ========================================
## circlize version 0.4.8
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: http://jokergoo.github.io/circlize_book/book/
## 
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization 
##   in R. Bioinformatics 2014.
## ========================================

Sys.setenv(LANGUAGE = "en") #显示英文报错信息
options(stringsAsFactors = FALSE) #禁止chr转成factor

输入文件

输入文件使用之前的示例文件

# 左上角的数据
up <- read.table("easy_input_amp.txt",sep = "\t",row.names = 1,check.names = F,stringsAsFactors = F,header = T)
up[1:3, 1:3]

##              KIRC      KIRP       KICH
## ALKBH5 0.04924242 0.5902778 0.00000000
## FTO    0.18560606 0.5243056 0.31818182
## ZC3H13 0.03977273 0.1006944 0.03030303

# 右下角的数据
dn <- read.table("easy_input_del.txt",sep = "\t",row.names = 1,check.names = F,stringsAsFactors = F,header = T)
dn[1:3, 1:3]

##              KIRC       KIRP       KICH
## ALKBH5 0.08522727 0.04861111 0.75757576
## FTO    0.04166667 0.02430556 0.07575758
## ZC3H13 0.15340909 0.08333333 0.66666667

# 检验两个矩阵列名和行名是一致的
identical(rownames(up), rownames(dn))

## [1] TRUE

identical(colnames(up), colnames(dn))

## [1] TRUE

绘图前处理

由于加载的数据的基因名和注释的结果的列是反的，重新排列数据框

RightOrder <- rev(rownames(up))
up <- up[RightOrder,]
dn <- dn[RightOrder,]

设置颜色

对于ComplexHeatmap当中的颜色设置都是通过，colorRamp2来进行设置的

UpColor <- colorRamp2(breaks = c(0, 1), colors = c("#FFFADD","#AB221F"))
DnColor <- colorRamp2(breaks = c(0, 1), colors = c("#FFFADD","#3878C1"))

绘图

使用ComplexHeatmap绘图的原理主要是

选择一个数据集，绘制一个空的边框。对于热图当中的聚类，则是基于这个数据集来进行的。同时热图的绘制会自动产生一个颜色的注释，这个不是我们需要的，所以需要去掉

## 使用up数据集来产生数据
Heatmap(up, 
        column_title = "Copy number variation across cancer types", ## 列的标题
        rect_gp = gpar(type = "none"),  #绘制空的数据框
        show_heatmap_legend = F, ##是否显示基本的注释说明
        cluster_rows = T, cluster_columns = T, ## 是否对行列进行聚类 
        )

## Warning: The input is a data frame, convert it to the matrix.

绘制左侧的注释信息

使用HeatmapAnnotation绘制注释信息，

row_an <-  HeatmapAnnotation(type = c(rep("R", 10), rep("W", 8), rep("E", 2)), ##注释信息的内容。
                                show_annotation_name = F, ## 是否显示注释的标题
                                col = list(type = c("R" = "#5AC9FA", "W" = "#FAC67A", "E" = "#51B743")), ## 注释信息的颜色
                                show_legend = T,  ## 是否显示注释信息的说明
                                annotation_legend_param = list(title = "m6A group",nrow = 1), ## 注释信息图例的个性化说明，nrow表示把所有分类的图例放到一行。
                                which = "row" #对行或者列进行注释 
                             )

通过cell_fun参数来自定义每个热图方格内显示的内容。这里我们主要通过两个grid.polygon绘制两个三角形，三角形当中的填充色分别是up和down的值。

首先自定义一个接受两个数据集合的函数

DiagFunc <- function(up, down){
    function(j, i, x, y, width, height, fill){
        grid.polygon(unit.c(x - 0.5*width, x - 0.5*width, x + 0.5*width), 
                         unit.c(y - 0.5*height, y + 0.5*height, y + 0.5*height),
                         gp = gpar(fill = DnColor(down[i, j]), col = "grey")) 
        grid.polygon(unit.c(x + 0.5*width, x + 0.5*width, x - 0.5*width), 
                         unit.c(y + 0.5*height, y - 0.5*height, y - 0.5*height),
                         gp = gpar(fill = UpColor(up[i, j]), col = "grey"))
    }
}

绘制基本图形

p1 <- Heatmap(up, column_title = "Copy number variation across cancer types", rect_gp = gpar(type = "none"), show_heatmap_legend = F,  cluster_rows = F, cluster_columns = T, ##绘制空的热图框
              left_annotation = row_an, ##添加左侧注释信息
              cell_fun = DiagFunc(up = up, down = dn) ## 绘制表格内的内容
              ); p1

## Warning: The input is a data frame, convert it to the matrix.

绘制注释

图片基本绘制完成，但是热图当中的颜色还没有具体的说明，ComplexHeatmap可以通过Legend绘制自定义自定义注释。

col_fun = colorRamp2(c(-1, 0, 1), c("#3878C1", "#FFFADD", "#AB221F")) ##自定义颜色信息
lgd <- Legend(title = "CNV Frequency", ## 注释的标题
              col_fun = col_fun, ## 注释的颜色
              at = c(-1,-0.5,0,0.5,1), ## 注释刻度的分组
              labels = c("1","Loss","0","Gain","1"),  ## 注释刻度的重命名
              direction = "horizontal" ## 注释的方向
              )

热图和注释融合到一起

通过draw函数可以把热图和注释融合到一起

draw(p1, annotation_legend_list = lgd, annotation_legend_side = "bottom",heatmap_legend_side = "bottom", merge_legend = TRUE)

DiagHeatmap

Hao

10/29/2019

写在前面