我们用这个表达量文件先做一个简单的热图

读入数据

df<-read.csv("NM/NK_markers_1.csv",header=T,row.names = 1)
head(df)

最简单的热图

library(pheatmap)
pdf(file = "NM/hp-1.pdf",width = 4,height = 10)
pheatmap(df,fontsize = 3)
dev.off()

image.png

我们可以看到上图右侧所有的基因名都显示出来了，如果我们想只显示自己感兴趣的，那该如何实现呢？可以用开头提到的自定义函数

add.flag <- function(pheatmap,
                     kept.labels,
                     repel.degree) {
  
  # repel.degree = number within [0, 1], which controls how much 
  #                space to allocate for repelling labels.
  ## repel.degree = 0: spread out labels over existing range of kept labels
  ## repel.degree = 1: spread out labels over the full y-axis
  
  heatmap <- pheatmap$gtable
  
  new.label <- heatmap$grobs[[which(heatmap$layout$name == "row_names")]] 
  
  # keep only labels in kept.labels, replace the rest with ""
  new.label$label <- ifelse(new.label$label %in% kept.labels, 
                            new.label$label, "")
  
  # calculate evenly spaced out y-axis positions
  repelled.y <- function(d, d.select, k = repel.degree){
    # d = vector of distances for labels
    # d.select = vector of T/F for which labels are significant
    
    # recursive function to get current label positions
    # (note the unit is "npc" for all components of each distance)
    strip.npc <- function(dd){
      if(!"unit.arithmetic" %in% class(dd)) {
        return(as.numeric(dd))
      }
      
      d1 <- strip.npc(dd$arg1)
      d2 <- strip.npc(dd$arg2)
      fn <- dd$fname
      return(lazyeval::lazy_eval(paste(d1, fn, d2)))
    }
    
    full.range <- sapply(seq_along(d), function(i) strip.npc(d[i]))
    selected.range <- sapply(seq_along(d[d.select]), function(i) strip.npc(d[d.select][i]))
    
    return(unit(seq(from = max(selected.range) + k*(max(full.range) - max(selected.range)),
                    to = min(selected.range) - k*(min(selected.range) - min(full.range)), 
                    length.out = sum(d.select)), 
                "npc"))
  }
  new.y.positions <- repelled.y(new.label$y,
                                d.select = new.label$label != "")
  new.flag <- segmentsGrob(x0 = new.label$x,
                           x1 = new.label$x + unit(0.15, "npc"),
                           y0 = new.label$y[new.label$label != ""],
                           y1 = new.y.positions)
  
  # shift position for selected labels
  new.label$x <- new.label$x + unit(0.2, "npc")
  new.label$y[new.label$label != ""] <- new.y.positions
  
  # add flag to heatmap
  heatmap <- gtable::gtable_add_grob(x = heatmap,
                                     grobs = new.flag,
                                     t = 4, 
                                     l = 4
  )
  
  # replace label positions in heatmap
  heatmap$grobs[[which(heatmap$layout$name == "row_names")]] <- new.label
  
  # plot result
  grid.newpage()
  grid.draw(heatmap)
  
  # return a copy of the heatmap invisibly
  invisible(heatmap)
}

将以上函数放到文本文件里，通过source()加载这个函数

source("useful_R_function/add_flag.r")

选择感兴趣的基因名，我这里就随机选取几个了

gene_name<-sample(rownames(df),10)

画图

source("useful_R_function/add_flag.r")
library(grid)
gene_name<-sample(rownames(df),10)
p1<-pheatmap(df)
add.flag(p1,
         kept.labels = gene_name,
         repel.degree = 0.2)

结果就变成了如下

image.png

接下来是简单的美化

代码

source("useful_R_function/add_flag.r")
df<-read.csv("NM/NK_markers_1.csv",header=T,row.names = 1)
head(df)
library(pheatmap)
library(grid)
gene_name<-sample(rownames(df),10)
paletteLength <- 100
mycolor<-colorRampPalette(c("blue","white","red"))(100)
mycolor
myBreaks <- unique(c(seq(min(df), 0, length.out=ceiling(paletteLength/2) + 1), 
                     seq(max(df)/paletteLength, max(df),
                         length.out=floor(paletteLength/2))))
p1<-pheatmap(df,color = mycolor,breaks = myBreaks)
pdf(file = "NM/hp-2.pdf",width = 4,height = 8)
add.flag(p1,
         kept.labels = gene_name,
         repel.degree = 0.2)
dev.off()