在簡書 土豆學生信 分享的內容看到這篇論文 簡書的鏈接是 http://www.lxweimin.com/p/bbf9cb13b41a

論文是

image.png

論文對應的代碼是公開的 https://github.com/ajwilk/2020_Wilk_COVID

image.png

在學習他這個代碼的時候發現其中自定義了一個函數可以操作熱圖的文字標簽，可以讓熱圖上只顯示我們感興趣的文字標簽。

我在運行這個代碼的時候遇到了報錯，沒有把代碼完全運行完，但是已經獲得和NK.markers這個表達量文件，部分內容如下

image.png

我們用這個表達量文件先做一個簡單的熱圖

讀入數據

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

最簡單的熱圖

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

image.png

我們可以看到上圖右側所有的基因名都顯示出來了，如果我們想只顯示自己感興趣的，那該如何實現呢？可以用開頭提到的自定義函數

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)
}

將以上函數放到文本文件里，通過source()加載這個函數

source("useful_R_function/add_flag.r")

選擇感興趣的基因名，我這里就隨機選取幾個了

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

畫圖

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)

結果就變成了如下

image.png

接下來是簡單的美化

代碼

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()

image.png

這個圖和開頭提到的論文里的Figure3f就有幾分相似了，但是還沒有添加分組信息

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