在简书 土豆学生信 分享的内容看到这篇论文 简书的链接是 https://www.jianshu.com/p/bbf9cb13b41a
论文是
image.png
论文对应的代码是公开的 https://github.com/ajwilk/2020_Wilk_COVID
image.png
在学习他这个代码的时候发现其中自定义了一个函数可以操作热图的文字标签，可以让热图上只显示我们感兴趣的文字标签。
我在运行这个代码的时候遇到了报错，没有把代码完全运行完，但是已经获得和
NK.markers这个表达量文件，部分内容如下
image.png
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()
image.png
这个图和开头提到的论文里的Figure3f就有几分相似了，但是还没有添加分组信息
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