颜值够格的风险森林图

0.输入数据和R包

在生信星球公众号聊天框回复“forest779”即可获得输入数据。也可以自己根据表达矩阵与临床信息生成，如下：

rm(list = ls())
load("cox_dat.Rdata")
str(dat)

## 'data.frame':    516 obs. of  10 variables:
##  $ time  : num  47.867 0.533 39.7 24.5 49.767 ...
##  $ event : num  0 0 1 1 0 0 0 0 0 0 ...
##  $ gender: Factor w/ 2 levels "female","male": 2 1 2 2 2 2 2 1 2 2 ...
##  $ stage : Factor w/ 4 levels "i","ii","iii",..: 3 3 1 1 2 2 1 1 1 1 ...
##  $ age   : num  66 77 57 59 57 67 70 52 51 53 ...
##  $ miR21 : num  16.7 18 18 17.8 18.4 ...
##  $ miR143: num  17 18 17.1 16.5 16.8 ...
##  $ miR10b: num  17.7 17.9 17.5 17.1 17.1 ...
##  $ miR192: num  15.3 11.6 14.4 14.2 14.6 ...
##  $ miR183: num  8.44 11.24 10.53 10.03 8.43 ...

library(survival)
library(survminer)
library(forestplot)
library(stringr)

1.多因素cox回归

建模就一句代码，出森林图也是一句代码。

model <- coxph(Surv(time, event) ~., data = dat )
ggforest(model)

这个虽然是ggplot2对象，但是灰色背景去不掉，也没办法用常规的ggplot2语法去修改颜色，只能是导出去再慢慢调咯。

2.美化版森林图-forestplot

用到一个新的R包，forestplot。

它就没有ggforest那么智能了，森林图展示的内容是需要自己组织的。

2.1学学帮助文档

看看函数的帮助文档，最简单的用法：

row_names <- list(list("test = 1", expression(test >= 2)))
test_data <- data.frame(
  coef = c(1.59, 1.24),
  low = c(1.4, 0.78),
  high = c(1.8, 1.55)
)
forestplot(row_names,
  test_data$coef,
  test_data$low,
  test_data$high,
  zero = 1,
  cex  = 2,
  lineheight = "auto",
  xlab = "Lab axis txt"
)

最核心的信息就是HR值和它的置信区间范围，我们可以从cox模型中提取到图上的这些信息。

2.2组织输入数据

照葫芦画瓢开始，准备添加在图上的label列。

m = summary(model)
colnames(m$coefficients)

## [1] "coef"      "exp(coef)" "se(coef)"  "z"         "Pr(>|z|)"

colnames(m$conf.int)

## [1] "exp(coef)"  "exp(-coef)" "lower .95"  "upper .95"

#p值改一下格式，加上显著性
p = ifelse(
  m$coefficients[, 5] < 0.001,
  "<0.001 ***",
  ifelse(
    m$coefficients[, 5] < 0.01,
    "<0.01  **",
    ifelse(
      m$coefficients[, 5] < 0.05,
      paste(round(m$coefficients[, 5], 3), " *"),
      round(m$coefficients[, 5], 3)
    )
  )
)
#HR和它的置信区间
dat2 = as.data.frame(round(m$conf.int[, c(1, 3, 4)], 2))
dat2 = tibble::rownames_to_column(dat2, var = "Trait")
colnames(dat2)[2:4] = c("HR", "lower", "upper")
#需要在图上显示的HR文字和p值
dat2$HR2 = paste0(dat2[, 2], "(", dat2[, 3], "-", dat2[, 4], ")")
dat2$p = p

str(dat2)

## 'data.frame':    10 obs. of  6 variables:
##  $ Trait: chr  "gendermale" "stageii" "stageiii" "stageiv" ...
##  $ HR   : num  1.08 1.22 2.5 6.39 1.03 1.41 0.86 0.89 0.82 0.95
##  $ lower: num  0.76 0.61 1.6 4.12 1.01 1.14 0.74 0.75 0.76 0.85
##  $ upper: num  1.53 2.46 3.89 9.9 1.05 1.75 0.99 1.05 0.9 1.06
##  $ HR2  : chr  "1.08(0.76-1.53)" "1.22(0.61-2.46)" "2.5(1.6-3.89)" "6.39(4.12-9.9)" ...
##  $ p    : chr  "0.668" "0.574" "<0.001 ***" "<0.001 ***" ...

2.3基础画图

forestplot(
  dat2[, c(1, 4, 6)],
  mean = dat2[, 2],
  lower = dat2[, 3],
  upper = dat2[, 4],
  zero = 1,
  boxsize = 0.4,
  col = fpColors(box = '#1075BB', lines = 'black', zero = 'grey'),
  lty.ci = "solid",
  graph.pos = 2
)

2.4加点细节

主要是分类向量，如这里的性别和分期，需要画出reference，那就需要在输入数据dat2里添加几行。

dat2$Trait = str_remove(dat2$Trait, "gender|stage")

ins = function(x) {
  c(x, rep(NA, ncol(dat2) - 1))
}

dat2 = rbind(
  c("Trait", NA, NA, NA, "HR", "p"),
  ins("gender"),
  ins("female"),
  dat2[1, ],
  ins("stage"),
  ins("i"),
  dat2[2:nrow(dat2), ]
)
for(i in 2:4) {
  dat2[, i] = as.numeric(dat2[, i])
}
str(dat2)

## 'data.frame':    15 obs. of  6 variables:
##  $ Trait: chr  "Trait" "gender" "female" "male" ...
##  $ HR   : num  NA NA NA 1.08 NA NA 1.22 2.5 6.39 1.03 ...
##  $ lower: num  NA NA NA 0.76 NA NA 0.61 1.6 4.12 1.01 ...
##  $ upper: num  NA NA NA 1.53 NA NA 2.46 3.89 9.9 1.05 ...
##  $ HR2  : chr  "HR" NA NA "1.08(0.76-1.53)" ...
##  $ p    : chr  "p" NA NA "0.668" ...

重新画图

forestplot(
  dat2[, c(1, 5, 6)],
  mean = dat2[, 2],
  lower = dat2[, 3],
  upper = dat2[, 4],
  zero = 1,
  boxsize = 0.4,
  col = fpColors(box = '#1075BB', lines = 'black', zero = 'grey'),
  lty.ci = "solid",
  graph.pos = 2,
  #xticks = F,
  is.summary = c(T, T, F, F, T, rep(F, 10)),
  align = "l",
  hrzl_lines = list(
    "1" = gpar(lty=1),
    "2" = gpar(lty=1),
    "16"= gpar(lty=1)),
  colgap = unit(5, 'mm')
)

差不多啦！可以。