高分Panel复现系列｜三元突变比例图：从三组比例到三角坐标映射
三元图适合展示一个事件在三个类别之间的组成倾向。这里每个点代表一个基因或突变事件，位置由 Low Grade Glioma、Primary GBM 和 Secondary GBM 三组比例共同决定。
图片来源
| 项目 | 内容 |
|---|---|
| 文章 | Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor |
| 期刊/年份 | Cell, 2018 |
| 图号 | Figure 1B |
| DOI/链接 | https://doi.org/10.1016/j.cell.2018.09.038 |
这张图用三元坐标把不同基因改变在低级别胶质瘤、原发 GBM、继发 GBM 中的相对富集方向放到同一个三角形里，非常适合做“三组组成比例”的可视化复现。
图片解读
这是一张 ternary plot，三元比例图。
三角形三个顶点分别代表三个类别：
| 顶点 | 含义 |
|---|---|
| 顶部 | Low Grade Glioma |
| 左下角 | Primary GBM |
| 右下角 | Secondary GBM |
每个点代表一个基因改变或分子事件。点越靠近某个顶点，说明它在该类别中的比例越高。点的大小可以映射事件强度、频率或重要性；点的颜色可以映射其中一个类别的比例，例如 Secondary GBM 占比。
输入数据
输入数据需要是长表，每一行代表一个基因或事件，三组比例相加最好为 100。
| 列名 | 含义 |
|---|---|
alteration | 基因或事件名称 |
low_grade_glioma | Low Grade Glioma 中的比例 |
primary_gbm | Primary GBM 中的比例 |
secondary_gbm | Secondary GBM 中的比例 |
score | 点大小映射值 |
label_dx | 标签横向微调 |
label_dy | 标签纵向微调 |
library(tidyverse)dat<-read_csv("input_data.csv",show_col_types=FALSE)需要示例数据的后台添加小编领取，调整好数据结构，以下代码可以直接复制粘贴运行。
第一步：把三组比例转换成三角坐标
三元图本质上是把三个比例映射到一个等边三角形中。这里不依赖额外三元图包，直接用坐标变换完成。
tri_h<-sqrt(3)/2to_xy<-function(low_grade_glioma,primary_gbm,secondary_gbm){total<-low_grade_glioma+primary_gbm+secondary_gbm tibble(x=(secondary_gbm+0.5*low_grade_glioma)/total,y=tri_h*low_grade_glioma/total)}point_df<-bind_cols(dat,to_xy(dat$low_grade_glioma,dat$primary_gbm,dat$secondary_gbm))|>mutate(lab_x=x+label_dx,lab_y=y+label_dy)第二步：绘制三角边框和内部网格
三元图的阅读依赖网格线。这里分别绘制三个方向的 20、40、60、80 辅助线。
triangle<-tibble(x=c(0,1,0.5,0),y=c(0,0,tri_h,0))make_seg<-function(type,value,start,end){tibble(type=type,value=value,x=start$x,y=start$y,xend=end$x,yend=end$y)}grid_df<-purrr::map_dfr(seq(20,80,by=20),function(v){bind_rows(make_seg("low",v,to_xy(v,100-v,0),to_xy(v,0,100-v)),make_seg("primary",v,to_xy(100-v,v,0),to_xy(0,v,100-v)),make_seg("secondary",v,to_xy(100-v,0,v),to_xy(0,100-v,v)))})第三步：添加刻度、顶点标签和方向箭头
顶点标签用不同颜色强调三个类别，方向箭头用于提示三组比例变化趋势。
tick_low<-tibble(value=seq(20,80,by=20))tick_low<-bind_cols(tick_low,to_xy(tick_low$value,100-tick_low$value,0))|>mutate(label=as.character(value),angle=-60,x=x-0.045,y=y)tick_primary<-tibble(value=seq(20,100,by=20))tick_primary<-bind_cols(tick_primary,to_xy(0,tick_primary$value,100-tick_primary$value))|>mutate(label=as.character(value),angle=55,x=x-0.018,y=y-0.040)tick_secondary<-tibble(value=seq(20,100,by=20))tick_secondary<-bind_cols(tick_secondary,to_xy(0,100-tick_secondary$value,tick_secondary$value))|>mutate(label=as.character(value),angle=0,x=x,y=y-0.043)axis_lab<-tribble(~label,~x,~y,~col,~hjust,"Low Grade Glioma",0.50,tri_h+.045,"blue",0.5,"Primary\nGBM",-0.085,-0.055,"magenta",0.5,"Secondary\nGBM",1.085,-0.055,"red",0.5)arrow_df<-tribble(~x,~y,~xend,~yend,~col,-0.11,0.46,-0.23,0.30,"#bb33ff",0.50,-0.11,0.72,-0.11,"#e0007a",1.10,0.30,0.94,0.47,"#4050ff",1.10,0.30,1.21,0.17,"#d81b60")第四步：叠加突变点和基因标签
点的位置由三组比例决定，点大小用score控制，颜色用secondary_gbm控制。标签可以通过label_dx和label_dy做局部微调。
p<-ggplot()+geom_segment(data=grid_df,aes(x,y,xend=xend,yend=yend),color="#e6e6e6",linewidth=0.25)+geom_path(data=triangle,aes(x,y),linewidth=0.42,color="black")+geom_text(data=bind_rows(tick_low,tick_primary,tick_secondary),aes(x,y,label=label,angle=angle),size=1.80,color="#3a3a3a")+geom_curve(data=arrow_df,aes(x,y,xend=xend,yend=yend,color=col),curvature=0,linewidth=0.60,arrow=arrow(length=unit(0.055,"inches")),show.legend=FALSE)+geom_point(data=point_df,aes(x,y,size=score,fill=secondary_gbm),shape=21,color="black",stroke=0.32,alpha=0.96)+geom_segment(data=point_df,aes(x,y,xend=lab_x,yend=lab_y),color="#333333",linewidth=0.16,alpha=0.55)+geom_text(data=point_df,aes(lab_x,lab_y,label=alteration),size=2.05,fontface="italic",color="#202020")+geom_text(data=axis_lab,aes(x,y,label=label,color=col,hjust=hjust),size=2.15,lineheight=0.90,show.legend=FALSE)+scale_fill_gradientn(colours=c("#f1d8f8","#7a237d","#cf0017"),limits=c(0,100))+scale_size(range=c(1.30,4.55),guide="none")+scale_color_identity()+coord_equal(xlim=c(-0.23,1.23),ylim=c(-0.13,tri_h+0.08),clip="off")+theme_void()+theme(legend.position="none",plot.background=element_rect(fill="white",color=NA),plot.margin=margin(10,14,12,14))第五步：导出图片
ggsave("ternary_glioma_alteration.png",p,width=3.7,height=3.05,dpi=420,bg="white")ggsave("ternary_glioma_alteration.pdf",p,width=3.7,height=3.05,bg="white")完整代码
library(tidyverse)library(scales)dat<-read_csv("input_data.csv",show_col_types=FALSE)tri_h<-sqrt(3)/2to_xy<-function(low_grade_glioma,primary_gbm,secondary_gbm){total<-low_grade_glioma+primary_gbm+secondary_gbm tibble(x=(secondary_gbm+0.5*low_grade_glioma)/total,y=tri_h*low_grade_glioma/total)}point_df<-bind_cols(dat,to_xy(dat$low_grade_glioma,dat$primary_gbm,dat$secondary_gbm))|>mutate(lab_x=x+label_dx,lab_y=y+label_dy)triangle<-tibble(x=c(0,1,0.5,0),y=c(0,0,tri_h,0))make_seg<-function(type,value,start,end){tibble(type=type,value=value,x=start$x,y=start$y,xend=end$x,yend=end$y)}grid_df<-purrr::map_dfr(seq(20,80,by=20),function(v){bind_rows(make_seg("low",v,to_xy(v,100-v,0),to_xy(v,0,100-v)),make_seg("primary",v,to_xy(100-v,v,0),to_xy(0,v,100-v)),make_seg("secondary",v,to_xy(100-v,0,v),to_xy(0,100-v,v)))})tick_low<-tibble(value=seq(20,80,by=20))tick_low<-bind_cols(tick_low,to_xy(tick_low$value,100-tick_low$value,0))|>mutate(label=as.character(value),angle=-60,x=x-0.045,y=y)tick_primary<-tibble(value=seq(20,100,by=20))tick_primary<-bind_cols(tick_primary,to_xy(0,tick_primary$value,100-tick_primary$value))|>mutate(label=as.character(value),angle=55,x=x-0.018,y=y-0.040)tick_secondary<-tibble(value=seq(20,100,by=20))tick_secondary<-bind_cols(tick_secondary,to_xy(0,100-tick_secondary$value,tick_secondary$value))|>mutate(label=as.character(value),angle=0,x=x,y=y-0.043)axis_lab<-tribble(~label,~x,~y,~col,~hjust,"Low Grade Glioma",0.50,tri_h+.045,"blue",0.5,"Primary\nGBM",-0.085,-0.055,"magenta",0.5,"Secondary\nGBM",1.085,-0.055,"red",0.5)arrow_df<-tribble(~x,~y,~xend,~yend,~col,-0.11,0.46,-0.23,0.30,"#bb33ff",0.50,-0.11,0.72,-0.11,"#e0007a",1.10,0.30,0.94,0.47,"#4050ff",1.10,0.30,1.21,0.17,"#d81b60")p<-ggplot()+geom_segment(data=grid_df,aes(x,y,xend=xend,yend=yend),color="#e6e6e6",linewidth=0.25)+geom_path(data=triangle,aes(x,y),linewidth=0.42,color="black")+geom_text(data=bind_rows(tick_low,tick_primary,tick_secondary),aes(x,y,label=label,angle=angle),size=1.80,color="#3a3a3a")+geom_curve(data=arrow_df,aes(x,y,xend=xend,yend=yend,color=col),curvature=0,linewidth=0.60,arrow=arrow(length=unit(0.055,"inches")),show.legend=FALSE)+geom_point(data=point_df,aes(x,y,size=score,fill=secondary_gbm),shape=21,color="black",stroke=0.32,alpha=0.96)+geom_segment(data=point_df,aes(x,y,xend=lab_x,yend=lab_y),color="#333333",linewidth=0.16,alpha=0.55)+geom_text(data=point_df,aes(lab_x,lab_y,label=alteration),size=2.05,fontface="italic",color="#202020")+geom_text(data=axis_lab,aes(x,y,label=label,color=col,hjust=hjust),size=2.15,lineheight=0.90,show.legend=FALSE)+scale_fill_gradientn(colours=c("#f1d8f8","#7a237d","#cf0017"),limits=c(0,100))+scale_size(range=c(1.30,4.55),guide="none")+scale_color_identity()+coord_equal(xlim=c(-0.23,1.23),ylim=c(-0.13,tri_h+0.08),clip="off")+theme_void()+theme(legend.position="none",plot.background=element_rect(fill="white",color=NA),plot.margin=margin(10,14,12,14))ggsave("ternary_glioma_alteration.png",p,width=3.7,height=3.05,dpi=420,bg="white")ggsave("ternary_glioma_alteration.pdf",p,width=3.7,height=3.05,bg="white")复现结果
参考链接
- DOI: https://doi.org/10.1016/j.cell.2018.09.038
- 文章链接: https://www.cell.com/cell/fulltext/S0092-8674(18)31250-9