缘由
生信之路，道阻且长，记录经验，但渡有缘人。
碎碎念
及时当勉励，岁月不饶人 ——陶渊明
原图 & 文章
Liao C, Glodowski CR, Fan C, Liu J, Mott KR, Kaushik A, Vu H, Locasale JW, McBrayer SK, DeBerardinis RJ, Perou CM, Zhang Q. Integrated Metabolic Profiling and Transcriptional Analysis Reveals Therapeutic Modalities for Targeting Rapidly Proliferating Breast Cancers. Cancer Res. 2022 Feb 15;82(4):665-680. doi: 10.1158/0008-5472.CAN-21-2745. PMID: 34911787; PMCID: PMC8857046.
功能：同时展示了差异丰度图和聚类信息。
炫图
注意：top部分的线段不会随图片大小改变，需要手动调整，该部分使用AI画更方便。
1.箭头部分的透明色不会
2.坐标轴颜色调整不会
代码
library(ggplot2)
library(grid)
library(ggh4x)
library(aplot)
set.seed(123)生成数据
pathway_ids <- 1:20
pathway_names <- paste("Pathway", pathway_ids)
score <- runif(20,min=-1,max=1)
cluster1 <- sample(1:10,20, replace =TRUE)
cluster2 <- sample(1:10,20, replace =TRUE)
dected <- sample(1:15,20, replace =TRUE)
pathway_category <- sample(c("Metabolism","Signal Transduction","Cell Cycle","Gene Expression"),
20, replace =TRUE)
draw <- data.frame(
Pathway_ID = pathway_ids,
Pathway_Name = pathway_names,
score = score,
Cluster1 = cluster1,
Cluster2 = cluster2,
Dected = dected,
Pathway_Category = pathway_category
)
draw$type <- ifelse(draw$score >0,"up","down")
draw$Pathway_Category <- factor(draw$Pathway_Category)
draw <- draw %>% reshape::melt(c("Pathway_ID","Pathway_Name","score","Pathway_Category","type"))%>%
mutate(x = case_when(
variable =="Cluster1"~-1,
variable =="Cluster2"~0,
variable =="Dected"~1
))
draw$Pathway_Name <- factor(draw$Pathway_Name,
levels = unique(draw$Pathway_Name[order(draw$score, decreasing =TRUE)]))图形1：文本标注
p1 <- ggplot(data = draw, aes(score, Pathway_Name))+
geom_text(aes(label = Pathway_Name, color = Pathway_Category), x =1, vjust =0, hjust =1)+
scale_colour_brewer(palette ="Dark2", guide ="stringlegend")+
theme_minimal()+ theme(
plot.margin = margin(2,1,2,0,"cm"),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.grid = element_blank()
)+
guides(position ="none")+ labs(x =NULL, y =NULL)+
coord_cartesian(clip ="off")+
annotation_custom(
grob = grid::segmentsGrob(
y0 = unit(1.05,"native"),
y1 = unit(1.05,"native"),
arrow = arrow(angle =90,length= unit(.2,"cm"), ends ="both"),
gp = grid::gpar(lwd =3, col ="black")
),
xmin =0.5, xmax =1
)+
annotation_custom(
grob = grid::textGrob(
label ="Metabolite Pathways",
gp = grid::gpar(col ="black", fontsize =8),
x =0.75, y =1.05*1.025
)
)• 文本绘制： • 每行显示 Pathway_Name，用颜色区分Pathway_Category。• 注释： • 使用 annotation_custom添加箭头和文本，标明上下方向。
图形2：柱状图
p2 <- ggplot(data = draw %>% distinct(Pathway_Name, .keep_all =TRUE),
aes(score, Pathway_Name))+
geom_col(aes(fill = type))+
scale_fill_manual(values =c(down ="#00bfc4", up ="#f8766d"))+
labs(x =NULL, y =NULL)+
coord_cartesian(clip ="off")+
annotation_custom(
grob = grid::segmentsGrob(
y0 = unit(1.05,"native"),
y1 = unit(1.05,"native"),
arrow = arrow(angle =45,length= unit(.2,"cm"), ends ="first"),
gp = grid::gpar(lwd =3, col ="#00bfc4")
),
xmin =0, xmax =-1
)+
annotation_custom(
grob = grid::textGrob(
label ="Cluster1",
gp = grid::gpar(col ="#00bfc4", fontsize =8, face ="bold"),
x =0.25, y =1.05*1.025
)
)+ theme_bw()+
theme(
panel.border = element_rect(color ="black", linewidth =0.5),
panel.grid = element_blank(),
axis.text.y = element_blank(),
legend.position ="none"
)• 柱状图绘制： • 按照 score绘制柱状图，颜色区分up和down。
图形3：多变量文本
p3 <- ggplot(draw, aes(x, Pathway_Name))+
geom_text(aes(label = value, color = variable))+
labs(x =NULL, y =NULL)+
scale_x_continuous(
position ="top", breaks =c(-1,0,1), labels =c("Detected","Cluster1","Cluster2")
)+
scale_color_manual(values =c("black","#0186fe","red"))+
theme_bw()+
theme(
axis.text.x = element_text(angle =60, size =8, face ="bold"),
panel.grid = element_blank(),
axis.ticks = element_blank()
)• 显示多变量值： • 使用文本显示 Cluster1、Cluster2和Detected的数值。
拼接图表
p4 <- p1 %>%
insert_right(p2, width = 1.2) %>%
insert_right(p3, width = 1.2)
print(p4)• 使用 aplot将p1、p2和p3水平拼接成综合图。
此代码生成了一组直观的图形，结合文本、柱状图和多变量展示，为复杂数据提供了直观的表达方式。
ALL
#!/usr/env/ Rscript
library(ggplot2)
library(grid)
library(ggh4x)
library(aplot)
set.seed(123)
# 定义Pathway_ID和Pathway_Name
pathway_ids <- 1:20
pathway_names <- paste("Pathway", pathway_ids)
# 生成score（在-1到1之间）
score <- runif(20,min=-1,max=1)
# 生成cluster1, cluster2, dected（正整数）
cluster1 <- sample(1:10,20, replace =TRUE)
cluster2 <- sample(1:10,20, replace =TRUE)
dected <- sample(1:15,20, replace =TRUE)
# 生成pathway category列（随机选择几类）
pathway_category <- sample(c("Metabolism","Signal Transduction","Cell Cycle","Gene Expression"),
20, replace =TRUE)
# 创建数据框
draw <- data.frame(
Pathway_ID = pathway_ids,
Pathway_Name = pathway_names,
score = score,
Cluster1 = cluster1,
Cluster2 = cluster2,
Dected = dected,
Pathway_Category = pathway_category
)
draw$type <- ifelse(draw$score>0,"up","down")
draw$Pathway_Category <- factor(draw$Pathway_Category)
draw <- draw %>% reshape::melt(c("Pathway_ID","Pathway_Name","score","Pathway_Category","type"))%>%
mutate(x=case_when(variable=="Cluster1"~-1,
variable=="Cluster2"~0,
variable=="Dected"~1))
draw$Pathway_Name <- factor(draw$Pathway_Name,
levels = unique(draw$Pathway_Name[order(draw$score,decreasing =T)]))
##part1
p1 <- ggplot(data=draw, aes(score,Pathway_Name))+
geom_text(aes(label=Pathway_Name, color=Pathway_Category), x=1, vjust=0, hjust=1)+
scale_colour_brewer(palette ="Dark2", guide ="stringlegend")+
theme_minimal()+theme(
plot.margin = margin(2,1,2,0,"cm"),# 增加外部边距,
axis.text.x=element_blank(),
axis.text.y=element_blank(),
panel.grid = element_blank(),
)+
guides(postion="none")+labs(x=NULL, y=NULL)+
coord_cartesian(clip ="off")+
# annotation_custom(
# grob = grid::segmentsGrob(
# x0 = unit(c(0, 0, 0.2), "native"),
# x1 = unit(c(0.2, 0, 0.2), "native"),
# y0 = unit(c(1.05, 1.05, 1.05), "native"),
# y1 = unit(c(1.05, 1, 1), "native"),
# gp = gpar(col = "black", lwd = 2)
# )
# ) +
annotation_custom(
grob = grid::segmentsGrob(
y0 = unit(1.05,"native"), # 起点的 y 坐标
y1 = unit(1.05,"native"), # 终点的 y 坐标
arrow = arrow(
angle =90,length= unit(.2,"cm"), ends ="both"),
gp = grid::gpar(lwd =3, col ="black")
),
xmin =0.5, xmax =1,
)+
annotation_custom(
grob = grid::textGrob(
label ="Metabolite Pathways",
gp = grid::gpar(col ="black", fontsize =8),
x=0.75, y=1.05*1.025)
)
p2 <- ggplot(data=draw %>% distinct(Pathway_Name, .keep_all =T),
aes(score,Pathway_Name))+geom_col(aes(fill=type))+
scale_fill_manual(values=c(down="#00bfc4", up="#f8766d"))+labs(x=NULL, y=NULL)+
coord_cartesian(clip ="off")+ annotation_custom(
grob = grid::segmentsGrob(
y0 = unit(1.05,"native"), # 起点的 y 坐标
y1 = unit(1.05,"native"), # 终点的 y 坐标
arrow = arrow(
angle =45,length= unit(.2,"cm"), ends ="first"), # 箭头出现在起点),
gp = grid::gpar(lwd =3, col ="#00bfc4")
),
xmin =0, xmax =-1
)+
annotation_custom(
grob = grid::segmentsGrob(
y0 = unit(1.05,"native"), # 起点的 y 坐标
y1 = unit(1.05,"native"),
arrow = arrow(
angle =45,length= unit(.2,"cm"), ends ="last" # 箭头出现在起点
),
gp = grid::gpar(lwd =3, col ="#f8766d")
),
xmin =0, xmax =1,# 终点的 y 坐标
)+
annotation_custom(
grob = grid::textGrob(
label ="Cluster1",
gp = grid::gpar(col ="#00bfc4", fontsize =8,face="bold"),
x=0.25, y=1.05*1.025
)
)+ annotation_custom(
grob = grid::textGrob(
label ="Cluster2",
gp = grid::gpar(col ="#f8766d", fontsize =8,face="bold"),
x=0.75, y=1.05*1.025,
)
)+theme_bw()+theme( panel.border = element_rect(color="black", linewidth =0.5),
panel.grid = element_blank(),
axis.ticks.x=element_blank(),
axis.text.x=element_text(face="bold"),
axis.text.y = element_blank(),
legend.position ="none"
)+ theme(plot.margin = margin(2,1,2,0,"cm"))+
guides(postion="none")
p3 <- ggplot(draw, aes(x, Pathway_Name))+
geom_text(aes(label = value, color = variable))+labs(x=NULL, y=NULL)+xlim(-1,1.5)+
scale_x_continuous(
position ="top", breaks =c(-1,0,1), labels =c("Detected","Cluster1","Cluster2")
)+
scale_color_manual(values =c("black","#0186fe","red"))+
theme_bw()+theme(
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.line.x = element_line(color ="black"),# Adds red line to the top axis
axis.text.x = element_text(angle=60, hjust=-0,vjust=0, size=8, face ="bold"),
axis.text.y = element_blank(),
#plot.margin = margin(0.5, -1, 1, 1, "cm"),
legend.position ="none"
)+ geom_hline(yintercept =0.45, color ="black", size =0.5)
#拼图
p4 <- p1 %>%
insert_right(p2, width =1.2)%>%# 将 p2 放在 p1 的右边
insert_right(p3, width =1.2) # 将 p3 放在右边（再插入一次）
print(p4)
