# 安装包
if (!requireNamespace("packcircles", quietly = TRUE)) {
install.packages("packcircles")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
install.packages("ggplot2")
}
if (!requireNamespace("dplyr", quietly = TRUE)) {
install.packages("dplyr")
}
if (!requireNamespace("viridis", quietly = TRUE)) {
install.packages("viridis")
}
if (!requireNamespace("ggiraph", quietly = TRUE)) {
install.packages("ggiraph")
}
if (!requireNamespace("htmlwidgets", quietly = TRUE)) {
install.packages("htmlwidgets")
}
if (!requireNamespace("ggraph", quietly = TRUE)) {
install.packages("ggraph")
}
if (!requireNamespace("igraph", quietly = TRUE)) {
install.packages("igraph")
}
if (!requireNamespace("tidyverse", quietly = TRUE)) {
install.packages("tidyverse")
}
if (!requireNamespace("flare", quietly = TRUE)) {
install.packages("flare")
}
if (!requireNamespace("cowplot", quietly = TRUE)) {
install.packages("cowplot")
}
if (!requireNamespace("circlepackeR", quietly = TRUE)) {
remotes::install_github("jeromefroe/circlepackeR")
}
if (!requireNamespace("data.tree", quietly = TRUE)) {
install.packages("data.tree")
}
# 加载包
library(packcircles)
library(ggplot2)
library(dplyr)
library(tidyr)
library(viridis)
library(ggiraph)
library(htmlwidgets)
library(ggraph)
library(igraph)
library(tidyverse)
library(flare)
library(cowplot)
library(circlepackeR)
library(data.tree)包珠图
包珠图（Circular Packing），可视为一种特殊的分类树状图，特别适合展示具有层级关系的分类数据。
示例
环境配置
系统要求： 跨平台（Linux/MacOS/Windows）
编程语言：R
依赖包：
packcircles;ggplot2;dplyr;tidyr;viridis;ggiraph;ggiraph;htmlwidgets;ggraph;igraph;tidyverse;flare;cowplot;circlepackeR;data.tree
数据准备
#GO BP
data_BP <- readr::read_csv("https://bizard-1301043367.cos.ap-guangzhou.myqcloud.com/data_BP.csv")
#flare
data_edges <- flare$edges
data_vertices <- flare$vertices
#KEGG
data_KEGG <- readr::read_csv("https://bizard-1301043367.cos.ap-guangzhou.myqcloud.com/data_KEGG.csv")
#KEGG_type
data_KEGG_type <- readr::read_csv("https://bizard-1301043367.cos.ap-guangzhou.myqcloud.com/data_KEGG_type.csv")
data_KEGG_type$pvalue_log <- -log10(data_KEGG_type$pvalue)
summary(data_KEGG_type) PW ID pvalue NES
Length:33 Length:33 Min. :3.490e-06 Min. :-1.7497
Class :character Class :character 1st Qu.:4.930e-05 1st Qu.:-1.5834
Mode :character Mode :character Median :6.342e-04 Median :-1.5103
Mean :2.093e-03 Mean :-0.7439
3rd Qu.:2.889e-03 3rd Qu.:-1.3939
Max. :8.710e-03 Max. : 2.0829
type subtype pvalue_log
Length:33 Length:33 Min. :2.060
Class :character Class :character 1st Qu.:2.539
Mode :character Mode :character Median :3.198
Mean :3.442
3rd Qu.:4.307
Max. :5.457 data_KEGG_type1 <- data_KEGG_type %>%
dplyr::select(type, subtype, PW, pvalue_log, NES) %>%
arrange(type, subtype)可视化
1. 单级包珠图
1.1 基础包珠图
以小鼠肺组织使用顺铂前后的差异基因对应的GO分析结果数据为例。
##颜色
data_BP1 <- data_BP
data_BP1$pvalue_log <- -log10(data_BP1$pvalue)
packing_BP <- circleProgressiveLayout(data_BP1$pvalue_log, sizetype='area' )
#合并绘图数据
data_BUBBLE_BP <- cbind(data_BP1, packing_BP)
#生成圆的各个顶点坐标，npoint为顶点数
dat.gg_BP <- circleLayoutVertices(packing_BP, npoints=50)
#绘图
p <- ggplot() +
geom_polygon(data = dat.gg_BP,
aes(x, y, group = id, fill=as.factor(id)),
colour = "black", alpha = 0.6) +
scale_fill_manual(values = magma(nrow(data_BUBBLE_BP))) + #修改颜色
geom_text(data = data_BUBBLE_BP,
aes(x, y, size=pvalue_log, label = str_wrap(BP,width = 10)),
show.legend = FALSE) +
scale_size_continuous(range = c(0.5,1.5)) +
theme_void() +
theme(legend.position="none",
plot.title = element_text(hjust=0.5,size = 20)) +
coord_equal() +
ggtitle("基础包珠图+颜色自定义")
p
上图展示了小鼠肺组织使用顺铂前后的差异基因GO分析的Biological Process部分，颜色表示不同的生物过程。
1.2 颜色映射数据大小
以小鼠肺组织使用顺铂前后的差异基因对应的GO分析结果数据为例。
#基础包珠图（ggplot2）----
#绘图----
#返回每个泡泡位置和大小
data_BP1 <- data_BP
data_BP1$pvalue_log <- -log10(data_BP1$pvalue)
packing_BP <- circleProgressiveLayout(data_BP1$pvalue_log, sizetype='area' )
#合并绘图数据
data_BUBBLE_BP <- cbind(data_BP1, packing_BP)
#生成圆的各个顶点坐标，npoint为顶点数
dat.gg_BP <- circleLayoutVertices(packing_BP, npoints=50)
#value映射NES值
dat.gg_BP$NES <- rep(data_BP1$NES, each=51)
#绘图
ggplot() +
geom_polygon(data = dat.gg_BP, aes(x, y, group = id, fill=NES), #颜色表示NES值
colour = "black", alpha = 0.6) +
scale_fill_continuous(type = "viridis")+
#添加标签+控制大小
geom_text(data = data_BUBBLE_BP,
aes(x, y, size=pvalue_log, label = str_wrap(BP,width = 10)),
show.legend = FALSE) +
scale_size_continuous(range = c(0.5,1.5)) +
#主题
theme_void() +
theme(plot.title = element_text(hjust=0.5,size = 20)) +
coord_equal()+
ggtitle("Biological Process")
上图展示了小鼠肺组织使用顺铂前后的差异基因GO分析的Biological Process部分，圆圈大小表示log10（P）的负值，圆圈越大表示结果越可靠。圆圈颜色表示NES值。
1.3 背景自定义
以小鼠肺组织使用顺铂前后的差异基因对应的KNGG通路分析结果数据为例。
##背景自定义
#颜色映射数据值
data_KEGG1 <- data_KEGG
data_KEGG1$pvalue_log <- -log10(data_KEGG1$pvalue)
packing_KEGG <- circleProgressiveLayout(data_KEGG1$pvalue_log, sizetype='area' )
#合并绘图数据
data_BUBBLE_KEGG <- cbind(data_KEGG1, packing_KEGG)
#生成圆的各个顶点坐标，npoint为顶点数
dat.gg_KEGG <- circleLayoutVertices(packing_KEGG, npoints=50)
#value映射NES值
dat.gg_KEGG$NES <- rep(data_KEGG1$NES, each=51)
p3 <- ggplot() +
geom_polygon(data = dat.gg_KEGG, aes(x, y, group = id, fill=NES),
colour = "grey", alpha = 0.6, size=.5) +
scale_fill_distiller(palette = "Spectral", direction = 1 ) +
#添加标签+控制大小
geom_label(data = data_BUBBLE_KEGG,
aes(x, y, size=pvalue_log, label = str_wrap(PW,width = 15)),
show.legend = FALSE) +
scale_size_continuous(range = c(1.5,2)) +
#主题，自定义
theme_void() +
theme(
legend.text = element_text(colour="white", size = 10),
plot.background = element_rect(fill="black"),
plot.title = element_text(color="grey", hjust=0.5,size = 20)
) +
coord_equal() +
ggtitle("自定义背景")
p3
2. 交互单级包珠图
ggiraph包允许我们绘制交互包珠图。
#交互----
#颜色映射数据值
data_KEGG1 <- data_KEGG
data_KEGG1$pvalue_log <- -log10(data_KEGG1$pvalue)
packing_KEGG <- circleProgressiveLayout(data_KEGG1$pvalue_log, sizetype='area' )
#合并绘图数据
data_BUBBLE_KEGG <- cbind(data_KEGG1, packing_KEGG)
#生成圆的各个顶点坐标，npoint为顶点数
dat.gg_KEGG <- circleLayoutVertices(packing_KEGG, npoints=50)
#value映射NES值
dat.gg_KEGG$NES <- rep(data_KEGG1$NES, each=51)
data_BUBBLE_KEGG$text <- paste("Pathway:",data_BUBBLE_KEGG$PW,
"\n", "-log10(P):", data_BUBBLE_KEGG$pvalue_log,
"\n","NES:", data_BUBBLE_KEGG$NES)
p5 <- ggplot() +
geom_polygon_interactive(data = dat.gg_KEGG,
aes(x, y, group = id, fill=NES, tooltip = data_BUBBLE_KEGG$text[id], data_id = id),
colour = "black", alpha = 0.6) +
scale_fill_distiller(palette = "BuPu", direction = 1 ) +
#添加标签+控制大小
geom_text(data = data_BUBBLE_KEGG,
aes(x, y, size=pvalue_log, label = str_wrap(PW,width = 15)),
show.legend = FALSE) +
scale_size_continuous(range = c(1.5,2)) +
#主题
theme_void() +
theme(plot.title = element_text(hjust=0.5,size = 20)) +
coord_equal()+
ggtitle("交互图形")
P_inter <- girafe(ggobj = p5, width_svg = 7, height_svg = 7)
P_inter交互单级包珠图
上图为交互单级包珠图，鼠标悬停处圆圈为橙色，可交互查看通路名称，P值的-log10值以及NES值，同时提供下载功能。
3. 多级别包珠图
这里以补充了通路分类的KNGG通路分析结果数据为例。
绘制多级别包珠图需要将数据整理为顶点（vertices）数据集以及边（edges）数据集，igraph包的graph_from_data_frame()函数可以很好的将包含边列表和边/顶点属性的一个或两个数据帧创建为igraph图，用于绘图。
#点数据集
rstat_nodes_KEGG <- data.frame(name = c("KEGG Pathways",
unique(data_KEGG_type1$type),
unique(data_KEGG_type1$subtype),
unique(data_KEGG_type1$PW)),
pvalue_log = c(rep(0,22),data_KEGG_type1$pvalue_log),
NES = c(rep(0,22),data_KEGG_type1$NES),
label = c("KEGG Pathways",
unique(data_KEGG_type1$type),
unique(data_KEGG_type1$subtype),
unique(data_KEGG_type1$PW)))
#开始创建边数据集
edges1 <- rep("KEGG Pathways", length(unique(data_KEGG_type1$type)))
#创建函数用于批量处理
fcount1 <- function(n){
data1 <- filter(data_KEGG_type1, data_KEGG_type1$type == unique(data_KEGG_type1$type)[n])
count <- length(unique(data1$subtype))
result <- rep(unique(data_KEGG_type1$type)[n], count)
return(result)
}
edges2<- NULL
for(i in seq(1, 6)){
edges2 <- c(edges2,fcount1(i))
#print(edges2)
}
#创建函数用于批量处理
fcount2 <- function(n){
data1 <- filter(data_KEGG_type1, data_KEGG_type1$subtype == unique(data_KEGG_type1$subtype)[n])
count <- length(unique(data1$PW))
result <- rep(unique(data_KEGG_type1$subtype)[n], count)
return(result)
}
edges3<- NULL
for(i in seq(1,15)){
edges3 <- c(edges3,fcount2(i))
#print(edges3)
}
edgesa <- unique(data_KEGG_type1$type)
#创建函数用于批量处理
fcounta <- function(n){
data1 <- filter(data_KEGG_type1, data_KEGG_type1$type == unique(data_KEGG_type1$type)[n])
result <- unique(data1$subtype)
return(result)
}
edgesb<- NULL
for(i in seq(1, 6)){
edgesb <- c(edgesb,fcounta(i))
#print(edgesb)
}
#创建函数用于批量处理
fcountb <- function(n){
data1 <- filter(data_KEGG_type1, data_KEGG_type1$subtype == unique(data_KEGG_type1$subtype)[n])
result <- unique(data1$PW)
return(result)
}
edgesc<- NULL
for(i in seq(1, 15)){
edgesc <- c(edgesc,fcountb(i))
#print(edgesc)
}
#边数据集
rstat_edges_KEGG <- data.frame(from = c(edges1, edges2, edges3),
to = c(edgesa, edgesb, edgesc))
#创建igraph图
mygraph_KEGG <- graph_from_data_frame( rstat_edges_KEGG, vertices=rstat_nodes_KEGG )
# 绘图
ggraph(mygraph_KEGG, layout = 'circlepack', weight = pvalue_log ) + #泡泡大小对应值的大小
geom_node_circle(aes(fill = NES)) +
theme_void()+
geom_node_text( aes(filter=leaf, size=pvalue_log,
label = str_wrap(label,width = 10)),
show.legend = FALSE) +
scale_size_continuous(range = c(1.5,2)) +
scale_fill_viridis(alpha = 0.5) +
ggtitle("KEGG通路多级别分类") +
theme(plot.title = element_text(color="black", hjust=0.5,size = 20))
上图为KEGG通路多级别分类的包珠图，最小级别圆圈的大小表示log10（P）的负值，圆圈越大表示相关程度越大。圆圈颜色表示NES值。
分类依据KEGG官网的通路分类：
一级分类：Metabolism（代谢）、Genetic Information Processing（遗传信息处理）、Environmental Information Processing（环境信息处理）、Cellular Processes（细胞过程）、Organismal Systems（有机系统）、Human Diseases（人类疾病）、Drug Development（药物开发）
二级分类：例如Cellular Processes下的5个二级分类——Transport and catabolism、Cell growth and death、Cellular community – eukaryotes、Cellular community – eukaryotes和Cell motility
4. 交互多级别包珠图
通过circlepackeR包可以实现交互多级别包珠图的绘制。
#分组边框显示
#多层级数据
data <- data.frame(
root=rep("root", 15),
group=c(rep("group A",5), rep("group B",5), rep("group C",5)),
subgroup= rep(letters[1:5], each=3),
subsubgroup=rep(letters[1:3], 5),
value=sample(seq(1:15), 15)
)
#更改数据形式
data$pathString <- paste("world", data$group, data$subgroup, data$subsubgroup, sep = "/")
population <- as.Node(data)
p_inter1 <- circlepackeR(population,
size = "value",
color_min = "hsl(56,80%,80%)",
color_max = "hsl(341,30%,40%)")
p_inter1交互多级别包珠图
上图为可交互多级别包珠图，可实现组别边框显示以及不同级别组别的放大缩小。
以flare数据集为例
#所有级别边框显示
data_edge <- flare$edges
data_edge$from <- gsub(".*\\.","",data_edge$from)
data_edge$to <- gsub(".*\\.","",data_edge$to)
#head(data_edge) # This is an edge list
data_tree <- FromDataFrameNetwork(data_edge)
data_nested <- ToDataFrameTree(data_tree,
level1 = function(x) x$path[2],
level2 = function(x) x$path[3],
level3 = function(x) x$path[4],
level4 = function(x) x$path[5])[-1,-1]
data_nested <- na.omit(data_nested)
#绘图
data_nested$pathString <- paste("roots", data_nested$level1, data_nested$level2, data_nested$level3, data_nested$level4, sep = "/")
data_nested$value=1
data_Node <- as.Node(data_nested)
p_inter2 <- circlepackeR(data_Node, size = "value")
p_inter2以flare数据集为例
上图为可交互多级别包珠图，可实现所有的边框显示以及不同级别组别的放大缩小。
应用场景
有色圆圈代表药物化学空间骨架并标记其骨架的频率。圆圈的面积和颜色与骨架频率有关。在同一群集中的骨架被分组为灰圈。 [1]
参考文献
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