用R语言的circlize包复现一下Microbiome期刊中的圈图

library(circlize)
library(readxl)
library(tidyverse)
library("RColorBrewer")


dat01<-read_excel("2024.data/20240610/data01.xlsx")
dat01 %>% 
  mutate(phylum=factor(phylum,levels = phylum)) -> dat01

dat02<-read_excel("2024.data/20240610/data02.xlsx")
dat02

dat03<-read_excel("2024.data/20240610/data03.xlsx")
dat03

dat04<-read_excel("2024.data/20240610/data04.xlsx")
dat04

dat05<-read_excel("2024.data/20240610/data05.xlsx")
dat05

dat06.mat<-dat03 %>% pull(y) %>% as.matrix(ncol=1)
dat06.mat

data07<-read_excel("2024.data/20240610/data07.xlsx")
data07

brk<-seq(0,30,2)
brk
circos.par(start.degree =86,clock.wise = T)
## 热图的圈参考链接 https://jokergoo.github.io/circlize_book/book/circos-heatmap.html
circos.heatmap.initialize(dat06.mat, 
                          split = factor(dat06$phylum,
                                         levels = dat06$phylum %>% unique()))
circos.initialize(factors = dat01$phylum, 
                  xlim = matrix(c(dat01$start,dat01$numberofgenera),ncol=2))

## 第一圈文字
circos.genomicLabels(dat02,side = "outside",labels.column = 4)

## 第一圈灰色背景
circos.trackPlotRegion(dat01$phylum, 
                       ylim = c(0, 10),
                       track.height = 0.05,
                       bg.border = NA, 
                       #ylim=CELL_META$ylim,
                       panel.fun = function(x, y) {
                         circos.text(mean(CELL_META$xlim),mean(CELL_META$ylim), 
                                     get.cell.meta.data("sector.index"))
                       },
                       bg.col='#EEEEEE6E')
## 第一圈线段
for (chromosome in dat02$phylum){
  circos.segments(sector.index = chromosome,
                  x0=dat02[dat02$phylum==chromosome,]$start,
                  x1=dat02[dat02$phylum==chromosome,]$end,
                  y0=-2,y1=12,col="red")
}

## 第一圈刻度
for (chromosome in dat01$phylum){
  circos.axis(sector.index = chromosome,
              h = 12,
              major.at = brk,
              minor.ticks = 0,
              labels = FALSE)
}

## 第二圈
circos.trackPlotRegion(dat01$phylum, 
                       ylim = c(0, 100),
                       track.height = 0.1,
                       bg.col = '#EEEEEE6E', 
                       bg.border = "black")

for (chromosome in dat03$phylum){
  circos.points(sector.index = chromosome,
                x=dat03[dat03$phylum==chromosome,]$x,
                y=dat03[dat03$phylum==chromosome,]$y,
                col="red",
                pch=19)
}

## 第三圈

circos.trackPlotRegion(dat01$phylum, 
                       ylim = c(0, 100),
                       track.height = 0.1,
                       bg.col = '#EEEEEE6E', 
                       bg.border = NA)

for (chromosome in dat04$phylum){
  circos.lines(sector.index = chromosome,
               x=dat04[dat04$phylum==chromosome,]$x,
               y=dat04[dat04$phylum==chromosome,]$y,
               col="#559a90",
               area=TRUE,
               border=NA,
               type="I")
  
}

## 第四圈
circos.trackPlotRegion(dat01$phylum, 
                       ylim = c(0, 100),
                       track.height = 0.05,
                       bg.col = '#EEEEEE6E', 
                       bg.border = "black")

for (chromosome in dat05$phylum){
  circos.points(sector.index = chromosome,
                x=dat05[dat05$phylum==chromosome,]$x,
                y=dat05[dat05$phylum==chromosome,]$y,
                col="red",
                pch=dat05[dat05$phylum==chromosome,]$shape)
}

## 第五圈

circos.heatmap(dat06.mat,
               col = colorRamp2(breaks = c(0, 50, 100), 
                                col = brewer.pal(n = 3, name = "RdBu")),
               track.height=0.1)


## 最内圈的连线

for (i in 1:nrow(data07)){
  circos.link(sector.index1=data07$phylum01[i],
              point1=data07$x01[i],
              sector.index2=data07$phylum02[i], 
              point2=data07$x02[i])
}


circos.clear()