跟着iMeta学做图｜circlize绘制环状热图展示细菌功能聚类分析

2024-02-03 23:40
文章标签 功能 展示 绘制 跟着 聚类分析 环状 imeta 热图 细菌 学做 circlize

本文主要是介绍跟着iMeta学做图｜circlize绘制环状热图展示细菌功能聚类分析，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

原始教程链接 

https://github.com/iMetaScience/iMetaPlot/tree/main/221116circlize

如果你使用本代码，请引用：

Jiao Xi et al. 2022. Microbial community roles and chemical mechanisms in the parasitic development of Orobanche cumana. iMeta https://doi.org/10.1002/imt2.31

写在前面

热图 (Heat map) 可以在微生物组研究中展示展示细菌功能聚类分析的结果，而环状热图是热图的一种表现形式。本期我们挑选2022年6月13日刊登在iMeta上的Microbial community roles and chemical mechanisms in the parasitic development of Orobanche cumana- iMeta | 西农林雁冰/ James M. Tiedje等揭示菌群对寄生植物列当的调控作用，选择文章的Figure 2B进行复现，基于顾祖光博士开发的circlize包，讲解和探讨环形热图的绘制方法，先上原图：

939bea14a50be39019d918a1cc326004.gif

8c564ee0d902770ff9bfb2f0a7c92de5.png

接下来，我们将通过详尽的代码逐步拆解原图，最终实现对原图的复现。

R包检测和安装

01

安装核心R包circlize以及一些功能辅助性R包，并载入所有R包。

# 检查开发者工具devtools，如没有则安装
if (!require("devtools"))install.packages("devtools")
# 加载开发者工具devtools
library(devtools)
# 检查circlize包，没有则通过github安装最新版
if (!require("circlize"))install_github("jokergoo/circlize")
if (!require("tidyverse"))install.packages('tidyverse') 
if (!require("ComplexHeatmap"))install.packages('ComplexHeatmap') 
if (!require("tidyverse"))install.packages('tidyverse') 
# 加载包
library(circlize)
library(tidyverse)
library(ComplexHeatmap)
library(gridBase)

生成测试数据

02

由于没有在补充文件里找到原文相关数据，在这里我们通过生成随机数据来替代。

#生成KEGG数据矩阵（矩阵1）
data1<-matrix(rnorm(670,mean=0.5),nrow=67)
rownames(data1)<-c("K01446","K01971","K01142","K01151","K01246","K00784","K02031","K01644","K02037","K02065","K01448","K01890","K00266","K01725","K00806","K00231","K01737","K00858","K00019","K01715","K01692","K00249","K00023","K00626","K00101","K00803","K01710","K01791","K01176","K00799","K00800","K01667","K01668","K01712","K00053","K01696","K01697","K00108","K00639","K01489","K00226","K01488","K02339","K01428","K01438","K02124","K02275","K01796","K00632","K00648","K00849","K01805","K01685","K00065","K00090","K01619","K01834","K00121","K02182","K02082","K02005","K01266","K01990","K01463","K02217","K01174","K02003")
colnames(data1)<-c(paste('H',seq(1:5),sep = ""),paste("PS",seq(1:5),sep = ""))
#生成EC数据矩阵（矩阵2）
data2<-matrix(rnorm(130,mean=1),nrow = 13)
rownames(data2)<-c("2.1.1.165","1.1.5.-","4.2.99.20","1.1.1.86","1.1.99.1","1.1.2.3","4.2.1.28","4.2.1.82","5.4.1.3","1.13.12.16","5.1.99.4","1.17.4.1","1.8.4.-")
colnames(data2)<-c(paste('H',seq(1:5),sep = ""),paste("PS",seq(1:5),sep = ""))
#生成细菌数据矩阵（矩阵3）
data3<-matrix(rnorm(20,mean=1),nrow = 10)
supdata<-matrix(0,nrow = 10,ncol = 8)
#由于该矩阵为10×2矩阵，需补充10×8全为0的矩阵，使得矩阵123均为m×10的矩阵
data3<-cbind(data3,supdata)
rownames(data3)<-c("Proteobacteria","Actinobacteria","Acidobacteria","Bacteroidetes","Gemmatimonadetes","Chloroflexi","Planctomycetes","Firmicutes","Verrucomicrobia","unidentified_Acidobacteria")
#将三个矩阵按行合并
mat_data<-rbind(data1,data2,data3)
#按行将矩阵反转，这样矩阵3的非零数据会出现在内圈
mat_dataR<-mat_data%>% as.data.frame() %>% rowwise() %>% rev() %>% as.matrix() 
rownames(mat_dataR)<-rownames(mat_data)

6cbed2cca8fbcec940cdd5ed9bd68a9e.png

‍

环形热图预览

03

开始作图，首先画一个最基本的环形热图：

pdf("plot1.pdf",width = 8, height = 6)
#设置热图颜色范围：
colpattern = colorRamp2(c(-1, 0, 1), c("#2574AA", "white", "#ED7B79"))
#设置扇区，这里划分了三个扇区，KEGG,EC和细菌种类。
level_test<-c(rep("KEGG",67),rep("EC",13),rep("SP",10)) %>% factor()#画图
circos.heatmap(mat_dataR, col = colpattern, rownames.side = "outside", cluster = TRUE)
circos.clear()dev.off()

935ed5cd0d448eb356f592d4ddabb600.png

04

添加扇区分化，单元格边框，轨道高度，扇区间间隔：

pdf("plot2.pdf",width = 8, height = 6)
circos.par(gap.after = c(10, 10, 12))
circos.heatmap(mat_dataR, split = level_test, col = colpattern, rownames.side = "outside", cluster = TRUE,cell.lwd=0.8,cell.border="white",track.height = 0.2)
circos.clear()dev.off()

16e6d132267b54781c860773c86b2bd0.png

05

添加矩阵的列名。circos.heatmap()不直接支持矩阵的列名，可以通过自定义panel.fun函数轻松添加：

pdf("plot3.pdf",width = 8, height = 6)
circos.par(gap.after = c(10, 10, 12))
circos.heatmap(mat_dataR, split = level_test, col = colpattern, rownames.side = "outside", cluster = TRUE,cell.lwd=0.8,cell.border="white",track.height = 0.2) 
circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 1) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 3) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)
circos.clear()dev.off()

24f54bb23849419100e586d834eb0d93.png

06

接下来添加连接线，连接线表示位置和位置的对应关系。首先生成数据：

#灰色连接线数据
df_link = data.frame(from_index = sample(nrow(mat_dataR), 30),to_index = sample(nrow(mat_dataR), 30)
)
#红色连接线数据
red_df_link<-data.frame(from_index = c(86,87,82),to_index = c(2,15,36))
#蓝色连接线数据
blue_df_link<-data.frame(from_index = c(84,86,90),to_index = c(72,76,69))

5bed0fb9a9e2a75c7ccdc92953c7bbfa.png

07

接下来开始添加link：

pdf("plot4.pdf",width = 8, height = 6)
circos.par(gap.after = c(10, 10, 12))
circos.heatmap(mat_dataR, split = level_test, col = colpattern, rownames.side = "outside", cluster = TRUE,cell.lwd=0.8,cell.border="white",track.height = 0.2) 
circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 1) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 3) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)for(i in seq_len(nrow(df_link))) {circos.heatmap.link(df_link$from_index[i],df_link$to_index[i],col = "grey")
}for(i in seq_len(nrow(red_df_link))) {circos.heatmap.link(red_df_link$from_index[i],red_df_link$to_index[i],col = "red")
}for(i in seq_len(nrow(blue_df_link))) {circos.heatmap.link(blue_df_link$from_index[i],blue_df_link$to_index[i],col = "blue")
}
circos.clear()dev.off()

13bd8f48fe8ee022b4a586671417a515.png

08

添加图例，circos.heatmap()本身是不支持添加图例的，但我们可以利用gridBase和ComplexHeatmap包添加图例：

pdf("plot5.pdf",width = 8, height = 6)
plot.new()
circle_size = unit(1, "snpc") # snpc unit gives you a square regionpushViewport(viewport(x = 0, y = 0.5, width = circle_size, height = circle_size,just = c("left", "center")))
par(omi = gridOMI(), new = TRUE)
circos.par(gap.after = c(10, 10, 12))
circos.heatmap(mat_dataR, split = level_test, col = colpattern, rownames.side = "outside", cluster = TRUE,cell.lwd=0.8,cell.border="white",track.height = 0.2) 
circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 1) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 3) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)for(i in seq_len(nrow(df_link))) {circos.heatmap.link(df_link$from_index[i],df_link$to_index[i],col = "grey")
}for(i in seq_len(nrow(red_df_link))) {circos.heatmap.link(red_df_link$from_index[i],red_df_link$to_index[i],col = "red")
}for(i in seq_len(nrow(blue_df_link))) {circos.heatmap.link(blue_df_link$from_index[i],blue_df_link$to_index[i],col = "blue")
}
circos.clear()
upViewport()h = dev.size()[2]
lgd = Legend(title = "", col_fun = colpattern)
draw(lgd, x = circle_size, just = "left")dev.off()

49334a7f3203152529d925d27b46e01d.png

09

距离成功只差一步啦，最后用AI进行修图，处理掉不合理的部分。成品图如下：

c3044785366a61daa8ffd47802140514.jpeg

完整代码

、

# 检查开发者工具devtools，如没有则安装
if (!require("devtools"))install.packages("devtools")
# 加载开发者工具devtools
library(devtools)
# 检查circlize包，没有则通过github安装最新版
if (!require("circlize"))install_github("jokergoo/circlize")
if (!require("tidyverse"))install.packages('tidyverse') 
if (!require("ComplexHeatmap"))install.packages('ComplexHeatmap') 
if (!require("tidyverse"))install.packages('tidyverse') 
# 加载包
library(circlize)
library(tidyverse)
library(ComplexHeatmap)
library(gridBase)#part1 生成数据
set.seed(123)
#生成KEGG数据矩阵（矩阵1）
data1<-matrix(rnorm(670,mean=0.5),nrow=67)
rownames(data1)<-c("K01446","K01971","K01142","K01151","K01246","K00784","K02031","K01644","K02037","K02065","K01448","K01890","K00266","K01725","K00806","K00231","K01737","K00858","K00019","K01715","K01692","K00249","K00023","K00626","K00101","K00803","K01710","K01791","K01176","K00799","K00800","K01667","K01668","K01712","K00053","K01696","K01697","K00108","K00639","K01489","K00226","K01488","K02339","K01428","K01438","K02124","K02275","K01796","K00632","K00648","K00849","K01805","K01685","K00065","K00090","K01619","K01834","K00121","K02182","K02082","K02005","K01266","K01990","K01463","K02217","K01174","K02003")
colnames(data1)<-c(paste('H',seq(1:5),sep = ""),paste("PS",seq(1:5),sep = ""))
#生成EC数据矩阵（矩阵2）
data2<-matrix(rnorm(130,mean=1),nrow = 13)
rownames(data2)<-c("2.1.1.165","1.1.5.-","4.2.99.20","1.1.1.86","1.1.99.1","1.1.2.3","4.2.1.28","4.2.1.82","5.4.1.3","1.13.12.16","5.1.99.4","1.17.4.1","1.8.4.-")
colnames(data2)<-c(paste('H',seq(1:5),sep = ""),paste("PS",seq(1:5),sep = ""))
#生成细菌数据矩阵（矩阵3）
data3<-matrix(rnorm(20,mean=1),nrow = 10)
supdata<-matrix(0,nrow = 10,ncol = 8)
#由于该矩阵为10×2矩阵，需补充10×8全为0的矩阵，使得矩阵123均为m×10的矩阵
data3<-cbind(data3,supdata)
rownames(data3)<-c("Proteobacteria","Actinobacteria","Acidobacteria","Bacteroidetes","Gemmatimonadetes","Chloroflexi","Planctomycetes","Firmicutes","Verrucomicrobia","unidentified_Acidobacteria")
#将三个矩阵按行合并
mat_data<-rbind(data1,data2,data3)
#按行将矩阵反转，这样矩阵3的非零数据会出现在内圈
mat_dataR<-mat_data%>% as.data.frame() %>% rowwise() %>% rev() %>% as.matrix() 
rownames(mat_dataR)<-rownames(mat_data)
#设置热图颜色范围
colpattern = colorRamp2(c(-1, 0, 1), c("#2574AA", "white", "#ED7B79"))
#设置扇区，这里划分了三个扇区，KEGG,EC和细菌种类
level_test<-c(rep("KEGG",67),rep("EC",13),rep("SP",10)) %>% factor()
#level_test<-row.names(mat_dataR) %>% str_match("[k]+") %>% replace_na("EC") %>% factor()
#生成连接线数据
df_link = data.frame(from_index = sample(nrow(mat_dataR), 30),to_index = sample(nrow(mat_dataR), 30)
)
red_df_link<-data.frame(from_index = c(86,87,82),to_index = c(2,15,36))blue_df_link<-data.frame(from_index = c(84,86,90),to_index = c(72,76,69))#开始画图
pdf("Figure 2B.pdf",width = 8, height = 6)
plot.new()
circle_size = unit(1, "snpc") # snpc unit gives you a square regionpushViewport(viewport(x = 0, y = 0.5, width = circle_size, height = circle_size,just = c("left", "center")))
par(omi = gridOMI(), new = TRUE)
circos.par(gap.after = c(10, 10, 12))
circos.heatmap(mat_dataR, split = level_test, col = colpattern, rownames.side = "outside", cluster = TRUE,cell.lwd=0.8,cell.border="white",track.height = 0.2) 
circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 1) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)circos.track(track.index = get.current.track.index(), panel.fun = function(x, y) {if(CELL_META$sector.numeric.index == 3) { # the last sectorcn = colnames(mat_dataR)n = length(cn)circos.text(rep(CELL_META$cell.xlim[2], n) + convert_x(1, "mm"), 1:n - 0.5, cn, cex = 0.3, adj = c(0, 0.5), facing = "inside")}
}, bg.border = NA)for(i in seq_len(nrow(df_link))) {circos.heatmap.link(df_link$from_index[i],df_link$to_index[i],col = "grey")
}for(i in seq_len(nrow(red_df_link))) {circos.heatmap.link(red_df_link$from_index[i],red_df_link$to_index[i],col = "red")
}for(i in seq_len(nrow(blue_df_link))) {circos.heatmap.link(blue_df_link$from_index[i],blue_df_link$to_index[i],col = "blue")
}
circos.clear()
upViewport()h = dev.size()[2]
lgd = Legend(title = "", col_fun = colpattern)
draw(lgd, x = circle_size, just = "left")dev.off()

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1.前言 在10.0的系统rom定制化开发中，在进行一些平板等默认横屏的设备开发的过程中，需要在进入camera2的 时候，默认预览图像也是需要横屏显示的，在上一篇已经实现了横屏预览功能，然后发现横屏预览后，拍照保存的图片 依然是竖屏的，所以说同样需要将图片也保存为横屏图标了，所以就需要看下mtk的camera2的相关横屏保存图片功能， 如何实现实现横屏保存图片功能 如图所示: 2.mtk
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Spring+MyBatis+jeasyui 功能树列表

Spring+MyBatis+jeasyui 功能树列表

java代码@EnablePaging@RequestMapping(value = "/queryFunctionList.html")@ResponseBodypublic Map<String, Object> queryFunctionList() {String parentId = "";List<FunctionDisplay> tables = query(parent
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PostgreSQL核心功能特性与使用领域及场景分析

PostgreSQL核心功能特性与使用领域及场景分析

PostgreSQL有什么优点？ 开源和免费 PostgreSQL是一个开源的数据库管理系统，可以免费使用和修改。这降低了企业的成本，并为开发者提供了一个活跃的社区和丰富的资源。 高度兼容 PostgreSQL支持多种操作系统（如Linux、Windows、macOS等）和编程语言（如C、C++、Java、Python、Ruby等），并提供了多种接口（如JDBC、ODBC、ADO.NET等
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利用matlab bar函数绘制较为复杂的柱状图，并在图中进行适当标注

利用matlab bar函数绘制较为复杂的柱状图，并在图中进行适当标注

示例代码和结果如下：小疑问：如何自动选择合适的坐标位置对柱状图的数值大小进行标注？😂 clear; close all;x = 1:3;aa=[28.6321521955954 26.2453660695847 21.69102348512086.93747104431360 6.25442246899816 3.342835958564245.51365061796319 4.87
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