跟着Nature Communications学绘图—tree+分支颜色调整+分组注释+热图注释+柱状堆积图注释！！！

跟着Nature Communications学绘图—tree+分支颜色调整+分组注释+热图注释+柱状堆积图注释！！！ by 科研后花园

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绘制代码

设置工作环境并加载R包

rm(list=ls())#clear Global Environmentsetwd('D:/桌面/tree+分支颜色调整+分组注释+热图注释+柱状堆积图注释')#设置工作路径
#加载包library(ggtree) # an R package for visualization of tree and annotation datalibrary(ape) # Analyses of Phylogenetics and Evolutionlibrary(vegan) # Community Ecology Packagelibrary(ggtreeExtra) # An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree"library(ggnewscale) # Multiple Fill and Colour Scales in 'ggplot2'library(ggstar) # Multiple Geometric Shape Point Layer for 'ggplot2'

加载数据并构建进化树

#读取数据OTU <- read.table("OTU.txt",check.names = F, row.names = 1, header = 1, sep = "\t")

#计算距离矩阵df_dist <- vegdist(t(OTU),method = 'bray')#使用bray curtis方法计算距离矩阵####进行层次聚类,可选择方法有single、complete、median、mcquitty、average 、centroid 、ward df_hc <- hclust(df_dist,method="average")#使用类平均法进行聚类
# 将聚类结果转成系统发育格式df_tree <- as.phylo(df_hc)# 对树分组sample <- list(A=c("A_1","A_2","A_3","A_4","A_5","A_6","A_7","A_8"),            B=c("B_1","B_2","B_3","B_4","B_5","B_6","B_7","B_8"),            C=c("C_1","C_2","C_3","C_4","C_5","C_6","C_7","C_8"),            D=c("D_1","D_2","D_3","D_4","D_5","D_6","D_7","D_8"),            E=c("E_1","E_2","E_3","E_4","E_5","E_6","E_7","E_8"),            F=c("F_1","F_2","F_3","F_4","F_5","F_6","F_7","F_8"),            G=c("G_1","G_2","G_3","G_4","G_5","G_6","G_7","G_8"))# 将分组信息和进化树组合到一起tree<-groupOTU(df_tree,sample)
# ggtree初步展示ggtree(tree,size=0.8, layout="circular",              ladderize=FALSE, branch.length="none", aes(col=group),show.legend = F)+  geom_tiplab(aes(color=group),size=4,align = F,              offset = 0.001,show.legend = F)

整理注释所需数据

##读取分组注释信息group <- read.table("sample.txt",check.names = F, row.names = 1, header = 1, sep = "\t")group$sample <- rownames(group)
##计算各样本的物种丰度信息#读取分类信息tax <- read.table("tax.txt",check.names = F, header = 1, sep = "\t")#根据OTUID合并OTU数据和分类信息OTU2 <- OTUOTU2$OTU_id <- rownames(OTU2)df <- merge(tax,OTU2,by="OTU_id")#提取属水平的信息data_Genus <- df[,c(7,9:64)]##利用循环处理具有重复的数据#初始化data<-aggregate(A_1 ~ Genus,data=data_Genus,sum)#重命名colnames(data)[2]<-"example"for (i in colnames(data_Genus)[2:length(colnames(data_Genus))]){  #计算每列的和  data1<-aggregate(data_Genus[,i]~Genus,data=data_Genus,sum)  colnames(data1)[2]<-i    #合并列  data<-merge(data,data1,by="Genus")}df2<-data[,-2]rownames(df2)=df2$Genus#修改行名df3=df2[,-1]#删除多的列
#计算物种总丰度并降序排列df3$rowsum <- apply(df3,1,sum)df4 <- df3[order (df3$rowsum,decreasing=TRUE),]df5 = df4[,-57]#删除求和列#求物种相对丰度df6 <- apply(df5,2,function(x) x/sum(x))#由于之间已经按照每行的和进行过升序排列，所以可以直接取前10行df7 <-  df6[1:10,]df8 <- 1-apply(df7, 2, sum) #计算剩下物种的总丰度#合并数据df9 <- rbind(df7,df8)row.names(df9)[11]="Others"df9 <- as.data.frame(df9)df10 <- as.data.frame(t(df9))df10$sample <- rownames(df10)
##根据样本名称合并分组与丰度信息group <- merge(group,df10,by="sample")
##第二层注释的数据data2 <- group[,c(1,4:11)]data2 <- melt(data2)data2$`Diet (detailed)` <- as.character(data2$variable)data2$value <- as.character(data2$value)
##第六层注释的数据data6 <- group[,c(1,15:25)]data6 <- melt(data6)

group

data2

data6

绘制进化树并逐层添加注释信息

###为进化树添加注释信息ggtree(tree,size=0.8, layout="circular",        ladderize=FALSE, branch.length="none", aes(col=group),show.legend = F)+#图形主体  geom_tiplab(aes(color=group),size=3,align = F,              offset = 0.001,show.legend = F)+#分支标签  scale_color_manual(    values=c("black", "#0ebeff", "#47cf73","#ae63e4",             "#fcd000","#ff3c41","#b62b6e","#00b7c9"))+#自定义分支颜色  #第一层注释_Diet  new_scale_fill() +   geom_fruit(    data=group,    geom=geom_tile,    mapping=aes(y=sample, fill=Diet),    width = 0.4,#注释的宽度    offset=0.3 #位置  )+  scale_fill_manual(    values=c("#00ff01", "#fecc32", "#ff0100"),    guide=guide_legend(keywidth=1, keyheight=1, order=1))+  #第二层注释_Diet (detailed)  new_scale_fill() +  geom_fruit(    data=data2,    geom=geom_tile,    mapping=aes(y=sample, x=`Diet (detailed)`, fill=value),    color="grey80",    pwidth=0.8,    offset=0.05, #位置    axis.params=list(      axis="x", # 添加图层的轴文本      text.angle=0, #x 轴的文本角度      hjust=0.5,  # 调整文字轴的水平位置      text.size = 4,#文字大小      line.alpha = 0    )  )+  scale_fill_manual(    values=c("#000000", "#a95401", "#ff8000","#ffff00"),    guide=guide_legend(keywidth=1, keyheight=1, order=2),    name="Diet (detailed)")+  #添加第三层注释——Habitat  new_scale_fill() +  geom_fruit(data=group,             geom = geom_star,             mapping = aes(y=sample,fill = Habitat),              starshape = 13, color = NA,             size = 5, starstroke = 0, pwidth = 2, offset = 0.2,             grid.params=list(               linetype=3,               size=0.4             ))+  scale_fill_manual(    values=c("#ff9901", "#01ccff", "#999601", "#663300"),    guide=guide_legend(keywidth=1, keyheight=1, order=3)  )+  #添加第四层注释——Captive-wild  new_scale_fill() +  geom_fruit(data=group,             geom = geom_star,             mapping = aes(y=sample,fill = `Captive-wild`),              starshape = 13, color = NA,             size = 5, starstroke = 0, pwidth = 2, offset = 0.2,             grid.params=list(               linetype=3,               size=0.4             ))+  scale_fill_manual(    values=c("#ff9901", "#0000ff", "#009900"),    guide=guide_legend(keywidth=1, keyheight=1, order=4)  )+  #添加第五层注释——Captive-wild  new_scale_fill() +  geom_fruit(data=group,             geom = geom_star,             mapping = aes(y=sample,fill = `Sample type`),              starshape = 13, color = NA,             size = 5, starstroke = 0, pwidth = 2, offset = 0.2,             grid.params=list(               linetype=3,               size=0.4,             ))+  scale_fill_manual(    values=c("#663300", "#cc9a00"),    guide=guide_legend(keywidth=1, keyheight=1, order=5)  )+  new_scale_fill() +  geom_fruit(    data=data6,    geom=geom_bar,    mapping=aes(y=sample, x=value, fill=variable),    pwidth=1,    width = 0.6,    offset = 0.1,    stat="identity",    orientation="y",    axis.params = list(axis = "x",                       text.size = 2,                       vjust = 0.5,                       hjust = 0,                       text.angle = -90,                       limits = c(0,1)),    grid.params = list(linetype = 3)  )+  scale_fill_manual(    values=c("#4078c0", "#6cc644", "#bd2c00","#c9510c", "#6e5494",             "#fbbc05","#ec6400", "#d5df00", "#e30061","#009ee3",             "#aca6a2"),    guide=guide_legend(keywidth=1, keyheight=1, order=6),    name = "Genus"  )