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R绘图模板——进化树+柱状堆积图+热图！
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代码如下：
1、设置工作环境、加载R包
rm(list=ls())#clear Global Environmentsetwd('D:\\test')#设置工作路径
#加载R包library (reshape2)library(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphicslibrary(plyr) # Tools for Splitting, Applying and Combining Datalibrary(ggtree) # an R package for visualization of tree and annotation datalibrary(vegan) # Community Ecology Packagelibrary(ape) # Analyses of Phylogenetics and Evolutionlibrary( RColorBrewer) # not installed on this machinelibrary(aplot) # Decorate a 'ggplot' with Associated Information2、加载数据并进行数据处理
#读取数据df1 <- read.table(file="Genus.txt",sep="\t",header=T,check.names=FALSE)#查看前6行head(df1)##利用循环处理具有重复的数据#初始化data<-aggregate(E ~ Tax,data=df1,sum)#重命名colnames(data)[2]<-"example"for (i in colnames(df1)[2:length(colnames(df1))]){ #计算每列的和 data1<-aggregate(df1[,i]~Tax,data=df1,sum) colnames(data1)[2]<-i #合并列 data<-merge(data,data1,by="Tax")}df2<-data[,-2]rownames(df2)=df2$Tax#修改行名df3=df2[,-1]#删除多的列
#计算物种总丰度并降序排列df3$rowsum <- apply(df3,1,sum)df4 <- df3[order (df3$rowsum,decreasing=TRUE),]df5 = df4[,-6]#删除求和列#求物种相对丰度df6 <- apply(df5,2,function(x) x/sum(x))#由于之间已经按照每行的和进行过升序排列，所以可以直接取前10行df7 <- df6[1:10,]df8 <- 1-apply(df7, 2, sum) #计算剩下物种的总丰度#合并数据df9 <- rbind(df7,df8)row.names(df9)[11]="Others"#变量格式转换,宽数据转化为长数据,方便后续作图df_genus <- melt(df9)names(df_genus)[1:2] <- c("Taxonomy","sample") #修改列名#颜色col <-colorRampPalette(brewer.pal(12,"Paired"))(11)3、柱状堆积图绘制
##柱状堆积图绘制绘图p1<-ggplot(df_genus, aes( x = sample,y=100 * value,fill = Taxonomy))+#geom_col和geom_bar这两条命令都可以绘制堆叠柱形图 geom_col(position = 'stack',width = 0.8)+ #geom_bar(position = "stack", stat = "identity", width = 0.6) scale_y_continuous(expand = c(0,0))+# 调整y轴属性，使柱子与X轴坐标接触 labs(x=NULL,y="Relative Abundance(%)",#设置X轴和Y轴的名称以及添加标题 fill="Taxonomy")+ guides(fill=guide_legend(keywidth = 1, keyheight = 1)) + coord_flip()+ theme(panel.grid = element_blank(), panel.background = element_blank())+ scale_fill_manual(values = col)+ theme(axis.text.x = element_text(color = "black",size = 10))p14、进化树绘制
####聚类树data2 <- df3[-6]#计算距离矩阵df_dist <- vegdist(t(data2),method = 'bray')#使用bray curtis方法计算距离矩阵####进行层次聚类,可选择方法有single、complete、median、mcquitty、average 、centroid 、ward df_hc <- hclust(df_dist,method="average")#使用类平均法进行聚类#绘图plot(as.dendrogram(df_hc),type="rectangle",horiz=T)#实现将垂直的聚类树变成水平聚类树，绘图# 将聚类结果转成系统发育格式df_tree <- as.phylo(df_hc)# 对树分组gro <- list(group1=c("A","E"), group2=c("C","D"), group3=c("B"))# 将分组信息和进化树组合到一起tree<-groupOTU(df_tree,gro)# ggtree绘图p2 <- ggtree(tree,size=1.2)+ geom_tiplab(aes(color=group),size=5,align = F, offset = 0.008,show.legend = F)+ geom_tippoint(aes(shape=group,color=group), show.legend = T, size=4)p25、热图绘制
#### 热图绘制#去排名前20的属df7 <- df6[1:19,]df8 <- 1-apply(df7, 2, sum) #计算剩下物种的总丰度#合并数据df9 <- rbind(df7,df8)row.names(df9)[20]="Others"#添加分组信息df9 <- as.data.frame(df9)df9$group <- factor(c(rep("a",8),rep("b",6),rep("c",4),rep("d",2)))df9$tax <- rownames(df9)#变量格式转换,宽数据转化为长数据,方便后续作图df_heatmap <- melt(df9,id.vars = c("group","tax"))#指定显示顺序df_heatmap$tax <- factor(df_heatmap$tax,levels = df9$tax)#颜色col2 <-c("#1aafd0","#6a67ce","#ffb900","#fc636b")#绘图p2 <- ggplot()+ geom_point(df_heatmap,mapping = aes(x = tax, y = variable, size = value, color=group))+ scale_color_manual(values = col2)+ scale_size_continuous(range = c(1, 10))+ theme(panel.background = element_blank(), legend.key = element_blank(), axis.text.x = element_text(color = "black",angle=90,size = 10,vjust = 0.5,hjust = 0), axis.text.y = element_text(color = "black",size = 10), panel.grid.major = element_line(color = "gray"),#网格线条颜色 panel.border = element_rect(color="black",fill=NA))+#边框色 labs(x=NULL,y=NULL)+ annotate("rect", xmin = 0.5, xmax = 8.5, ymin = 0, ymax = 5.8, alpha = 0.2,fill="#1aafd0") + annotate("rect", xmin = 8.5, xmax = 14.5, ymin = 0, ymax = 5.8, alpha = 0.2,fill="#6a67ce") + annotate("rect", xmin = 14.5, xmax = 18.5, ymin = 0, ymax = 5.8, alpha = 0.2,fill="#ffb900") + annotate("rect", xmin = 18.5, xmax = 21, ymin = 0, ymax = 5.8, alpha = 0.2,fill="#fc636b") + scale_x_discrete(position = "top")p26、将柱状堆积图与树图和热图进行组合
#####将柱状堆积图与树图进行组合p1 <- p1+theme(axis.text.y = element_text(color = "black",size=12))col3 <-colorRampPalette(brewer.pal(9,"Set1"))(3)p3 <- ggtree(tree,size=1)+ geom_tippoint(aes(shape=group,color=group), show.legend = T, size=4)+ scale_color_manual(values = col3)p4 <- p2+theme(axis.text.y = element_blank())p <- p1%>%insert_left(p3,width = 0.4)%>% insert_right(p4,width = 0.9)p温馨提示
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