绘制进化树的方法有很多，入门的MEGA。美化比较好用的，也是我经常用的工具如iTOL，evolview等。
今天测试了另外一个别的paper和笔记中经常用的的一个R包：ggtree，据说可以轻轻松松绘制高端进化树。所以闲暇之余就测试了一下。
======安装========
source("https://bioconductor.org/biocLite.R")
biocLite("ggtree")
或者：
# Bioconductor version
library(BiocManager)
BiocManager::install("ggtree")
# github version
devtools::install_github("YuLab-SMU/ggtree")
===例子测试======
library(ggtree)
library(treeio)
library(tidytree)
以这个test.tree作为测试：
tree <- read.newick("test.tree",node.label = "support")
注：也可以读取其它格式的tree文件
read.beast() ## for parsing output of BEASE
read.codeml() ## for parsing output of CODEML (rst andmlc files)
read.codeml_mlc() ## for parsing mlc file (output of CODEML)
read.hyphy() ## for parsing output of HYPHY
read.jplace() ## for parsing jplace file includingoutput from EPA and pplacer
read.nhx() ## for parsing NHX file includingoutput from PHYLODOG and RevBayes
read.paml_rst() ## for parsing rst file (output of BASEMLand CODEML)
read.r8s() ## for parsing output of r8s
read.raxml() ## for parsing output of RAxML
//显示进化树
ggtree(tree)
ggtree(tree, color="firebrick", size=1, linetype="dotted")
# 默认情况下，树以ladderize形式查看，可以设置参数ladderize= FALSE以禁用它
ggtree(tree, ladderize=FALSE)
//设置branch.length="none"只查看树的拓扑结构
ggtree(tree,branch.length="none")
//layout参数设置不同的展示布局,默认为“rectangular”
ggtree(tree, layout="rectangular") +ggtitle("rectangular (phylogram)")
ggtree(tree, layout="rectangular",branch.length= "none") + ggtitle("rectangular (cladogram)")
ggtree(tree, layout="slanted") +ggtitle("slanted (phylogram)")
ggtree(tree, layout="circular") +ggtitle("circular (phylogram)")
ggtree(tree, layout="circular",branch.length ="none") + ggtitle("circular (cladogram)")
ggtree(tree, layout="fan", open.angle=160) +ggtitle("fan (phylogram)")
ggtree(tree, layout="equal_angle") +ggtitle("equal angle (unrooted)")
ggtree(tree, layout="daylight") +ggtitle("daylight (unrooted)")
ggtree(tree, layout="daylight",branch.length ="none") + ggtitle("daylight (cladogram)")
显示树的尺度和时间标度：
ggtree(tree) + geom_treescale()
ggtree(tree) + theme_tree2()
ggtree(tree) + geom_treescale(fontsize=8, linesize=1, offset=-0.5) + theme_tree2()
显示树的节点和节点标签
# 使用geom_nodepoint，geom_tippoint或者geom_point添加内部节点
ggtree(tree) + geom_point(aes(shape=isTip, color=isTip), size=3)
例如通过控制node，tip或者point来一起控制节点的美观：
ggtree(tree) + geom_nodepoint(color="red", alpha=1/4, size=5)+geom_tippoint(color="blue", alpha=1/4, size=10,shape=1)
使用geom_nodelab和geom_tiplab显示节点标签
ggtree(tree) + geom_nodepoint(color="blue",alpha=1/4, size=5) + geom_tiplab(size=3, color="red")
ggtree(tree) + geom_tiplab(size=3, color="red") + geom_nodelab(aes(subset=!isTip,label=node),hjust=-.3,color="blue")
设置不同的主题
ggtree(tree, color="red") +theme_tree("black") //就是换背景色
显示树上所有节点的序号
ggtree(tree) +geom_text2(aes(label=node),hjust=-.3,color="red")
当然，也可以显示内部节点的序号
ggtree(tree) +geom_text2(aes(subset=!isTip,label=node),hjust=-.3,color="red")
给指定节点的clade添加标签
ggtree(tree) + geom_text(aes(label=node),hjust=-.3,color="red") + geom_cladelabel(node=19,label="Group A",barsize = 2,color="blue") +geom_cladelabel(node=15,label="Group B",barsize = 2,color="green")
高亮指定节点所对应的区域
ggtree(tree) + geom_text(aes(label=node),hjust=-.3,color="red") + geom_hilight(node=15,fill = "green",alpha = 0.6)+geom_cladelabel(node=15,label="Group B",barsize = 2,color="green")
在两个节点之间添加线段和标记
ggtree(tree) + geom_text(aes(label=node),hjust=-.3,color="red") + geom_strip(17,15,barsize = 1,color="blue",label="A")
====其它数据和进化树关联起来=====
Kat Holt开发了plotTree（https://github.com/katholt/plotTree）包含了R和python两个版本，可以支持把一些相关的信息（比如taxa info，SNP位点，matrix等）和进化树关联起来，这样可以使图片更好看。
下载：
https://github.com/katholt/plotTree
然后导入包：
source("plotTree.R")
plotTree(tree="tree.nwk")
plotTree(tree="tree.nwk",ancestral.reconstruction=F,tip.colour.cex=1,cluster=T,tipColours=c("black","purple2","skyblue2","grey"),lwd=1,infoFile="info.csv",colourNodesBy="location",treeWidth=10,infoWidth=10,infoCols=c("name","location","year"))
//加入一些额外的描述信息，下为info.csv信息
plotTree(tree="tree.nwk",heatmapData="pan.csv",ancestral.reconstruction=F,tip.colour.cex=1,cluster=T,tipColours=c("black","purple2","skyblue2","grey"),lwd=1,infoFile="info.csv",colourNodesBy="location",treeWidth=5,dataWidth=20,infoCols=NA)
//也可以加入heatmap数据
pan.csv里数据
plotTree(tree="tree.nwk",heatmapData="res_genes.csv",ancestral.reconstruction=F,tip.colour.cex=1,cluster=F,heatmap.colours=c("white","grey","seagreen3","darkgreen","green","brown","tan","red","orange","pink","magenta","purple","blue","skyblue3","blue","skyblue2"),tipColours=c("black","purple2","skyblue2","grey"),lwd=1,infoFile="info.csv",colourNodesBy="location",treeWidth=10,dataWidth=10,infoCols=c("name","year"),infoWidth=8)
//基因信息，其实也相当于离散型的heatmap，下面为res_genes.csv内容
plotTree(tree="tree.nwk",barData="bar.csv")
//也可以通过barData添加bar图
当然很好也可以利用facet_plot实现。
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