用ape显示野兽祖先状态重建

Question

我已经用野兽运行了祖先状态的重建，这给了我一个这样的Nexus文件

#NEXUS

Begin taxa;
    Dimensions ntax=93;
        Taxlabels
            adan1251 
            blag1240-nule 
            wers1238-marit
            ;
End;
Begin trees;
    Translate
           1 adan1251,
           2 blag1240-nule,
           3 wers1238-marit
;
tree STATE_0 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_1 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:1.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_2 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:2.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_3 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:3.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_4 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:4.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
End;

(除了20倍的分类群，2000倍的树木和实际上不同的树木。)

我想将意图节点和提示节点中的词法项的重建可视化，看来ape可能是一个很好的工具，因为它可以编写脚本，它可以读取Nexus文件(我尝试使用read.nexus("filename.nex")，并且看起来str是合理的)，从screenshots.html判断它可以以一种很好的格式显示重构：

​

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在从原始数据构建某种共识树之后，如何使ape从10000个不同的Newick树的注释([&...])中给出的数据构建类似于此的[&...]树？

Answer 1

在查看了ape包的一些文档之后，如果您对nodelabels()函数感兴趣的话。

在绘制了树图之后，您只需传递一个向量，其长度等于您拥有的分类法的数量，而每个值代表了占据其中一个字符状态的概率。然后，只需用thermo选项绘制节点上的概率。

在帮助文件中：

data(bird.orders)
plot(bird.orders, "c", use.edge.length = FALSE, font = 1) 
nodelabels(thermo = runif(22), cex = .8)

如果有两个以上的状态，则必须创建一个矩阵，其列数等于您的状态数，行数等于分类法的数目。对于每一行，包括每个分类群的相对概率。每一行加起来应该是1。

扩展示例，有三个状态：

thermo <- matrix(c(.6,.3,.1), nrow=22, ncol=3)
plot(bird.orders, "c", use.edge.length = FALSE, font = 1)
nodelabels(thermo = thermo, cex = .8)