I've been playing testing the optimization method for tip matching in the
cophylo
method for co-phylogenetic plotting. Although a formal
proof is lacking (& not forthcoming!), I have shown via simulation that
when trees match precisely in their topology and labels, cophylo
can
successfully
find this matching - even for large trees that are completely scrambled
a priori.
This also seems to be true when some lineages are missing from one or the
other tree - for instance when cospeciation is perfect, but some taxa have
been lost due to extinction or missing taxa; and when cospeciation is
perfect, but host lineages might possess multiple, divergent parasite
races, themselves forming a monophyletic group - such as when 'speciation'
for the parasite lineages occurs more rapidly than in the hosts.
Below, I demonstrate both of these cases.
First, with some missing lineages. Let's start by simulating:
library(phytools)
## simulate matching trees
t1<-t2<-rtree(n=100)
## rotate nodes randomly in one of the trees
for(i in 1:100) t2<-rotateNodes(t2,sample(1:t2$Nnode+Ntip(t2),1))
## drop lineages from each tree at random
t1<-drop.tip(t1,sample(t1$tip.label,40))
t2<-drop.tip(t2,sample(t2$tip.label,30))
## these trees are fully scrambled!
plot(cophylo(t1,t2,rotate=FALSE),fsize=0.7)
![plot of chunk 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)
## match them by rotating nodes
obj<-cophylo(t1,t2)
## Rotating nodes to optimize matching...
## Done.
obj
## Object of class "cophylo" containing:
##
## (1) 2 (possibly rotated) phylogenetic trees in an object of class "multiPhylo".
##
## (2) A table of associations between the tips of both trees.
plot(obj,fsize=0.7)
![plot of chunk 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)
Next, let's imagine a case in which cospeciation is perfect, but in which
each host species matches with a clade of parasites (for instance). We
can simulate this by first generating host & parasite trees that match,
then slicing the host tree at a time point (say, when 26 lineages are
extant). Next we compute all the descendants from that point in each subtree.
These are our parasite clades.
## simulate trees
host<-parasite<-pbtree(n=100,scale=10)
## slice to get all subtrees
LTT<-ltt(host,plot=FALSE)
t<-LTT$times[which(LTT$ltt==26)]
subs<-treeSlice(parasite,t,trivial=TRUE)
hosts<-sapply(subs,function(x) x$tip.label[1])
host<-drop.tip(host,setdiff(host$tip.label,hosts))
host$tip.label<-LETTERS
for(i in 1:Ntip(host)){
if(i==1) assoc<-cbind(rep(host$tip.label[i],Ntip(subs[[i]])),
subs[[i]]$tip.label)
else {
assoc<-rbind(assoc,
cbind(rep(host$tip.label[i],Ntip(subs[[i]])),
subs[[i]]$tip.label))
}
}
host
##
## Phylogenetic tree with 26 tips and 25 internal nodes.
##
## Tip labels:
## A, B, C, D, E, F, ...
##
## Rooted; includes branch lengths.
parasite
##
## Phylogenetic tree with 100 tips and 99 internal nodes.
##
## Tip labels:
## t1, t4, t50, t51, t23, t91, ...
##
## Rooted; includes branch lengths.
assoc
## [,1] [,2]
## [1,] "A" "t1"
## [2,] "B" "t4"
## [3,] "C" "t50"
## [4,] "C" "t51"
## [5,] "D" "t23"
## [6,] "D" "t91"
## [7,] "D" "t92"
## [8,] "E" "t20"
## [9,] "E" "t21"
## [10,] "F" "t61"
## [11,] "F" "t62"
## [12,] "F" "t15"
## [13,] "G" "t14"
## [14,] "G" "t16"
## [15,] "G" "t38"
## [16,] "G" "t39"
## [17,] "G" "t30"
## [18,] "H" "t2"
## [19,] "I" "t36"
## [20,] "I" "t37"
## [21,] "I" "t33"
## [22,] "I" "t34"
## [23,] "J" "t8"
## [24,] "J" "t22"
## [25,] "J" "t73"
## [26,] "J" "t74"
## [27,] "J" "t48"
## [28,] "K" "t5"
## [29,] "L" "t6"
## [30,] "M" "t55"
## [31,] "M" "t56"
## [32,] "N" "t99"
## [33,] "N" "t100"
## [34,] "N" "t64"
## [35,] "N" "t52"
## [36,] "N" "t17"
## [37,] "N" "t40"
## [38,] "N" "t41"
## [39,] "O" "t19"
## [40,] "O" "t68"
## [41,] "O" "t69"
## [42,] "O" "t18"
## [43,] "P" "t25"
## [44,] "P" "t43"
## [45,] "P" "t44"
## [46,] "P" "t70"
## [47,] "P" "t71"
## [48,] "P" "t63"
## [49,] "P" "t11"
## [50,] "P" "t10"
## [51,] "P" "t9"
## [52,] "Q" "t13"
## [53,] "Q" "t46"
## [54,] "Q" "t47"
## [55,] "Q" "t42"
## [56,] "R" "t28"
## [57,] "R" "t29"
## [58,] "S" "t49"
## [59,] "S" "t89"
## [60,] "S" "t90"
## [61,] "T" "t26"
## [62,] "T" "t27"
## [63,] "U" "t45"
## [64,] "U" "t79"
## [65,] "U" "t80"
## [66,] "U" "t97"
## [67,] "U" "t98"
## [68,] "U" "t12"
## [69,] "V" "t7"
## [70,] "W" "t57"
## [71,] "W" "t58"
## [72,] "W" "t59"
## [73,] "W" "t81"
## [74,] "W" "t82"
## [75,] "W" "t60"
## [76,] "W" "t32"
## [77,] "X" "t24"
## [78,] "X" "t75"
## [79,] "X" "t76"
## [80,] "X" "t77"
## [81,] "X" "t78"
## [82,] "X" "t65"
## [83,] "X" "t66"
## [84,] "X" "t67"
## [85,] "X" "t95"
## [86,] "X" "t96"
## [87,] "Y" "t53"
## [88,] "Y" "t54"
## [89,] "Y" "t35"
## [90,] "Y" "t93"
## [91,] "Y" "t94"
## [92,] "Y" "t85"
## [93,] "Y" "t86"
## [94,] "Y" "t72"
## [95,] "Y" "t83"
## [96,] "Y" "t84"
## [97,] "Y" "t87"
## [98,] "Y" "t88"
## [99,] "Y" "t31"
## [100,] "Z" "t3"
## scramble order of parasites
for(i in 1:100) parasite<-rotateNodes(parasite,sample(1:parasite$Nnode+
Ntip(parasite),1))
## they are fully scrambled
plot(cophylo(host,parasite,assoc=assoc,rotate=FALSE),
fsize=c(1,0.5))
![plot of chunk 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)
## rotate to optimize matching
obj<-cophylo(host,parasite,assoc=assoc)
## Rotating nodes to optimize matching...
## Done.
plot(obj,fsize=c(1,0.5),link.type="curved")
![plot of chunk 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)
Pretty neat, right?