如何通过节点或叶子中的 label 折叠 phylog.netic 树中的分支？

Question

I have built a phylog.netic tree for a protein family that can be split into different groups, classifying each one by its type of receptor or type of response. The nodes in the tree are labeled as the type of receptor.

In the phylog.netic tree I can see that proteins that belong to the same groups or type of receptor have clustered together in the same branches. So I would like to collapse these branches that have labels in common, grouping them by a given list of keywords.

The command would be something like this:

./collapse_tree_by_label -f phylog.netic_tree.newick -l list_of_labels_to_collapse.txt -o collapsed_tree.eps(or pdf)

My list_of_labels_to_collapse.txt would be like this: AB C D

My newick tree would be like this: (A_1:0.05,A_2:0.03,A_3:0.2,A_4:0.1):0.9,(((B_1:0.05,B_2:0.02,B_3:0.04):0.6,(C_1:0.6,C_2:0.08):0.7):0.5,(D_1:0.3,D_2:0.4,D_3:0.5,D_4:0.7,D_5:0.4):1.2)

The output image without collapsing is like this: http://i.stack.imgur.com/pHkoQ.png

The output image collapsing should be like this (collapsed_tree.eps): http://i.stack.imgur.com/TLXd0.png

The width of the triangles should represent the branch length, and the high of the triangles must represent the number of nodes in the branch.

I have been playing with the "ape" package in R. I was able to plot a phylog.netic tree, but I still can't figure out how to collapse the branches by keywords in the labels:

require("ape")

This will load the tree:

cat("((A_1:0.05,A_2:0.03,A_3:0.2,A_4:0.1):0.9,(((B_1:0.05,B_2:0.02,B_3:0.04):0.6,(C_1:0.6,C_2:0.08):0.7):0.5,(D_1:0.3,D_2:0.4,D_3:0.5,D_4:0.7,D_5:0.4):1.2):0.5);", file = "ex.tre", sep = "\n")
tree.test <- read.tree("ex.tre")

Here should be the code to collapse

This will plot the tree:

plot(tree.test)

Answer 1

Your tree as it is stored in R already has the tips stored as polytomies. It's just a matter of plotting the tree with triangles representing the polytomies.

There is no function in ape to do this, that I am aware of, but if you mess with the plotting function a little bit you can pull it off

# Step 1: make edges for descendent nodes invisible in plot:
groups <- c("A", "B", "C", "D")
group_edges <- numeric(0)
for(group in groups){
  group_edges <- c(group_edges,getMRCA(tree.test,tree.test$tip.label[grepl(group, tree.test$tip.label)]))
}
edge.width <- rep(1, nrow(tree.test$edge))
edge.width[tree.test$edge[,1] %in% group_edges ] <- 0


# Step 2: plot the tree with the hidden edges
plot(tree.test, show.tip.label = F, edge.width = edge.width)

# Step 3: add triangles
add_polytomy_triangle <- function(phy, group){
  root <- length(phy$tip.label)+1
  group_node_labels <- phy$tip.label[grepl(group, phy$tip.label)]
  group_nodes <- which(phy$tip.label %in% group_node_labels)
  group_mrca <- getMRCA(phy,group_nodes)

  tip_coord1 <- c(dist.nodes(phy)[root, group_nodes[1]], group_nodes[1])
  tip_coord2 <- c(dist.nodes(phy)[root, group_nodes[1]], group_nodes[length(group_nodes)])
  node_coord <- c(dist.nodes(phy)[root, group_mrca], mean(c(tip_coord1[2], tip_coord2[2])))

  xcoords <- c(tip_coord1[1], tip_coord2[1], node_coord[1])
  ycoords <- c(tip_coord1[2], tip_coord2[2], node_coord[2])
  polygon(xcoords, ycoords)
}

Then you just have to loop through the groups to add the triangles

for(group in groups){
  add_polytomy_triangle(tree.test, group)
}

Answer 2

I think the script is finally doing what I wanted. From the answer that @CactusWoman provided, I changed the code a little bit so the script will try to find the MRCA that represents the largest branch that matches to my search pattern. This solved the problem of not merging non-polytomic branches, or collapsing the whole tree because one matching node was mistakenly outside the correct branch.

In addition, I included a parameter that represents the limit for the pattern abundance ratio in a given branch, so we can select and collapse/group branches that have at least 90% of its tips matching to the search pattern, for example.

library(geiger)
library(phylobase)
library(ape)

#functions
find_best_mrca <- function(phy, group, threshold){

     group_matches <- phy$tip.label[grepl(group, phy$tip.label, ignore.case=TRUE)]
     group_mrca <- getMRCA(phy,phy$tip.label[grepl(group, phy$tip.label, ignore.case=TRUE)])
     group_leaves <- tips(phy, group_mrca)
     match_ratio <- length(group_matches)/length(group_leaves)

      if( match_ratio < threshold){

           #start searching for children nodes that have more than 95% of descendants matching to the search pattern
           mrca_children <- descendants(as(phy,"phylo4"), group_mrca, type="all")
           i <- 1
           new_ratios <- NULL
           nleaves <- NULL
           names(mrca_children) <- NULL

           for(new_mrca in mrca_children){
                child_leaves <- tips(tree.test, new_mrca)
                child_matches <- grep(group, child_leaves, ignore.case=TRUE)
                new_ratios[i] <- length(child_matches)/length(child_leaves)
                nleaves[i] <- length(tips(phy, new_mrca))
                i <- i+1
           }



           match_result <- data.frame(mrca_children, new_ratios, nleaves)


           match_result_sorted <- match_result[order(-match_result$nleaves,match_result$new_ratios),]
           found <- numeric(0);

           print(match_result_sorted)

           for(line in 1:nrow(match_result_sorted)){
                 if(match_result_sorted$ new_ratios[line]>=threshold){
                     return(match_result_sorted$mrca_children[line])
                     found <- 1
                 }

           }

           if(found==0){return(found)}
      }else{return(group_mrca)}




}

add_triangle <- function(phy, group,phylo_plot){

     group_node_labels <- phy$tip.label[grepl(group, phy$tip.label)]
     group_mrca <- getMRCA(phy,group_node_labels)
     group_nodes <- descendants(as(tree.test,"phylo4"), group_mrca, type="tips")
     names(group_nodes) <- NULL

     x<-phylo_plot$xx
     y<-phylo_plot$yy


     x1 <- max(x[group_nodes])
     x2 <-max(x[group_nodes])
     x3 <- x[group_mrca]

     y1 <- min(y[group_nodes])
     y2 <- max(y[group_nodes])
     y3 <-  y[group_mrca]

     xcoords <- c(x1,x2,x3)
     ycoords <- c(y1,y2,y3)

     polygon(xcoords, ycoords)

     return(c(x2,y3))

}



#main

  cat("((A_1:0.05,E_2:0.03,A_3:0.2,A_4:0.1,A_5:0.1,A_6:0.1,A_7:0.35,A_8:0.4,A_9:01,A_10:0.2):0.9,((((B_1:0.05,B_2:0.05):0.5,B_3:0.02,B_4:0.04):0.6,(C_1:0.6,C_2:0.08):0.7):0.5,(D_1:0.3,D_2:0.4,D_3:0.5,D_4:0.7,D_5:0.4):1.2):0.5);", file = "ex.tre", sep = "\n")
tree.test <- read.tree("ex.tre")


# Step 1: Find the best MRCA that matches to the keywords or search patten

groups <- c("A", "B|C", "D")
group_labels <- groups

group_edges <- numeric(0)
edge.width <- rep(1, nrow(tree.test$edge))
count <- 1


for(group in groups){

    best_mrca <- find_best_mrca(tree.test, group, 0.90)

    group_leaves <- tips(tree.test, best_mrca)

    groups[count] <- paste(group_leaves, collapse="|")
    group_edges <- c(group_edges,best_mrca)

    #Step2: Remove the edges of the branches that will be collapsed, so they become invisible
    edge.width[tree.test$edge[,1] %in% c(group_edges[count],descendants(as(tree.test,"phylo4"), group_edges[count], type="all")) ] <- 0
    count = count +1

}


#Step 3: plot the tree hiding the branches that will be collapsed/grouped

last_plot.phylo <- plot(tree.test, show.tip.label = F, edge.width = edge.width)

#And save a copy of the plot so we can extract the xy coordinates of the nodes
#To get the x & y coordinates of a plotted tree created using plot.phylo
#or plotTree, we can steal from inside tiplabels:
last_phylo_plot<-get("last_plot.phylo",envir=.PlotPhyloEnv)

#Step 4: Add triangles and labels to the collapsed nodes
for(i in 1:length(groups)){

  text_coords <- add_triangle(tree.test, groups[i],last_phylo_plot)

  text(text_coords[1],text_coords[2],labels=group_labels[i], pos=4)

}

Answer 3

I've also been searching for this kind of tool for ages, not so much for collapsing categorical groups, but for collapsing internal nodes based on a numerical support value.

The di2multi function in the ape package can collapse nodes to polytomies, but it currently can only does this by branch length threshold. Here is a rough adaptation that allows collapsing by a node support value threshold instead (default threshold = 0.5).

Use at your own risk, but it works for me on my rooted Bayesian tree.

di2multi4node <- function (phy, tol = 0.5) 
  # Adapted di2multi function from the ape package to plot polytomies
  # based on numeric node support values
  # (di2multi does this based on edge lengths)
  # Needs adjustment for unrooted trees as currently skips the first edge
{
  if (is.null(phy$edge.length)) 
    stop("the tree has no branch length")
  if (is.na(as.numeric(phy$node.label[2])))
    stop("node labels can't be converted to numeric values")
  if (is.null(phy$node.label))
    stop("the tree has no node labels")
  ind <- which(phy$edge[, 2] > length(phy$tip.label))[as.numeric(phy$node.label[2:length(phy$node.label)]) < tol]
  n <- length(ind)
  if (!n) 
    return(phy)
  foo <- function(ancestor, des2del) {
    wh <- which(phy$edge[, 1] == des2del)
    for (k in wh) {
      if (phy$edge[k, 2] %in% node2del) 
        foo(ancestor, phy$edge[k, 2])
      else phy$edge[k, 1] <<- ancestor
    }
  }
  node2del <- phy$edge[ind, 2]
  anc <- phy$edge[ind, 1]
  for (i in 1:n) {
    if (anc[i] %in% node2del) 
      next
    foo(anc[i], node2del[i])
  }
  phy$edge <- phy$edge[-ind, ]
  phy$edge.length <- phy$edge.length[-ind]
  phy$Nnode <- phy$Nnode - n
  sel <- phy$edge > min(node2del)
  for (i in which(sel)) phy$edge[i] <- phy$edge[i] - sum(node2del < 
                                                           phy$edge[i])
  if (!is.null(phy$node.label)) 
    phy$node.label <- phy$node.label[-(node2del - length(phy$tip.label))]
  phy
}

Answer 4

This is my answer based on phytools::phylo.toBackbone function, see http://blog.phytools.org/2013/09/even-more-on-plotting-subtrees-as.html , and http://blog.phytools.org/2013/10/finding-edge-lengths-of-all-terminal.html . First, load the function at the end of code.

library(ape)
library(phytools)  #phylo.toBackbone
library(phangorn) 

cat("((A_1:0.05,E_2:0.03,A_3:0.2,A_4:0.1,A_5:0.1,A_6:0.1,A_7:0.35,A_8:0.4,A_9:01,A_10:0.2):0.9,((((B_1:0.05,B_2:0.05):0.5,B_3:0.02,B_4:0.04):0.6,(C_1:0.6,C_2:0.08):0.7):0.5,(D_1:0.3,D_2:0.4,D_3:0.5,D_4:0.7,D_5:0.4):1.2):0.5);", file = "ex.tre", sep = "\n")

phy <- read.tree("ex.tre")
groups <- c("A", "B|C", "D") 

backboneoftree<-makebackbone(groups,phy)
#   tip.label clade.label  N     depth
# 1       A_1           A 10 0.2481818
# 2       B_1         B|C  6 0.9400000
# 3       D_1           D  5 0.4600000

par(mfrow=c(2,2), mar=c(0,2,2,0) )
plot(phy, main="Complete tree" )
plot(backboneoftree)

makebackbone<-function(groupings,phy){ 
  listofspecies<-phy$tip.label
  listtopreserve<-list()
  lengthofclades<-list()
  meandistnode<-list()
  newedgelengths<-list()
  for (i in 1:length(groupings)){
    groupings<-groups
    bestmrca<-getMRCA(phy,grep(groupings[i], phy$tip.label) )
    mrcatips<-phy$tip.label[unlist(phangorn::Descendants(phy,bestmrca, type="tips") )]
    listtopreserve[i]<- mrcatips[1]
    meandistnode[i]<- mean(dist.nodes(phy)[unlist(lapply(mrcatips,  
    function(x) grep(x, phy$tip.label) ) ),bestmrca] )
    lengthofclades[i]<-length(mrcatips)
    provtree<-drop.tip(phy,mrcatips, trim.internal=F, subtree = T)
    n3<-length(provtree$tip.label)
    newedgelengths[i]<-setNames(provtree$edge.length[sapply(1:n3,function(x,y) 
      which(y==x),y=provtree$edge[,2])],
      provtree$tip.label)[provtree$tip.label[grep("tips",provtree$tip.label)] ]
  }  
  newtree<-drop.tip(phy,setdiff(listofspecies,unlist(listtopreserve)), 
                    trim.internal = T)
  n<-length(newtree$tip.label)
  newtree$edge.length[sapply(1:n,function(x,y) 
    which(y==x),y=newtree$edge[,2])]<-unlist(newedgelengths)+unlist(meandistnode)
  trans<-data.frame(tip.label=newtree$tip.label,clade.label=groupings,
                    N=unlist(lengthofclades), depth=unlist(meandistnode) )
  rownames(trans)<-NULL
  print(trans)
  backboneoftree<-phytools::phylo.toBackbone(newtree,trans)
  return(backboneoftree)
}

EDIT: I haven't tried this, but it might be another answer: "Script and function to transform the tip branches of a tree , ie the thickness or to triangles, with the width of both correlating with certain parameters (eg, species number of the clade) (tip.branches.R)" http://www.sysbot.biologie.uni-muenchen.de/en/people/cusimano/use_r.html http://www.sysbot.biologie.uni-muenchen.de/en/people/cusimano/tip.branches.R

Answer 5

This doesn't address depicting the clades as triangles, but it does help with collapsing low-support nodes. The library ggtree has a function as.polytomy which can be used to collapse nodes based on support values.

For example, to collapse bootstraps less than 50%, you'd use:

polytree = as.polytomy(raxtree, feature='node.label', fun=function(x) as.numeric(x) < 50)