# Intertwining phylogenetic trees and networks: R Example Script

#### Klaus Schliep, Alastair Potts, David Morrison and Guido Grimm

#### March 23, 2019

Description: This script provides examples of the new functions available in the phangorn library to ‘intertwine’ trees and networks, i.e. compare trees and networks and data transferrance. It also provides a step-by-step guide for users new to R.

Methodological advancement: The major advancement in this phangorn update is the introduction of a generic network object with a wide range of related transfer and analysis functions. These new functions provide the first means to directly transfer information amongst a wide range of phylogenetic trees and networks, as well as means to visualise and further analyse this information. This should provide a platform for individuals to easily conduct tree-network comparisons and stimulate further function development by the community.

What next?: By implementing full network handling compatibility in R, and providing exemplar scripts (such as this) and support, the scientific community now has an easy means to analyse and compare the results of phylogenetic trees vs. network approaches. We hope this will open a largely unexplored world to the general phylogenetic audience.

### Installing R

1. Download R
   Select the nearest mirror to your location at https://cran.r-project.org/mirrors.html
2. Select your operating system and download the relevant installation file.
3. Install R following the instructions.

### Installing the phangorn library

Open R and run the two lines of code below in the command line.
(You will need to select a region from which to download the library)

    install.packages("phangorn", dependencies=TRUE)
    # install latest development version needs devtools
    install.packages("devtools", dependencies=TRUE)
    library(devtools)
    install_github("KlausVigo/phangorn")

### Getting started

    library(phangorn)    # load the phangorn library
    library(magrittr)

### Set the working directory

This is often a major stumbling block for new R users. You need to specify where in your folder structure you wish to work. i.e, where are the files stored that you wish to input?

This is done using the `setwd()` function, e.g. `setwd("C:/TreesNetworks/Example Files")`.

We now set it to the folder of the phangorn package, which contains the files we want to load for this example.

### Read in the example file datasets:

This example files are based on the woodmouse dataset available in the ape library. Ultimately, this dataset is from this study: Michaux, J. R., Magnanou, E., Paradis, E., Nieberding, C. and Libois, R. (2003) Mitochondrial phylogeography of the Woodmouse (Apodemus sylvaticus) in the Western Palearctic region. Molecular Ecology, 12, 685-697.)

    ## automatically set the correct working directory for the examples below
    # setwd(system.file("extdata/trees", package = "phangorn"))
    # for this vignette we create a path to the files we want to load
    fdir <- system.file("extdata/trees", package = "phangorn")
    ## in your case it may look something like this...
    # setwd("C:/TreesNetworks/Example Files")

    ##DNA Matrix, maybe not needed
    woodmouse <- read.phyDat(file.path(fdir, "woodmouse.fasta"),format="fasta")
    ## RAxML best-known tree with bipartition support (from previous analysis)
    raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse"))
    ## RAxML bootstrap trees (from previous analysis)
    raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse"))
    ## MrBayes consensus tree (50% majority rule) (from previous analysis)
    mrbayes.tree <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.con"))
    ## MrBayes sample runs 1 and 2 (from previous analysis)
    run1 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run1.t"))
    run2 <- read.nexus(file.path(fdir,"woodmouse.mrbayes.nex.run2.t"))
    ## How many trees are in the MrBayes tree sample?
    run1

    ## 1001 phylogenetic trees

    run2

    ## 1001 phylogenetic trees

    ## Combining the two runs and removing 25% burn-in
    mrbayes.trees <- c(run1[251:1001],run2[251:1001])
    ## NeigbourNet Nexus file generated by SplitsTree (from previous analysis)
    Nnet <- read.nexus.networx(file.path(fdir,"woodmouse.nxs"))

All example files read into R.

### Viewing the data

    par(mfrow=c(1,2), mar=c(1,1,1,1)) # Setting plot parameters
    ### Plotting trees with support values:
    ##  RAxML
    plot(raxml.tree)
    nodelabels(raxml.tree$node.label, adj = c(1, 0), frame = "none")
    ##  MrBayes
    plot(mrbayes.tree)
    nodelabels(mrbayes.tree$node.label, adj = c(1, 0), frame = "none")

    par(mfrow=c(1,1)) # Setting plot parameters
    # NeighbourNet
    plot(Nnet,"2D")

    ## alternatively,
    # plot(Nnet,"2D")

### Interwining trees and network functions

### 1A:

Identification of edge bundles (in black) in a neighbour-net (NN) network that correspond to branches (labelled 1-12) in a tree (a maximum likelihood tree, in this case).

    # create a vector of labels for the network corresponding to edges in the tree
    edge.lab <- createLabel(Nnet, raxml.tree, raxml.tree$edge[,2], "edge")
    # could be also 1:27 instead of raxml.tree$edge[,2]

    # Show the correspondingly labelled tree and network in R
    par(mfrow=c(1,2))
    plot(raxml.tree, "u", rotate.tree = 180, cex=.7)
    edgelabels(raxml.tree$edge[,2],col="blue", frame="none", cex=.7)

    # find edges that are in the network but not in the tree
    edge.col <- rep("black", nrow(Nnet$edge))
    edge.col[ is.na(edge.lab) ] <- "red"
    # or a simpler alternative...
    edge.col <- createLabel(Nnet, raxml.tree, "black", nomatch="red")

    x <- plot(Nnet, edge.label = edge.lab, show.edge.label = T, "2D", edge.color = edge.col,
                      col.edge.label = "blue", cex=.7)

    # the above plot function returns an invisible networx object and this object also
    # contains the colors for the edges.

### 1B:

Bootstrap support for all branches (branch labels) in the ML tree mapped on the corresponding edge bundles of the NN network. The edges in the network which are not found as ML tree branches are highlighted in red.

    # the scaler argument multiplies the confidence values. This is useful to switch
    # confidences values between total, percentage or ratios.
    x <- addConfidences(Nnet,raxml.tree, scaler = .01)
    # find splits that are in the network but not in the tree
    split.col <- rep("black", length(x$splits))
    split.col[ !matchSplits(as.splits(x), as.splits(raxml.tree)) ] <- "red"

    # simpler alternative...
    split.col2 <- createLabel(x, raxml.tree, label="black", "split", nomatch="red")

    # Plotting in R
    out.x <- plot(x,"2D",show.edge.label=TRUE, split.color=split.col, col.edge.label = "blue")

Again we can write to SplitsTree for viewing…

    # write.nexus.networx(out.x,"woodmouse.tree.support.nxs")
    ## or we can also export the splits alone (for usage in software other than SplitsTree)
    # write.nexus.splits(as.splits(out.x),"woodmouse.splits.support.nxs")

### 1C:

Frequencies of bipartitions found in the bootstrap pseudoreplicates mapped on the corresponding edge bundles of the NN network using a threshold of 10% (i.e. any edge is labelled that occurs in at least 100 of the 1000 ML-BS pseudoreplicates). Edge bundles not found in the ML tree are labelled using grey edges.

    y <- addConfidences(Nnet, as.splits(raxml.bootstrap))
    edge.col <- createLabel(y, raxml.tree, label="black", "edge", nomatch="grey")

    y <- plot(y,"2D",show.edge.label=TRUE, edge.color=edge.col)

    ## Write to SplitsTree for viewing
    # write.nexus.networx(y,"NN.with.bs.support.nxs")

### Extras…

We can also compare the neighborNet with a consensusNet (Holland BR, Huber KT, Moulton V, Lockhart PJ,2004, Using consensus networks to visualize contradictory evidence for species phylogeny. Molecular Biology and Evolution, 21, 1459-1461). Furthermore, we can extract the support values from the consensus network, and place these on to the NeighbourNet (this is similar to the process explained in 1C above).

    cnet <- consensusNet(raxml.bootstrap,prob=0.10)
    edge.col <- createLabel(cnet, Nnet, label="black", "edge", nomatch="grey")
    cnet <- plot(cnet, "2D", show.edge.label = TRUE, edge.color=edge.col)

    edge.col <- createLabel(Nnet, cnet, label="black", "edge", nomatch="grey")
    z <- plot(Nnet, "2D", show.edge.label = TRUE, edge.color=edge.col)

    obj <- addConfidences(Nnet,cnet)
    plot(obj,"2D",show.edge.label=T, edge.color=edge.col, col.edge.label = "blue")

    ## Write to SplitsTree for viewing
    # write.nexus.networx(obj,"Nnet.with.ML.Cnet.Bootstrap.nxs")

###

There are four possible data patterns in phylogenetic reconstruction: (1) patterns that are well supported in the network and appear in the bootstapped trees; (2) patterns that are well supported by (part of) the data/network but do not appear in the optimised trees, i.e. they are incompatible with the tree; (3) patterns that are weakly supported by the network but appear in the optimised trees anyway, i.e. they are compatible with the tree.

Here we demonstrate these patterns by showing the relationships between splits weights, from a NeighborNet splits graph, bootstrap bipartitions support and bootstrap percentages plotted on the optimised tree for a dataset of Wang, Braun, and Kimball (2012).

(An advanced user figure…)

    Nnet <- read.nexus.networx(file.path(fdir,"RAxML_distances.Wang.nxs"))
    raxml.tree <- read.tree(file.path(fdir,"RAxML_bestTree.Wang.out")) %>% unroot
    raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.Wang.out"))

    bs_splits <- as.splits(raxml.bootstrap)
    tree_splits <- as.splits(raxml.tree) %>% unique %>% removeTrivialSplits
    # we overwrite bootstrap values and set the weights
    # to 1e-6 (almost zero), as we plot them on a log scale later on
    attr(bs_splits, "weights")[] <- 1e-6
    # combine the splits from the bootstrap and neighbor net
    # and delete duplicates and add the confidence values
    # we get rid of trivial splits
    all_splits <- c(Nnet$splits, bs_splits) %>% unique %>% removeTrivialSplits %>% addConfidences(bs_splits, scaler=100)

    # For easier plotting we create a matrix with the confidences and
    # weights as columns
    tab <- data.frame(SplitWeight = attr(all_splits, "weights"), Bootstrap=attr(all_splits, "confidences"), Tree=FALSE)
    # we add a logical variable pto indicate which splits are in the RAxML tree
    tab$Tree[matchSplits(tree_splits, all_splits, FALSE)] = TRUE

    tab[is.na(tab[,"Bootstrap"]),"Bootstrap"] <- 0
    tab[,"Bootstrap"] <- round(tab[,"Bootstrap"])
    rownames(tab) <- apply(as.matrix(all_splits, zero.print = ".", one.print = "|"), 1, paste0, collapse="")
    tab[1:10,]

    ##                              SplitWeight Bootstrap  Tree
    ## ..||........................   0.0171433       100  TRUE
    ## ..||||......................   0.0013902        14 FALSE
    ## ..||||......|||||...........   0.0001589         0 FALSE
    ## ||.........................|   0.0027691         1 FALSE
    ## ||..........................   0.0840367       100  TRUE
    ## ...|||......................   0.0001773         0 FALSE
    ## ...|||........|.|...........   0.0003663         0 FALSE
    ## |.|.........................   0.0060907         0 FALSE
    ## ....||......................   0.0385909       100  TRUE
    ## ||||........................   0.0018195        34  TRUE

    col <- rep("blue", nrow(tab))
    col[tab[,"Bootstrap"]==0] <- "green"
    col[tab[,"SplitWeight"]==1e-6] <- "red"

    pch = rep(19, nrow(tab))
    pch[tab$Tree] <- 17

    par(mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
    plot(tab[,"SplitWeight"], tab[,"Bootstrap"], log="x", col=col, pch=pch,
         xlab="Split weight (log scale)", ylab="Bootstrap (%)")
    legend("topright", inset=c(-0.35,0), c("Pattern 1", "Pattern 2", "Pattern 3", "Pattern in the\nbest tree"), pch=c(19,19,19,17), col=c("blue", "green", "red", "black"), bty="n")

### Figure 4

    YCh <- read.tree(file.path(fdir, "RAxML_bestTree.YCh"))
    mtG <- read.tree(file.path(fdir, "RAxML_bestTree.mtG"))
    ncAI <- read.tree(file.path(fdir, "RAxML_bestTree.AIs"))
    all_data <- read.tree(file.path(fdir, "RAxML_bestTree.3moles"))
    YCh_boot <- read.tree(file.path(fdir, "RAxML_bootstrap.YCh"))
    mtG_boot <- read.tree(file.path(fdir, "RAxML_bootstrap.mtG"))
    ncAI_boot <- read.tree(file.path(fdir, "RAxML_bootstrap.AIs"))
    all_data_boot <- read.tree(file.path(fdir, "RAxML_bootstrap.3moles"))

There are several option plotting a co-phylogeny. In the following we use the `cophylo` function of the `phytools` package.

    library(phytools)
    par(mfrow=c(2,1))
    obj <- cophylo(YCh, mtG)
    plot(obj, mar=c(.1,.1,2,.1),scale.bar=c(.005,.05), ylim=c(-.2,1))
    title("A. YCh                                    B. mtG")
    obj <-  cophylo(ncAI, all_data)
    plot(obj, mar=c(.1,.1,2,.1),scale.bar=c(.005,.05), ylim=c(-.2,1))
    title("C. ncAI                              D. All data")

Unfortunately this function does not (yet) offer to add confidences to some splits, but we can do this easily with more basic plot functions.

    par(mfrow=c(2,2), mar = c(2,2,4,2))
    YCh <- plotBS(midpoint(YCh), YCh_boot, "phylogram", p=0, main = "YCh")
    mtG <- plotBS(midpoint(mtG), mtG_boot, "phylogram", p=0, main = "mtG")
    ncAI <- plotBS(midpoint(ncAI), ncAI_boot, "phylogram", p=0, main = "ncAI")
    all_data <- plotBS(midpoint(all_data), all_data_boot, "phylogram", p=0, main = "All data")

We can compare this with consensus network with the different bootstrap values for the different genes.

    par(mfrow=c(1,1))
    cn <- consensusNet(c(YCh, mtG, ncAI))
    cn <- addConfidences(cn, YCh_boot) %>% addConfidences(mtG_boot, add=TRUE) %>% addConfidences(ncAI_boot, add=TRUE) %>% addConfidences(all_data_boot, add=TRUE)
    plot(cn, "2D", show.edge.label=TRUE)

Schliep, Klaus, Alastair J. Potts, David A. Morrison, and Guido W. Grimm. 2017. “Intertwining Phylogenetic Trees and Networks.” Methods in Ecology and Evolution, n/a–n/a. doi:10.1111/2041-210X.12760.

Wang, Ning, Edward L. Braun, and Rebecca T. Kimball. 2012. “Testing Hypotheses About the Sister Group of the Passeriformes Using an Independent 30-Locus Data Set.” Molecular Biology and Evolution 29 (2): 737–50. doi:10.1093/molbev/msr230.