treespace/0000755000176200001440000000000013231640221012217 5ustar liggesuserstreespace/inst/0000755000176200001440000000000013213466220013201 5ustar liggesuserstreespace/inst/shiny/0000755000176200001440000000000013164413032014331 5ustar liggesuserstreespace/inst/shiny/ui.R0000644000176200001440000014451313225424527015112 0ustar liggesusersoptions(rgl.useNULL=TRUE) ## CHECKS ## require("scatterD3") require("shiny") require("rgl") require("RLumShiny") require("shinyBS") ## DEFINE UI ## shinyUI( navbarPage("",position="fixed-top", collapsible = TRUE, theme = "bootstrap.simplex.css", tabPanel("Tree landscape explorer", tags$link(rel = 'stylesheet', type = 'text/css', href = 'styles.css'), tags$style(type="text/css", "body {padding-top: 40px;}"), pageWithSidebar( ## TITLE ## headerPanel( img(src="img/logo.png", height="160") ), ## SIDE PANEL CONTENT ## sidebarPanel( tags$head(tags$style( type = 'text/css', 'form.well { max-height: 1600px; overflow-y: auto; }' )), ## SPECIFIC TO TREE LANDSCAPE EXPLORER ## conditionalPanel(condition = "$('li.active a').first().html()== 'Tree landscape explorer'", ## INPUT ## choice of type of data source img(src="img/line.png", width="100%"), h2(HTML(' > Input ')), radioButtons("datatype", HTML(' Choose data source:'), list("Example: Dengue fever"="exDengue", "Example: Woodmice"="exWoodmice", "Input file"="file")), ## choice of dataset if source is a file conditionalPanel(condition = "input.datatype=='file'", fileInput("datafile", p(HTML(' Choose input file:'), br(), strong("accepted formats:"), br(), em("- multiphylo"), "saved from R (.RData/.rda/.rds)", br(), em("- nexus"), "file (.nex/.nexus)") ), checkboxInput("randSamp","Randomly sample from the trees?", value=TRUE), bsTooltip("randSamp", "For large sets of trees and/or trees with many tips the app may be slow, so beginning the analysis with a small random sample is recommended.", placement = "right", trigger = "hover", options = NULL), conditionalPanel( condition="input.randSamp", sliderInput("sampleSize", "Size of random sample:", value=10, min=10, max=300, step=10) ) ), ## ANALYSIS img(src="img/line.png", width="100%"), h4(HTML(' > Analysis ')), ## choose metric selectInput("treemethod", "Choose a tree summary:", choices=c( "Kendall Colijn (rooted)" = "metric", "Billera, Holmes, Vogtmann (rooted, uses branch lengths)" = "BHV", "Kuhner & Felsenstein branch score distance (unrooted, uses branch lengths)" = "KF", "Robinson Foulds symmetric difference (unrooted, topological)" = "RF", "Weighted Robinson Foulds (unrooted, uses branch lengths)" = "wRF", "Steel & Penny tip-tip distance (unrooted, topological)" = "nNodes", "Steel & Penny weighted tip-tip distance (unrooted, uses branch lengths)" = "patristic", "Abouheif test (rooted, topological)" = "Abouheif", "Sum of direct descendents (rooted, topological)" = "sumDD")), ## lambda, axes uiOutput("lambda"), bsTooltip("lambda","When lambda=0 trees are compared topologically; increasing lambda gives more focus to branch lengths"), conditionalPanel( condition="input.plotType==1", uiOutput("naxes") ## Future: highlight median trees (if plotType==1) #checkboxInput("showMedians", label=strong("Highlight median tree(s)?"), value=FALSE) ), ## show Shepard plot? checkboxInput("quality", label=strong("Assess quality of projection (Shepard plot)?"), value=FALSE), bsTooltip("quality","A Shepard plot gives an indication of the quality of the MDS projection. It will be displayed below the main plot.", placement="right"), ## show screeplot? conditionalPanel( condition="input.graphics==1", checkboxInput("scree", label=strong("Show screeplot?"), value=FALSE), bsTooltip("scree","Display screeplot of the eigenvalues associated with each componenet? It will be displayed below the main plot.", placement="right") ), ## find clusters? checkboxInput("findClusters", label=strong("Identify clusters?"), value=FALSE), bsTooltip("findClusters","Statistical tools for choosing an appropriate clustering method and number of clusters will be added to treespace soon.", placement="right"), conditionalPanel(condition ="input.findClusters", radioButtons("clusterType", label="Method:", choices=c("statistically"="stat","by metadata"="meta"), selected="stat"), conditionalPanel( condition="input.clusterType=='stat'", ## clustering method selectInput("clustmethod", "Clustering method:", choices=c( "Ward" = "ward.D2", "Single" = "single", "Complete" = "complete", "UPGMA" = "average")), ## number of clusters uiOutput("nclust") ), conditionalPanel( condition="input.clusterType=='meta'", fileInput("metadatafile", p(HTML(' Choose input file:'), br(), strong("accepted formats:"), br(), em("- object of class factor/numeric/character/list"), "saved from R (.RData/.rda)", br(), em("- csv file"), "(.csv) (first column will be used)") ) ) ), ## relevant if method = KC metric, allow tip emphasis conditionalPanel( condition="input.treemethod=='metric'", ## Emphasise tips checkboxInput("emphTips", label=strong("Emphasise tips?"), value=FALSE), bsTooltip("emphTips","Choose tips to emphasise or de-emphasise: the vector elements corresponding to these tips are multiplied by the weight chosen below.", placement="right"), ## if tip emphasis is chosen, provide options: conditionalPanel( condition="input.emphTips", uiOutput("whichTips"), sliderInput("emphWeight", "Weight of emphasis", value=2,min=0.1,max=100) ) ), ## AESTHETICS img(src="img/line.png", width="100%"), h2(HTML(' > Aesthetics ')), ## tree landscape or compare to single reference tree conditionalPanel( condition="input.plot3D==2", radioButtons("plotType", "View", choices=c("Full tree landscape"=1,"Distances from a reference tree"=2), selected=1), bsTooltip("plotType", "Choose whether to view the relative distances between all trees, or a 1-dimensional plot of their distances from a fixed reference tree") ), ## Dimensions (3D possible if 3 or more axes retained, and full tree landscape) conditionalPanel(condition="input.naxes>2", conditionalPanel( condition="input.plotType==1", radioButtons("plot3D", "Plot dimensions", choices=c("2D"=2,"3D"=3), selected=2) ) ), conditionalPanel( condition="(input.plot3D==2)&&(input.plotType==1)", radioButtons("graphics", "Display using", choices=c("plotGrovesD3"=1,"plotGroves"=2), selected=1), bsTooltip("graphics", "Choose whether to view the tree landscape using plotGrovesD3 which uses scatterD3 (interactive html) or plotGroves which uses adegraphics") ), # if plotType=1, pick the axes to view: conditionalPanel( condition="input.plotType==1", ## select first axis to plot numericInput("xax", "Indicate the x axis", value=1, min=1, max=3), ## select second axis to plot numericInput("yax", "Indicate the y axis", value=2, min=1, max=3), bsTooltip("yax", "If multiple MDS axes have been retained, any combination of axes can be viewed"), ## if in 3D, need a z axis: conditionalPanel(condition="input.plot3D==3", numericInput("zax", "Indicate the z axis", value=3, min=1, max=3) ) ), ## aesthetics for tree landscape view conditionalPanel( condition="input.plotType==1", conditionalPanel( condition="(input.plot3D==2)&&(input.graphics==1)", ## Animate transitions? checkboxInput("transitions", label="Animate transitions?", value=TRUE) ), conditionalPanel( condition="(input.plot3D==2)&&(input.graphics==2)", ## convex hulls or ellipses when clusters identified conditionalPanel( condition="input.findClusters", radioButtons("scattertype", "Type of scatterplot", choices=c("chull","ellipse"), selected="chull") ), selectInput("screemds", "Position of the MDS screeplot:", choices=c("None" = "none", "Bottom right" = "bottomright", "Bottom left" = "bottomleft", "Top right" = "topright", "Top left" = "topleft"), selected="bottomleft") ), ## symbol size sliderInput("pointsize", "Size of the points", value=2, min=0, max=10, step=0.2), conditionalPanel( condition="(input.plot3D==2)&&(input.graphics==1)", ## symbol size sliderInput("pointopacity", "Opacity of the points", value=0.6, min=0, max=1, step=0.05) ), conditionalPanel( condition="input.plot3D==2", ## display labels checkboxInput("showlabels", label="Display tree labels?", value=FALSE), conditionalPanel( condition="input.showlabels", ## label size sliderInput("labelsize", "Size of the labels", value=1, min=0, max=10, step=1), conditionalPanel( condition="input.graphics==2", checkboxInput("optimlabels", label="Optimize label position?", value=FALSE) ) ) ) ), # if plotType=2, option to stretch conditionalPanel( condition="input.plotType==2", uiOutput("selectedRefTree"), sliderInput("stretch", "Height of plot (pixels)", value=1600, min=800, max=12800, step=200) ), ## choose color palette (if clusters detected) conditionalPanel( ## condition condition="input.findClusters", selectInput("palette", "Palette for the clusters", choices=c("funky", "spectral", "seasun", "lightseasun", "deepseasun", "rainbow", "azur", "wasp"), selected="funky") ), conditionalPanel( condition="input.plotType==1", ## choose label colors jscolorInput("labcol", "Label / point color", value="#1B2266", close=TRUE) ), br(),br(),br(),br(),br(),br(),br(), # add some blank space at the end of side panel br(),br(),br(),br(),br(),br(),br(), # add some blank space at the end of side panel width=4)), # end conditional panel and sidebarPanel; width is out of 12 ## MAIN PANEL mainPanel("", # TITLE # h2(HTML(' Tree landscape explorer ')), br(),br(), ## function I was using for testing: #verbatimTextOutput("plot_click"), # Removed: #verbatimTextOutput("caption"), conditionalPanel( condition="input.plot3D==2", uiOutput("treespacePlot") ), conditionalPanel( condition="input.plot3D==3", rglwidgetOutput("treespacePlot3D", width="800px") ), conditionalPanel( condition="(input.plotType==1)&&(input.plot3D==2)&&(input.graphics==1)", tags$p(actionButton("scatterD3-reset-zoom", HTML(" Reset Zoom"))) ), conditionalPanel( condition="input.quality", uiOutput("shep") ), conditionalPanel( condition="(input.scree)&&(input.graphics==1)", uiOutput("scree") ), br(), br(), ## OUTPUT (save) img(src="img/line.png", width="400px"), h2(HTML(' > Output ')), ## save MDS plot conditionalPanel( condition="(input.plotType==1)&&(input.plot3D==2)&&(input.graphics==1)", tags$p(tags$a(id = "scatterD3-svg-export", href = "#", class = "btn btn-default", HTML(" Save treespace plot as svg"))), downloadButton("downloadMDS2Dhtml", "Save treespace plot as interactive html") ), conditionalPanel( condition="(input.plotType==1)&&(input.plot3D==2)&&(input.graphics==2)", downloadButton("downloadMDS", "Save treespace image as png file") ), conditionalPanel( condition="input.plot3D==3", downloadButton("downloadMDS3Dhtml", "Save treespace 3D plot as interactive html") ), conditionalPanel( condition="input.quality", downloadButton("downloadShep", "Save Shepard plot as png file") ), conditionalPanel( condition="input.scree", downloadButton("downloadScree", "Save screeplot as png file") ), ## save trees to nexus file downloadButton('exporttrees', "Save trees to nexus file"), ## save results to csv downloadButton('exportrestocsv', "Save results (MDS+clusters) to csv file"), ## save results to RData downloadButton('exportrestordata', "Save results (MDS+clusters) to R object") ) # end mainPanel ) # end page with sidebar ), # end tabPanel tabPanel("Tree viewer", tags$link(rel = 'stylesheet', type = 'text/css', href = 'styles.css'), tags$style(type="text/css", "body {padding-top: 40px;}"), pageWithSidebar( ## TITLE ## headerPanel( img(src="img/logo.png", height="160") ), ## SIDE PANEL CONTENT ## sidebarPanel( tags$head(tags$style( type = 'text/css', 'form.well { max-height: 1600px; overflow-y: auto; }' )), ## INPUT ## choice of tree type img(src="img/line.png", width="100%"), h2(HTML(' > Input ')), h2(HTML(' >> Tree view ')), radioButtons("treePlotType", "View", choices=c("Single tree"=1,"Two tree comparison"=2), selected=1, width="100%"), bsTooltip("treePlotType", "Choose whether to view a single tree or two trees side by side with their differences highlighted."), h2(HTML(' >> Tree selection ')), conditionalPanel(condition = "input.treePlotType==1", radioButtons("treeChoice", "Selection", choices=c("Median tree"="med","General tree selection"="gen"), selected="med", width="100%"), bsTooltip("treeChoice", "A geometric median tree is plotted by default. If clusters have been identified, the median for each can be viewed. Alternatively, any individual tree can be plotted."), conditionalPanel(condition = "input.treeChoice=='med'", selectInput("selectedMedTree", "Median tree from:", choices=c("All trees"="all")) ), conditionalPanel(condition = "input.treeChoice=='gen'", uiOutput("selectedGenTree") ) ), # end single tree choice conditionalPanel(condition = "input.treePlotType==2", radioButtons("treeChoice1", "Select first tree", choices=c("Median tree"="med","General tree selection"="gen"), selected="med", width="100%"), bsTooltip("treeChoice1", "Plot a geometric median tree or any individual tree"), conditionalPanel(condition = "input.treeChoice1=='med'", selectInput("selectedMedTree1", "Median tree from:", choices=c("All trees"="all")) ), conditionalPanel(condition = "input.treeChoice1=='gen'", uiOutput("selectedGenTree1") ), radioButtons("treeChoice2", "Select second tree", choices=c("Median tree"="med","General tree selection"="gen"), selected="med", width="100%"), bsTooltip("treeChoice2", "Plot a geometric median tree or any individual tree"), conditionalPanel(condition = "input.treeChoice2=='med'", selectInput("selectedMedTree2", "Median tree from:", choices=c("All trees"="all")) ), conditionalPanel(condition = "input.treeChoice2=='gen'", uiOutput("selectedGenTree2") ), checkboxInput("showTipDiffTable", label="Display table of tip differences?", value=FALSE) ), # end tree comparison choices ## TREE AESTHETICS img(src="img/line.png", width="100%"), h2(HTML(' > Aesthetics ')), ## condition on tree being displayed conditionalPanel(condition = "input.selectedTree!=''", ## use edge lengths? checkboxInput("edgelengths", label="Use original branch lengths?", value=TRUE), ## ladderize checkboxInput("ladderize", label="Ladderize the tree(s)?", value=TRUE), ## type of tree radioButtons("treetype", "Type of tree", choices=c("phylogram","cladogram", "fan", "unrooted", "radial"), selected="phylogram", width="100%"), conditionalPanel(condition = "input.treePlotType==1", ## tree direction radioButtons("treedirection", "Direction of the tree", choices=c("rightwards", "leftwards", "upwards", "downwards"), selected="rightwards", width="100%") ), conditionalPanel(condition = "input.treePlotType==2", ## trees facing? radioButtons("treesFacing", "Direction of trees", choices=c("facing"=T, "rightwards"=F), selected=T, width="100%") ), ## tip labels checkboxInput("showtiplabels", label="Display tip labels?", value=TRUE), conditionalPanel(condition="input.showtiplabels", ## tip label font selectInput("tiplabelfont", "Tip label font", choices=c("Plain"=1,"Bold"=2,"Italic"=3,"Bold italic"=4), selected=1), ## tip label size sliderInput("tiplabelsize", "Size of the tip labels", value=1, min=0, max=5, step=0.1), conditionalPanel(condition="input.treePlotType==1", ## tip label colour jscolorInput("tiplabelcolour", "Tip label colour", value="#000000", close=TRUE) ), conditionalPanel(condition="input.treePlotType==2", selectInput("colourMethod", "Colouring method", choices=c("Gradual colour ramp"=1,"Palette from adegenet"=2), selected=1), ## basic tip label colour jscolorInput("basetiplabelcolour", "Label colour for tips with same ancestry", value="#BEBEBE", close=TRUE), conditionalPanel(condition="input.colourMethod==1", ## basic tip label colour jscolorInput("minortiplabelcolour", "Label colour for tips with smaller ancestral differences", value="#FFDAB9", close=TRUE), ## basic tip label colour jscolorInput("majortiplabelcolour", "Label colour for tips with greater ancestral differences", value="#EE0000", close=TRUE) ), conditionalPanel(condition="input.colourMethod==2", selectInput("tipPalette", "Palette", choices=c("funky"=1, "spectral"=2, "seasun"=3, "lightseasun"=4, "deepseasun"=5, "rainbow"=6, "azur"=7, "wasp"=8), selected=2) ) ) ), ## edge width sliderInput("edgewidth", "Width of the edges", value=2, min=1, max=20, step=0.2), ## edge colour jscolorInput("edgecolor", "Edge colour", value="#000000", close=TRUE), br(),br(),br(),br(),br(),br(),br(), # add some blank space at the end of side panel br(),br(),br(),br(),br(),br(),br(), # add some blank space at the end of side panel width=4)), # end conditional panel and sidebarPanel; width is out of 12 ## MAIN PANEL mainPanel("", # TITLE # h2(HTML(' Tree viewer ')), br(),br(), ## conditional panel: plot single tree if needed conditionalPanel(condition = "(input.treePlotType==1)&&(input.selectedTree!='')", plotOutput("tree", height = "800px"), br(), br(), ## OUTPUT (save) img(src="img/line.png", width="400px"), h2(HTML(' > Output ')), downloadButton("downloadTree", "Save tree image"), br(), br() ), # end single tree conditional panel ## conditional panel: plot tree comparison if needed conditionalPanel(condition = "(input.treePlotType==2)&&(input.selectedTree1!='')&&(input.selectedTree2!='')", plotOutput("treeDiff", height = "800px"), # conditional panel: show tip differences table: conditionalPanel(condition = "input.showTipDiffTable", tableOutput("tipDiffTable") ), br(), br(), ## OUTPUT (save) img(src="img/line.png", width="400px"), h2(HTML(' > Output ')), downloadButton("downloadTreeDiff", "Save tree comparison image"), # conditional panel: show tip differences table: conditionalPanel(condition = "input.showTipDiffTable", downloadButton("downloadTipDiffTable", "Save tip differences table") ), br(), br() ), # end tree comparison conditional panel ## Repeat of treespace plot, for reference img(src="img/line.png", width="400px"), h2(HTML(' > Copy of scatter plot ')), br(), br(), uiOutput("treespacePlotTreeTab") ) # end mainPanel ) # end page with sidebar ), # end tabPanel "Tree Viewer" tabPanel("densiTree viewer", tags$link(rel = 'stylesheet', type = 'text/css', href = 'styles.css'), tags$style(type="text/css", "body {padding-top: 40px;}"), pageWithSidebar( ## TITLE ## headerPanel( img(src="img/logo.png", height="160") ), ## SIDE PANEL CONTENT ## sidebarPanel( tags$head(tags$style( type = 'text/css', 'form.well { max-height: 1600px; overflow-y: auto; }' )), ## INPUT ## choice of tree type img(src="img/line.png", width="100%"), h2(HTML(' > Input ')), ## add densiTree selector (gets updated to number of clusters by ) selectInput("selectedDensiTree", "Choose collection of trees to view in densiTree plot", choices=c("Choose one"="","All trees"="all"), width="100%"), #h2(HTML(' Note: this can be slow for large sets of trees ')), bsTooltip("selectedDensiTree", "View all trees together in a densiTree plot. If clusters have been identified, the set of trees from a single cluster can be plotted. Note this function can be slow if many trees are included.", placement="bottom"), ## DENSITREE AESTHETICS img(src="img/line.png", width="100%"), h2(HTML(' > Aesthetics ')), conditionalPanel(condition = "input.selectedDensiTree!=''", ## alpha (semitransparency of edges) sliderInput("alpha", "Transparency of edges", value=0.5, min=0, max=1, step=0.05), checkboxInput("scaleX", label="Scale trees to equal heights?", value=FALSE), br(),br(),br(),br(),br(),br(),br(), # add some blank space at the end of side panel br(),br(),br(),br(),br(),br(),br(), # add some blank space at the end of side panel width=4)), # end conditional panel and sidebarPanel; width is out of 12 ## MAIN PANEL mainPanel("", # TITLE # h2(HTML(' densiTree viewer ')), br(),br(), ## conditional panel: plot tree if needed conditionalPanel(condition = "input.selectedDensiTree!=''", plotOutput("densiTree", height = "800px"), br(), br(), ## OUTPUT (save) img(src="img/line.png", width="400px"), h2(HTML(' > Output ')), downloadButton("downloadDensiTree", "Save densiTree image"), br(), br(), br(), br(), br(), br() ) # end densiTree conditional panel ) # end of main panel "Multi-tree viewer" ) # end of page with sidebar ),# end of tabPanel "densiTree viewer" ## HELP SECTION tabPanel("Help", tags$style(type="text/css", "body {padding-top: 40px;}"), HTML(paste(readLines("www/html/help.html"), collapse=" ")) ), ## SERVER INFO ## tabPanel("System info", tags$style(type="text/css", "body {padding-top: 40px;}"), verbatimTextOutput("systeminfo")) ) # end of tabsetPanel ) # end of Shiny UI treespace/inst/shiny/server.R0000644000176200001440000011174513225424306015777 0ustar liggesusers## DEFINE THE SERVER SIDE OF THE APPLICATION shinyServer(function(input, output, session) { ## LOAD PACKAGES require("ade4") require("adegenet") require("adegraphics") require("ape") require("distory") require("fields") require("htmlwidgets") require("MASS") require("phangorn") require("treespace") # suppress warning messages from creating temporary directories when 3d plotting suppressWarnings(warning("dir.create(dir)")) # the following resets the DensiTree plot every time the number of clusters changes - it was really slow without this rvs <- reactiveValues(showDensiTree=NULL) observeEvent(input$nclust, { rvs$showDensiTree <- NULL }) observeEvent(input$selectedDensiTree, { rvs$showDensiTree <- 1 }) ###################################### ### Define main reactive functions ###################################### getDataType <- reactive({ input$datatype }) getDataSet <- reactive({ dataType <- getDataType() if(dataType=="exDengue"){ return("Dengue") } if(dataType=="exWoodmice"){ return("woodmiceTrees") } else { # extract file name strsplit(input$datafile$name, '[.]')[[1]][1] } }) getSampleSize <- reactive({ input$sampleSize }) getRandSamp <- reactive({ input$randSamp }) ## GET DATA ## getData <- reactive({ out <- NULL dataType <- getDataType() samp <- NULL ## data is a distributed dataset if(dataType=="exDengue"){ if (!exists("DengueTrees")) { data("DengueTrees", package="treespace", envir=environment()) } out <- get("DengueTrees") } if(dataType=="exWoodmice"){ if (!exists("woodmiceTrees")) { data("woodmiceTrees", package="treespace", envir=environment()) } out <- get("woodmiceTrees") } ## data is an input file if(dataType=="file" && !is.null(input$datafile)){ ## need to rename input file oldName <- input$datafile$datapath extension <- adegenet::.readExt(input$datafile$name) newName <- paste(input$datafile$datapath, extension, sep=".") file.rename(oldName, newName) if(tolower(extension) %in% c("rdata","rda")){ out <- get(load(newName)) } if(tolower(extension) %in% c("rds")){ out <- readRDS(file=newName) } if(tolower(extension) %in% c("nex", "nexus")){ if(!require(ape)) stop("ape is required to read in NEXUS (.nex, .nexus) files") out <- read.nexus(file=newName) } l <- length(out) ## fix potential bug with input of two trees validate( need(l>2, "treespace expects at least three trees. The function treeDist is suitable for comparing two trees.") ) # get a manageable number of trees by sampling if necessary randSamp <- getRandSamp() if(randSamp == TRUE){ sampleSize <- getSampleSize() if (l>sampleSize) { updateSliderInput(session, "sampleSize", "Size of random sample:", value=sampleSize, min=10, max=l, step=10) samp <- sample(1:l,sampleSize) out <- out[samp] } else{ # could only happen initially if <=10 trees supplied updateSliderInput(session, "sampleSize", "Size of random sample:", value=l, min=3, max=l, step=1) } } ## fix potential bug with tip labels - they need to match tipLabelProblem <- FALSE for (i in 1:length(out)) { if (!setequal(out[[i]]$tip.label,out[[1]]$tip.label)) { tipLabelProblem <- TRUE validate( need(!tipLabelProblem, "Trees must have identical tip labels for the current version of treespace") ) } } } validate( need(!is.null(out), "Waiting for data") ) ## fix potential bug with names - they need to be defined and unique if(is.null(names(out))) {names(out) <- 1:length(out)} if(length(unique(names(out)))!=length(out)){ warning("duplicates detected in tree labels - using generic names") names(out) <- 1:length(out) } ## return data # need to pass on the sample so that metaData can be sampled too if(is.null(samp)) samp <- 1:length(out) return(list(out=out,samp=samp)) }) # end getData ## GET number of trees getLengthData <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) return(length(x)) }) ## GET tree names getTreeNames <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) return(names(x)) }) ## GET tip labels getTipLabels <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) return(x[[1]]$tip.label) }) ## GET tree method getTreemethod <- reactive({ input$treemethod }) # end getTreemethod ## GET number of axes retained getNaxes <- reactive({ if(is.null(input$naxes)){ naxes <- 3 } else { naxes <- as.numeric(input$naxes) # when naxes changes we update the options available for the axes # unfortunately I think they have to reset to their original 1,2,3 values # but at least they now only do this when naxes changes; they used to also do it for lambda etc. updateNumericInput(session,"xax", "Indicate the x axis", value=1, min=1, max=naxes) updateNumericInput(session,"yax", "Indicate the y axis", value=2, min=1, max=naxes) # (if relevant, update z axis selector too) dim <- getPlotDim() if (dim==3){ updateNumericInput(session,"zax", "Indicate the z axis", value=3, min=1, max=naxes) } } return(naxes) }) # end getNaxes ## GET lambda getLambda <- reactive({ l <- input$lambda ## the following removes the lambda error messages: validate( need(!is.null(l), "Loading data set") ) return(l) }) # end getLambda getTipsToEmphasise <- reactive({ input$whichTips }) getEmphWeight <- reactive({ input$emphWeight }) # GET the tree vectors as functions of lambda getKCtreeVecs <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) tips <- getTipsToEmphasise() weight <- getEmphWeight() df <- sapply(x, function(i) treeVec(i, return.lambda.function=TRUE, emphasise.tips=tips, emphasise.weight = weight)) }) # GET the tree vectors evaluated at lambda getKCtreeVecsAtLambda <- reactive({ vectors <- getKCtreeVecs() l <- getLambda() validate( need(!is.null(vectors), "Analysing data") ) t(sapply(vectors, function(i) i(l))) }) ## GET KC matrix, evaluated at lambda getKCmatrix <- reactive({ vls <- getKCtreeVecsAtLambda() as.dist(rdist(vls)) }) # end getKCmatrix ## GET medTrees for all clusters getMedTreesList <- reactive({ mat <- getKCtreeVecsAtLambda() groves <- getClusters() if(!is.null(groves$groups)){ # if clusters have been picked numGroups <- length(unique(groves$groups)) med <- medTree(mat,groves$groups) lapply(1:numGroups, function(x) med[[x]]$treenumbers[[1]]) } else{ medTree(mat)$treenumbers[[1]] } }) getMedTree <- reactive({ data <- getData() x <- data$out whichClust <- input$selectedMedTree medList <- getMedTreesList() if(whichClust=="all"){ x[[medList[[1]]]] } else{ x[[medList[[as.numeric(whichClust)]]]] } }) getMedTree1 <- reactive({ data <- getData() x <- data$out whichClust <- input$selectedMedTree1 medList <- getMedTreesList() if(whichClust=="all"){ x[[medList[[1]]]] } else{ x[[medList[[as.numeric(whichClust)]]]] } }) getMedTree2 <- reactive({ data <- getData() x <- data$out whichClust <- input$selectedMedTree2 medList <- getMedTreesList() if(whichClust=="all"){ x[[medList[[1]]]] } else{ x[[medList[[as.numeric(whichClust)]]]] } }) ## GET PCO analysis ## getPCO <- reactive({ D <- getKCmatrix() naxes <- getNaxes() validate( need(!is.null(D), "Analysing data") ) validate( need(!is.null(naxes), "Analysing data") ) dudi.pco(D,scannf=FALSE,nf=naxes) }) # end getPCO ## GET ANALYSIS ## getAnalysis <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) naxes <- getNaxes() TM <- getTreemethod() ## select method used to summarise tree if(!is.null(TM)){ if(TM %in% c("BHV","KF","RF","wRF","patristic","nNodes","Abouheif","sumDD")){ ## run treespace (suppress warnings about rootedness etc.) res <- suppressWarnings(treespace(x, method=TM, nf=naxes)) } else if(TM=="metric"){ ## don't actually need to call treespace here, to save on recomputation for varying lambda D <- getKCmatrix() pco <- getPCO() res <- list(D=D, pco=pco) } } ## return results return(res) }) # end getAnalysis ################################################# ### Little "get" functions to support getClusters ################################################# getNclust <- reactive({ if(!is.null(input$nclust)) { input$nclust } else { 2 } }) getClustmethod <- reactive({ input$clustmethod }) ################ ## GET CLUSTERS ################ getClusters <- reactive({ ## stop if clusters not required if(!input$findClusters) return(NULL) else if(input$clusterType=="meta") return(NULL) ## reset the densiTree plot to accommodate number of clusters available choices <- getClustChoices() updateSelectInput(session, "selectedDensiTree", "Choose collection of trees to view in densiTree plot", choices=choices, selected="") ## reset the median tree choices to accommodate number of clusters available updateSelectInput(session, "selectedMedTree", "Median tree from:", choices=choices, selected="all") updateSelectInput(session, "selectedMedTree1", "Median tree from:", choices=choices, selected="all") updateSelectInput(session, "selectedMedTree2", "Median tree from:", choices=choices, selected="all") ## get dataset data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) naxes <- getNaxes() TM <- getTreemethod() nclust <- getNclust() clustmethod <- getClustmethod() ## select method used to summarise tree if(!is.null(TM)){ if(TM %in% c("BHV","RF","KF","patristic","nNodes","Abouheif","sumDD")){ ## run findGroves res <- findGroves(x, method=TM, nf=naxes, nclust=nclust, clustering=clustmethod) } else if(TM=="metric"){ res <- findGroves(getAnalysis(), nclust=nclust, clustering=clustmethod) } } ## return results return(res) }) # end getClusters ## DYNAMIC UI COMPONENTS ## ## SELECTION OF MDS AXES output$naxes <- renderUI({ if(!is.null(getLengthData())) { nmax <- getLengthData() } else { nmax <- 100 } sliderInput("naxes", "Number of MDS axes retained:", min=2, max=nmax, value=3, step=1) }) ## VALUE OF LAMBDA FOR METRIC output$lambda <- renderUI({ ## if KC metric has been chosen TM <- getTreemethod() if(TM=="metric") { sliderInput("lambda", "Value of lambda", min=0, max=1, value=0, step=0.01) } else { NULL } }) ## SELECTION OF NUMBER OF CLUSTERS output$nclust <- renderUI({ if(!is.null(data <- getData())) { nmax <- length(data$out) } else { nmax <- 100 } nmax <- min(20, nmax) sliderInput("nclust", "Number of clusters:", min=2, max=nmax, value=2, step=1) }) ## SELECTION OF TIPS output$whichTips <- renderUI({ # populate selection box with tip choices allTips <- getTipLabels() choices <- c("",allTips) names(choices) <- c("Type here to search tip names",allTips) selectInput("whichTips", "Select one or more tips to emphasise:", choices=choices, selected=NULL, selectize=TRUE, multiple=TRUE) }) ## GET METADATA ## for colouring trees by type getMetaData <- reactive({ out <- NULL data <- getData() samp <- data$samp ## data is an input file if(input$clusterType=="meta" && !is.null(input$metadatafile)){ ## need to rename input file oldName <- input$metadatafile$datapath extension <- adegenet::.readExt(input$metadatafile$name) newName <- paste(input$metadatafile$datapath, extension, sep=".") file.rename(oldName, newName) if(tolower(extension) %in% c("rdata","rda")){ out <- get(load(newName)) validate( need(class(out)%in%c("numeric","character","list","factor"), paste0("The class of the input is ", class(out), ". Please upload a single object of class list, numeric, factor or character, whose length is the same as the number of trees.")) ) } if(tolower(extension) %in% c("csv")){ csvfile <- read.csv(file=newName, header=FALSE) out <- csvfile[,1] validate( need(class(out)%in%c("numeric","character","list","factor"), paste0("The first column of the csv file has been extracted. However, the class of the input is ", class(out), ". Please alter the entries so that it can be read by R as an object of class list, numeric, factor or character, whose length is the same as the number of trees.")) ) } if(class(out)=="list") {out <- unlist(out)} l <- getLengthData() out <- out[samp] validate( need(length(out)==l, paste0("The length of the metadata must be the same as the number of trees, which is ", l, ". However, the length of the input is ", length(out))) ) } ## return metadata return(out) }) # end getMetaData ###################################################### ### Little "get" functions to support getPlot ###################################################### getPalette <- reactive({ get(input$palette) }) getLabcol <- reactive({ ifelse(!is.null(input$labcol), input$labcol, "black") }) getBgcol <- reactive({ ifelse(!is.null(input$bgcol), input$bgcol, "white") }) getXax <- reactive({ input$xax }) getYax <- reactive({ input$yax }) getZax <- reactive({ input$zax }) getShowlabels <- reactive({ input$showlabels }) getLabelsize <- reactive({ input$labelsize }) getPointsize <- reactive({ input$pointsize }) getPlotFunction <- reactive({ input$graphics }) ############## ## GET plot ############## ## GET whether plot is 2D (default) or 3D getPlotDim <- reactive({ plotDim <- input$plot3D if(is.null(plotDim)) {2} # needed during startup else {return(plotDim)} }) ## GET 2D plot getPlot <- reactive({ res <- getAnalysis() pal <- getPalette() labcol <- getLabcol() groves <- getClusters() treeTypes <- getMetaData() showlabels <- getShowlabels() pointSize <- getPointsize() if(!is.null(treeTypes)) { groups <- treeTypes cols <- fac2col(1:length(unique(groups)),col.pal=pal) } else if (!is.null(groves)) { groups <- groves$groups cols <- fac2col(1:length(unique(groups)),col.pal=pal) } else { groups <- NULL n <- getLengthData() cols <- rep(labcol, n) } ## get aesthetics xax <- getXax() yax <- getYax() plotFunction <- getPlotFunction() if (plotFunction==1) { transitions <- input$transitions # labels and tree names treeNames <- getTreeNames() if (is.null(groups)) { tooltips <- paste0("Tree ", treeNames) } else { tooltips <- paste0("Tree ",treeNames,", cluster ",groups) } treeLabels <- NULL labelsize <- NULL if(showlabels==TRUE) { treeLabels <- getTreeNames() labelsize <- getLabelsize() } pointOpacity <- input$pointopacity plot <- plotGrovesD3(res$pco, xax=xax, yax=yax, treeNames=treeLabels, labels_size=labelsize*5, point_size = pointSize*40, point_opacity = pointOpacity, groups=groups, colors=cols, col_lab="Cluster", xlab=paste0("Axis ",xax), ylab=paste0("Axis ",yax), tooltip_text = tooltips, transitions=transitions, legend_width=50 ) # later could add: # other categories of variation e.g. metadata using symbols } else { # i.e. plotFunction==2 bgcol <- getBgcol() scattertype <- input$scattertype screemds <- input$screemds optimlabels <- input$optimlabels labelsize <- getLabelsize() if(is.null(groves)){ plot <- plotGroves(res$pco, groups=treeTypes, type=scattertype, xax=xax, yax=yax, scree.posi=screemds, lab.optim=optimlabels, lab.show=showlabels, lab.cex=labelsize, lab.col=labcol, point.cex=pointSize, bg=bgcol, col.pal=pal) } else { ## plot with statistically identified groups plot <- plotGroves(groves, type=scattertype, xax=xax, yax=yax, scree.posi=screemds, lab.optim=optimlabels, lab.show=showlabels, lab.cex=labelsize, lab.col=labcol, point.cex=pointSize, bg=bgcol, col.pal=pal) } } return(plot) }) getDistPlot <- reactive({ res <- getAnalysis() refTree <- input$selectedRefTree validate( need(refTree!="", "Select a reference tree") ) groves <- getClusters() treeNames <- getTreeNames() pal <- getPalette() dists <- as.matrix(res$D)[refTree,] g1 <- s1d.label(dists, labels=treeNames, poslabel="regular", p1d.horizontal=FALSE, p1d.reverse=TRUE, plot=FALSE) if(!is.null(groves$groups)){ pal <- getPalette() nclusts <- getNclust() ordercols <- fac2col(1:nclusts, col.pal=pal) g2 <- s1d.boxplot(dists,fac=groves$groups, col=ordercols, p1d.horizontal=FALSE, plot=FALSE) ADEgS(c(g1, g2), layout = c(1, 2)) } else{ g1 } }) getPlotType <- reactive({ input$plotType }) ## TREESPACE IMAGE ## output$treespacePlot <- renderUI({ type <- getPlotType() if (type==1){ # i.e. full tree landscape plotFunction <- getPlotFunction() if (plotFunction==1) { # i.e. scatterD3 scatterD3Output("scatterplotD3") } else { # i.e. adegraphics plotOutput("scatterplot", height = "800px") } } else{ # i.e. distance from reference tree plot i <- input$stretch height <- as.character(paste0(i,"px")) plotOutput("DistPlot", height = height) } }) # repeat treespacePlot for tree viewer tab output$scatterplotD3TreeTab <- renderScatterD3({ plotFunction <- getPlotFunction() # need to do this or you get an error when switching between plotGroves and plotGrovesD3 if (plotFunction==1) { withProgress(message = 'Loading plot', value = 0, { for (i in 1:15) { incProgress(1/15) } myplot <- getPlot() myplot }) } }) output$treespacePlotTreeTab <- renderUI({ scatterD3Output("scatterplotD3TreeTab") }) # 3d output output$treespacePlot3D <- renderRglwidget({ validate( need(packageVersion("rgl")>='0.96.0', paste0("You are running version ",packageVersion("rgl")," of the package rgl, which may not contain all the necessary features for 3D plotting (which are based on the old, separate rglwidget package). Please update to the latest version.") )) plot <- getPlot3d() plot rglwidget() }) output$scatterplotD3 <- renderScatterD3({ plotFunction <- getPlotFunction() # need to do this or you get an error when switching between plotGroves and plotGrovesD3 if (plotFunction==1) { withProgress(message = 'Loading plot', value = 0, { for (i in 1:15) { incProgress(1/15) } myplot <- getPlot() myplot }) } }) output$scatterplot <- renderPlot({ plotFunction <- getPlotFunction() # need to do this or you get an error when switching between plotGroves and plotGrovesD3 if (plotFunction==2) { withProgress(message = 'Loading plot', value = 0, { for (i in 1:15) { incProgress(1/15) } myplot <- getPlot() myplot }) } }, res=120) output$DistPlot <- renderPlot({ withProgress(message = 'Loading plot', value = 0, { for (i in 1:15) { incProgress(1/15) } myplot <- getDistPlot() plot(myplot) }) }, res=120) getPlot3d <- reactive({ res <- getAnalysis() xax <- getXax() yax <- getYax() zax <- getZax() col <- getLabcol() # show clusters? clusts <- getClusters() treeTypes <- getMetaData() if (!is.null(clusts)){ pal <- getPalette() cols3d <- fac2col(clusts$groups,col.pal=pal) } else if (!is.null(treeTypes)) { pal <- getPalette() cols3d <- fac2col(treeTypes,col.pal=pal) } else{cols3d <- col} rgl::plot3d(res$pco$li[,xax],res$pco$li[,yax],res$pco$li[,zax], type="s", size=getPointsize(), xlab="",ylab="",zlab="", col=cols3d, add=FALSE) }) ## make Shepard plot getShep <- reactive({ res <- getAnalysis() dim <- getPlotDim() xax <- getXax() yax <- getYax() if (dim==2) { shep <- Shepard(res$D,as.matrix(res$pco$li[,xax],res$pco$li[,yax])) } else { zax <- getZax() shep <- Shepard(res$D,as.matrix(res$pco$li[,xax],res$pco$li[,yax],res$pco$li[,zax])) } }) output$shepPlot <- renderPlot({ withProgress(message = 'Loading Shepard plot', value = 0, { for (i in 1:15) { incProgress(1/15) } shep <- getShep() labcol <- getLabcol() plot(shep, pch=19, cex=0.5, col=labcol, xlab="Distance in tree space", ylab="MDS distance") }) }, res=120) output$shep <- renderUI({ plotOutput("shepPlot", width="800px", height="800px") }) ## make screeplot output$screePlot <- renderPlot({ res <- getAnalysis() labcol <- getLabcol() barplot(res$pco$eig, col=labcol) }, res=120) output$scree <- renderUI({ plotOutput("screePlot") }) # get tree and aesthetics for plotting tree getTreeChoice <- reactive({ input$treeChoice }) getTreeChoice1 <- reactive({ input$treeChoice1 }) getTreeChoice2 <- reactive({ input$treeChoice2 }) getTree <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) treechoice <- getTreeChoice() if(treechoice=="med"){ tre <- getMedTree() } else{ g <- input$selectedGenTree validate( need(g!="", "Select tree to view") ) treeNum <- as.numeric(g) tre <- x[[treeNum]] } # return tree if(!is.null(tre)){ if(input$ladderize){ tre <- ladderize(tre) } return(tre) } else{ NULL } }) getTree1 <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) treechoice <- getTreeChoice1() if(treechoice=="med"){ tre <- getMedTree1() } else{ g <- input$selectedGenTree1 validate( need(g!="", "Select first tree to compare") ) treeNum <- as.numeric(g) tre <- x[[treeNum]] } # return tree if(!is.null(tre)){ if(input$ladderize){ tre <- ladderize(tre) } return(tre) } else{ NULL } }) getTree2 <- reactive({ data <- getData() x <- data$out validate( need(!is.null(x), "Loading data set") ) treechoice <- getTreeChoice2() if(treechoice=="med"){ tre <- getMedTree2() } else{ g <- input$selectedGenTree2 validate( need(g!="", "Select second tree to compare") ) treeNum <- as.numeric(g) tre <- x[[treeNum]] } # return tree if(!is.null(tre)){ if(input$ladderize){ tre <- ladderize(tre) } return(tre) } else{ NULL } }) getTipDiff <- reactive({ tr1 <- getTree1() tr2 <- getTree2() tipDiff(tr1,tr2) }) getTipDiffTable <- reactive({ tipDiff <- getTipDiff() # data frame of the tips with differences: if (!is.null(tipDiff)) { out <- tipDiff[which(tipDiff[,2]!=0),] rownames(out) <- NULL colnames(out) <- c("Tips with ancestral differences","No. of differences") return(out) } else {NULL} }) ## PHYLOGENY ## output$tree <- renderPlot({ tre <- getTree() if(!is.null(tre)){ ## plot tree ## par(mar=rep(2,4), xpd=TRUE) plot(tre, type=input$treetype, use.edge.length=as.logical(input$edgelengths), show.tip.lab=input$showtiplabels, tip.color=input$tiplabelcolour, font=as.numeric(input$tiplabelfont), cex=input$tiplabelsize, direction=input$treedirection, edge.width=input$edgewidth, edge.color=input$edgecolor ) } }) output$treeDiff <- renderPlot({ tr1 <- getTree1() tr2 <- getTree2() tipDiff <- getTipDiff() CM <- c("ramp","palette")[[as.numeric(input$colourMethod)]] tipPal <- c(funky, spectral, seasun, lightseasun, deepseasun, rainbow, azur, wasp)[[as.numeric(input$tipPalette)]] if(!is.null(tr1)&&!is.null(tr2)){ ## plot tree comparison ## #par(mar=rep(2,4), xpd=TRUE) plotTreeDiff(tr1,tr2, tipDiff = tipDiff, baseCol=input$basetiplabelcolour, col1=input$minortiplabelcolour, col2=input$majortiplabelcolour, colourMethod=CM, palette=tipPal, type=input$treetype, use.edge.length=as.logical(input$edgelengths), show.tip.lab=input$showtiplabels, font=as.numeric(input$tiplabelfont), cex=input$tiplabelsize, treesFacing=input$treesFacing, edge.width=input$edgewidth, edge.color=input$edgecolor ) } }) output$tipDiffTable <- renderTable({ table <- getTipDiffTable() }) ## DENSITREE # The slider bar is always at least 2 even when clusters haven't # been requested, so we can't just use getNclust. getNclustForDensiTree <- reactive({ if(input$clusterType=="meta"){NULL} else{input$nclust} }) getClustChoices <- reactive({ nclust <- getNclustForDensiTree() if(is.null(nclust)){ choices <- c("","all") names(choices) <- c("Choose one","All trees") } else{ choices <- c("",1:nclust,"all") names(choices) <- c("Choose one",paste0("Cluster ",1:nclust),"All trees") } return(choices) }) getDensiTree <- reactive({ clusterNo <- input$selectedDensiTree if(clusterNo==""){ NULL } else if(clusterNo=="all"){ data <- getData() x <- data$out medList <- getMedTreesList() med <- x[[medList[[1]]]] return(list(trees=x,con=med)) } else{ data <- getData() x <- data$out clusts <- getClusters() clustTrees <- x[which(clusts$groups==as.numeric(clusterNo))] medList <- getMedTreesList() med <- x[[medList[[as.numeric(clusterNo)]]]] return(list(trees=clustTrees, con=med)) } }) output$densiTree <- renderPlot({ if(is.null(rvs$showDensiTree)) {NULL} else{ withProgress(message = 'Loading densiTree plot', detail = 'Note: the final stage of this process may take a while for large sets of trees', value = 0, { for (i in 1:30) { incProgress(1/30) } clustTrees <- getDensiTree() densiTree(clustTrees$trees, col=4, consensus=clustTrees$con, alpha=input$alpha, scaleX=input$scaleX) }) } }) ## EXPORT TREES ## output$exporttrees <- downloadHandler( filename = function() { paste(getDataSet(), '.nex', sep='') }, content = function(file) { if(!require(ape)) stop("ape is required to save trees into nexus file") data <- getData() x <- data$out if(!is.null(x) && inherits(x, "multiPhylo")) ape::write.nexus(x, file=file) }) ## EXPORT ANALYSIS TO CSV ## output$exportrestocsv <- downloadHandler( filename = function() { paste(getDataSet(), "-analysis", '.csv', sep='') }, content = function(file) { data <- getData() x <- data$out res <- getClusters() if(!is.null(res)){ tab <- cbind.data.frame(res$groups, res$treespace$pco$li) names(tab) <- c("cluster", paste("PC", 1:ncol(res$treespace$pco$li), sep=".")) row.names(tab) <- names(x) } else{ res <- getAnalysis() tab <- res$pco$li names(tab) <- paste("PC", 1:ncol(tab), sep=".") row.names(tab) <- names(x) } if(!is.null(res)) write.csv(tab, file=file) }) ## EXPORT ANALYSIS TO RDATA ## output$exportrestordata <- downloadHandler( filename = function() { paste(getDataSet(), "-analysis", '.RData', sep='') }, content = function(file) { data <- getData() trees <- data$out analysis <- getClusters() if(is.null(analysis)) analysis <- getAnalysis() if(!is.null(analysis)) { save(trees, analysis, file=file) } }) ## EXPORT 2D plotGroves MDS PLOT AS png ## output$downloadMDS <- downloadHandler( filename = function() { paste0(getDataSet(),"scape2D.png") }, content = function(file) { myplot <- getPlot() png(file=file, width = 10, height = 10, units = 'in', res = 500) plot(myplot) dev.off() contentType = 'image/png' } ) ## EXPORT 2D plotGrovesD3 PLOT AS html ## output$downloadMDS2Dhtml <- downloadHandler( filename = function() { paste0(getDataSet(),"scape2D.html") }, content = function(file) { htmlwidgets::saveWidget( getPlot(), file=file, selfcontained = TRUE) }, contentType = 'html' ) ## EXPORT 3D MDS PLOT AS html ## output$downloadMDS3Dhtml <- downloadHandler( filename = function() { paste0(getDataSet(),"scape3D.html") }, content = function(file) { options(rgl.useNULL=FALSE) myplot <- getPlot3d() myplot rglwidget() rgl::writeWebGL(dir=getwd(), filename=file, snapshot=TRUE, width = 500, reuse=TRUE) }, contentType = 'html' ) ## EXPORT SHEPARD PLOT AS PNG ## output$downloadShep <- downloadHandler( filename = function() { paste0(getDataSet(),"Shepard.png") }, content = function(file) { shep <- getShep() labcol <- getLabcol() png(file=file, width = 10, height = 10, units = 'in', res = 500) plot(shep, pch=19, cex=0.5, col=labcol, xlab="Distance in tree space", ylab="Distance on MDS plot") dev.off() }, contentType = 'image/png' ) ## EXPORT SCREEPLOT AS PNG ## output$downloadScree <- downloadHandler( filename = function() { paste0(getDataSet(),"screeplot.png") }, content = function(file) { res <- getAnalysis() labcol <- getLabcol() png(file=file, width = 5, height = 3, units = 'in', res = 500) barplot(res$pco$eig, col=labcol) dev.off() }, contentType = 'image/png' ) ## EXPORT TREE PLOT AS PNG ## output$downloadTree <- downloadHandler( filename = function() { paste0(getDataSet(),"SingleTree.png") }, content = function(file) { tre <- getTree() png(file=file) plot(tre, type=input$treetype, show.tip.lab=input$showtiplabels, font=as.numeric(input$tiplabelfont), cex=input$tiplabelsize, direction=input$treedirection, edge.width=input$edgewidth) dev.off() contentType = 'image/png' } ) ## EXPORT TREE COMPARISON PLOT AS PNG ## output$downloadTreeDiff <- downloadHandler( filename = function() { paste0(getDataSet(),"TreeDiff.png") }, content = function(file) { tr1 <- getTree1() tr2 <- getTree2() png(file=file) plotTreeDiff(tr1,tr2, tipDiff = tipDiff, baseCol=input$basetiplabelcolour, col1=input$minortiplabelcolour, col2=input$majortiplabelcolour, colourMethod=CM, palette=tipPal, type=input$treetype, use.edge.length=as.logical(input$edgelengths), show.tip.lab=input$showtiplabels, font=as.numeric(input$tiplabelfont), cex=input$tiplabelsize, treesFacing = input$treesFacing, edge.width=input$edgewidth, edge.color=input$edgecolor ) dev.off() contentType = 'image/png' } ) ## EXPORT TIP DIFF TABLE ## output$downloadTipDiffTable <- downloadHandler( filename = function() { paste0(getDataSet(),"TipDiffTable.csv")}, content = function(file) { table <- getTipDiffTable() write.csv(table, file) } ) ## EXPORT DENSITREE PLOT AS PNG ## output$downloadDensiTree <- downloadHandler( filename = function() { paste(getDataSet(), 'DensiTreeCluster',input$selectedDensiTree,'.png', sep='') }, content = function(file) { clustTrees <- getDensiTree() png(file=file) densiTree(clustTrees, col=4, alpha=input$alpha, scaleX=input$scaleX) dev.off() contentType = 'image/png' } ) output$selectedGenTree <- renderUI({ numTrees <- getLengthData() treeNames <- getTreeNames() choices <- c("",1:numTrees) names(choices) <- c("Choose one",treeNames) selectInput("selectedGenTree", "Choose individual tree", choices=choices, selected="") }) output$selectedGenTree1 <- renderUI({ numTrees <- getLengthData() treeNames <- getTreeNames() choices <- c("",1:numTrees) names(choices) <- c("Choose one",treeNames) selectInput("selectedGenTree1", "Choose individual tree", choices=choices, selected="") }) output$selectedGenTree2 <- renderUI({ numTrees <- getLengthData() treeNames <- getTreeNames() choices <- c("",1:numTrees) names(choices) <- c("Choose one",treeNames) selectInput("selectedGenTree2", "Choose individual tree", choices=choices, selected="") }) output$selectedRefTree <- renderUI({ numTrees <- getLengthData() treeNames <- getTreeNames() choices <- c("",1:numTrees) names(choices) <- c("Choose one",treeNames) selectInput("selectedRefTree", "Select a reference tree", choices=choices, selected="") }) ## RENDER SYSTEM INFO ## output$systeminfo <- .render.server.info() }) # end shinyServer treespace/inst/shiny/www/0000755000176200001440000000000013164413033015156 5ustar liggesuserstreespace/inst/shiny/www/img/0000755000176200001440000000000013164413033015732 5ustar liggesuserstreespace/inst/shiny/www/img/logo.png0000644000176200001440000015625713164413033017420 0ustar liggesusers‰PNG  IHDR ˜ñhOsBIT|dˆ pHYs.#.#x¥?vtEXtSoftwarewww.inkscape.org›î< 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[statistical exploration of landscapes of phylogenetic trees]

treescape implements statistical tools for the exploration of sets of phylogenetic trees describing the evolutionary relationships between the same taxa. This web interface provides an easy access to the resources implemented in the package. Each tab is made of two panels: a sidebar used to choose inputs, analysis tools and aesthetics, and a main panel displaying results.

Tree landscape explorer

The Tree landscape explorer tab is where the whole tree space can be explored. Choose between a two- or three-dimensional plot to visualise the trees using Metric Multidimensional Scaling (MDS, a.k.a. Principal Coordinates Analysis, PCoA), which calculates the best reduced-spaced visualisation of the distances between trees. The sidebar contains the following sections:
  • Input: to upload the set of trees to analyse
  • Analysis: to customize the analysis
  • Aesthetics: to customize the graphics

Input

treescape takes a list of phylogenetic trees as input. The user can choose between data distributed with the package, or provide input files. Two types of input files can be used:
  • R objects saved using the function save(x, file="x.RData") where 'x' is a list of trees of the class multiphylo (from the ape package. Accepted extensions are ".RData" and ".rda".
  • list of trees saved in a nexus file, e.g. using ape's function write.nexus(x, file="x.nex") in R.

Analysis

Tree summary / metric: the method to be used to measure distances between tips of the trees. Choose from:
  • Kendall Colijn: the tree metric developed by Kendall & Colijn; used by default
  • Billera, Holmes, Vogtmann: the Billera, Holmes & Vogtmann tree metric (also known as the 'geodesic distance')
  • Robinson Foulds (unrooted): the Robinson Foulds tree metric. Note that this implementation of the metric (from the package phangorn) treats the trees as unrooted and uses the unweighted edge-count distance (Robinson Foulds 1981).
  • Tip-tip path distance (unrooted): metric by Steel and Penny which counts the number of internal nodes on the shortest path between each pair of tips. Along with its weighted version (below), this is also known as the tip distance, nodal distance, patristic distance and dissimilarity measure. Trees are treated as unrooted. (see ?nNodes in the package adephylo)
  • Tip-tip branch-length distance (unrooted): similar to the tip-tip path distance, but using the branch lengths instead of counting the edges. (see ?distTips in the package adephylo)
  • Abouheif test: the Abouheif test as presented in Pavoine et al. (2008) (see ?distTips in the package adephylo)
  • Sum of direct descendents: another test related to the Abouheif test (see ?distTips in the package adephylo)
Lambda: The value of lambda used in Kendall & Colijn's metric.
Number of MDS axes retained : The number of principal components to retain in the Metric Multidimensional Scaling (MDS).
Assess quality of projection (Shepard plot)? It is important to be aware of how well or otherwise the Multidimensional Scaling (MDS) plot represents the tree space. Euclidean metrics lend themselves to MDS plotting, whereas other metrics and summaries may prove difficult to accurately project into a small number of dimensions. A Shepard plot is a scatter plot of the actual distances in tree space (x-axis) versus the projected distances in the plot (y-axis). The stronger the correlation, the better the MDS plot represents the true distances.
Identify clusters? Whether to identify clusters by different colours in the plot. Clusters may be found statistically using the findGroves function, which attempts to group trees into clusters of nearby trees in the space. When this option is selected, you will also have the option to choose the clustering method and number of clusters. Alternatively, trees may be coloured by metadata. For example, if the trees were inferred from different genes, or using different inference software, then this can be shown on the plot. Upload a .RData or .csv file containing a list or vector corresponding to the trees. For example, if the trees were inferred from three different genes and there are 100 replicates per gene, create a character vector corresponding (in the same order) to the trees, e.g.
treeTypes <- c(rep("Gene1",100),rep("Gene2",100),rep("Gene3",100))
save as an .RData file, and upload. The trees will be coloured according to gene, making it possible to analyse whether or not the genes contain different phylogenetic signals, according to the chosen inference method.

Aesthetics

A number of graphical options are available.
View: The default is to view the entire "tree landscape", that is, a 2- or 3-dimensional map where the points correspond to trees and the distances between points approximate their relative distances in tree space, according to the chosen measure. The rest of the options detailed below correspond to this view. An alternative is to pick a single "reference tree" and plot the distances from it to each other tree. If this view is chosen, you will be given the option to select the reference tree of interest. When there are many trees in the set, it may be helpful to expand the plot by using the scale bar to increase the height, to view the tree labels more easily. Finally, if clusters are identified in the analysis then these will be marked on this plot in the corresponding colours. Note that this gives an indication of the quality of the clustering: highly scattered clusters correspond to poor resolution in the space. Clusters can overlap in this view: it is quite possible for multiple clusters of trees to be equidistant from the reference tree in different "directions" in the space.
Plot dimensions: If three or more axes have been retained, the option to view the space in three dimensions will be available. It is possible to rotate the image by clicking on it and dragging the mouse, and to zoom in and out with the mouse scroll button. Note that 3D plotting depends on the rglwidget package. At the time of writing, the latest CRAN version (0.1.1431) contains a bug; if you are running this version then you will receive a warning recommending installing the latest development version: install.packages('rglwidget', repos='http://R-Forge.R-project.org') .
x/y/z axes: Used to select which principal components are represented as x and y axes (and, if viewing in 3D, the z axis) on the scatterplot.

Output

Beneath the scatterplot are output options: the scatterplot, trees and analysis results can be exported in various formats. Currently available options are:
  • save the 2D scatterplot as a png file or the 3D scatterplot as an interactive html file. Unfortunately a formal method to save a snapshot of the 3D plot is not yet supported, but you can right-click (Mac: long-click) on the image and select 'Save image as ...'.
  • (if plotted) save the Shepard plot as a png file
  • save trees to a Nexus file (.nex)
  • save results as csv file (spreadsheet-like text file; compatible with most systems); results are output as a table where each row is a tree label, and columns contain optional clustering results as well as principal components (PC) of the Metric Multidimensional Scaling (MDS) of the tree space
  • save results as an R object (.RData); in this case, two objects will be saved in the .RData: 'trees' will be a list of trees of class 'multiPhylo', and 'analysis' will be the results of the analysis; when clusters are not inferred, 'analysis' is the output of the function 'treescape'; when clusters are inferred, 'analysis' is the output of the function 'findGroves'.

Tree viewer

The Tree viewer tab is where individual trees can be plotted, one or two at a time. The sidebar contains the following sections:
  • Input: to pick the tree or trees to view
  • Aesthetics: to customize the graphics

Input

The tree selection options are as follows:
  • Single tree view: for plotting a single tree
  • Two tree comparison: for plotting two trees side by side, using tip colour to highlight topological differences
Within each of these viewing modes, the tree or trees may be selected as follows:
  • Median tree: select the overall median tree, or, if clusters have been identified, the median from each cluster
  • General tree selection: select an individual tree by its name (if provided) or number in the list of trees supplied

Aesthetics

A number of graphical options are available. See ?plot.phylo from the package ape for more details.

Output

Beneath the tree(s) is the option to save the image as a .png file.

densiTree viewer

The densiTree viewer tab is where collections of trees can be viewed together using the densiTree function from the package phangorn, which is based on the software densiTree, which provides considerably more functionality. The sidebar contains the following sections:
  • Input: to pick the collection of trees to view
  • Aesthetics: to customize the graphics

Input

The entire tree set can be viewed using the option 'All trees'. Note that this is likely to be slow for large sets of trees. If clusters have been detected in the Tree landscape explorer tab then the collection of trees from each cluster can also be selected. This can help to show the variation within the cluster.

Aesthetics

A number of graphical options are available; further options will be added soon.

Output

Beneath the densiTree plot is the option to save the image as a .png file.

More information

treescape is developed on github. For questions, bug reports, feature requests and contributions, please use github's issue system.


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.control-label,.has-warning .radio,.has-warning .checkbox,.has-warning .radio-inline,.has-warning .checkbox-inline,.has-warning.radio label,.has-warning.checkbox label,.has-warning.radio-inline label,.has-warning.checkbox-inline label,.has-warning .form-control-feedback{color:#d9831f}.has-warning .form-control,.has-warning .form-control:focus{border-color:#d9831f}.has-error .help-block,.has-error .control-label,.has-error .radio,.has-error .checkbox,.has-error .radio-inline,.has-error .checkbox-inline,.has-error.radio label,.has-error.checkbox label,.has-error.radio-inline label,.has-error.checkbox-inline label,.has-error .form-control-feedback{color:#d9230f}.has-error .form-control,.has-error .form-control:focus{border-color:#d9230f}.has-success .help-block,.has-success .control-label,.has-success .radio,.has-success .checkbox,.has-success .radio-inline,.has-success .checkbox-inline,.has-success.radio label,.has-success.checkbox label,.has-success.radio-inline label,.has-success.checkbox-inline label,.has-success .form-control-feedback{color:#469408}.has-success .form-control,.has-success .form-control:focus{border-color:#469408}.pager a{color:#444444}.pager a:hover,.pager .active>a{border-color:#d9230f;color:#fff}.pager .disabled>a{border-color:#dddddd}treespace/inst/doc/0000755000176200001440000000000013231615741013752 5ustar liggesuserstreespace/inst/doc/tipCategories.Rmd0000644000176200001440000003635513231611237017230 0ustar liggesusers--- title: "Comparing trees by tip label categories" author: "Michelle Kendall" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{treespace worked example: comparing by tip categories} \usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be 7x4 inches knitr::opts_chunk$set(fig.width=7, fig.height=4, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) ``` We introduce distance measures between trees with 'related' tip labels in a recent bioRxiv preprint, [*Comparing phylogenetic trees according to tip label categories*](https://doi.org/10.1101/251710). Here we provide an overview of the measures and present some simple examples. ```{r setupVisible, message=FALSE} # load treespace and packages for plotting: library(treespace) library(RColorBrewer) library(ggplot2) library(reshape2) # set colour scheme pal <- brewer.pal(3,"Dark2") ``` Suppose you have a tree with taxa labels which correspond to some broad "categories" and you want to compare it to other tree(s) whose taxa also correspond to those categories. If the tip labels of the trees do not match exactly then the other metrics supplied in `treespace` cannot be applied. The function `relatedTreeDist` is a version of `treeDist` (the Kendall Colijn metric) which compares tip labels at their category levels, and `treeConcordance` is a measure of how "concordant" a tree is to a reference tree linking the categories, as we shall explain below. Some examples of instances where we may wish to compare trees with such labels: ```{r create_examples_table, echo=FALSE} cats <- c("Bacterial sub-types e.g. serogroups", "Species", "Host", "Protein families", "Population groups", "Disjoint features or phenotypes", "Broad taxonomy") indivs <- c("Bacterial isolates", "Orthologous genes", "Deep sequencing reads of pathogen", "Proteins", "Individual organisms", "Individual organisms", "Individual organisms") exTable <- cbind(cats,indivs) colnames(exTable) <- c("Categories", "Individuals") ``` ```{r table1, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'} require(pander) panderOptions('table.split.table', Inf) pander(exTable, style = 'rmarkdown') ``` ## Related tree distance We begin by demonstrating the measure between trees with related tip sets, `relatedTreeDist`. As an example, suppose we have trees created from deep-sequencing reads from patients A, B and C as follows: ```{r create_trees_for_collapsing_example, echo=FALSE} tr1 <- read.tree(text="(((c4,c3),(c2,c1)),((b3,(b2,b1)),((a3,a2),a1)));") tr1$tip.label <- c("Patient C read 4","Patient C read 3", "Patient C read 2","Patient C read 1", "Patient B read 3","Patient B read 2", "Patient B read 1", "Patient A read 3","Patient A read 2", "Patient A read 1") tr1Collapsed <- read.tree(text="(C,(B,A));") tr1Collapsed$tip.label <- c("Patient C","Patient B","Patient A") tr2 <- read.tree(text="((((c4,c3),(c2,c1)),b2),(b1,((a3,a2),a1)));") tr2$tip.label <- c("Patient C read 4","Patient C read 3", "Patient C read 2","Patient C read 1", "Patient B read 2","Patient B read 1", "Patient A read 3","Patient A read 2", "Patient A read 1") tr2Collapsed <- read.tree(text="((C,B),(B,A));") tr2Collapsed$tip.label <- c("Patient C","Patient B","Patient B","Patient A") ``` ```{r plot_patient_trees, echo=FALSE} layout(matrix(1:2,1,2)) plot(tr1, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],3),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, cex=0.8, edge.color=c("black",rep(pal[[1]],6),"black",rep(pal[[2]],5),rep(pal[[3]],5))) plot(tr2, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],2),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, cex=0.8, edge.color=c(rep("black",2),rep(pal[[1]],6),pal[[2]],"black",pal[[2]],"black", rep(pal[[3]],4))) ``` We can think of "collapsing" these trees down to one tip per monophyletic clade of tips from the same category, and renaming the new tips with just the category labels. For example, the trees above would collapse into these, respectively: ```{r plot_collapsed_trees, echo=FALSE} layout(matrix(1:2,1,2)) plot(tr1Collapsed, tip.color=c(pal[[1]],pal[[2]],pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset=0.1, font=4, cex=0.8, edge.color=c(pal[[1]],"black",pal[[2]],pal[[3]])) plot(tr2Collapsed, tip.color=c(pal[[1]],rep(pal[[2]],2),pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset=0.1, font=4, cex=0.8, edge.color=c("black",pal[[1]],pal[[2]],"black",pal[[2]],pal[[3]])) ``` We calculate the distance between the original trees using `relatedTreeDist` as follows. The function requires a data frame telling it which individuals belong to which categories: ```{r relatedTreeDist} df <- cbind(c(rep("Patient A",3),rep("Patient B",3),rep("Patient C",4)), sort(tr1$tip.label)) df relatedTreeDist(list(tr1,tr2),df)[[1]] ``` The function `relatedTreeDist` can take a list of many trees as input, and it produces a distance matrix giving the distances between each pair of trees. Since we only supplied two trees, we just returned the first (only) element of this matrix. We now explain how this value is calculated. We find the *mean* height of the most recent common ancestor (MRCA) of each pair of categories in the **collapsed** trees. That is, we effectively find all the pairwise depths of MRCAs: ```{r tipsMRCAdepths} tipsMRCAdepths(tr1Collapsed) tipsMRCAdepths(tr2Collapsed) ``` then reduce these down to an average depth per pair of (different category) tips, per tree: ```{r calculation_of_relatedTreeDist, echo=FALSE} MRCAdepths <- cbind(tipsMRCAdepths(tr1Collapsed),c(0.5,0,0.5)) colnames(MRCAdepths) <- c("tip1","tip2","Tree 1", "Tree 2") MRCAdepths ``` and then we take the Euclidean distances between the final two columns: $$ d(T_1,T_2) = \sqrt{ (1-0.5)^2 + (0-0)^2 + (0-0.5)^2 } = 0.7071068 $$ We now give a larger example with more trees. Along the way we use the function `simulateIndTree` which takes a "category-level" tree and randomly adds individuals to make an example individuals tree. ```{r six_comparable_trees, fig.height=12} set.seed(948) # set colour scheme pal2 <- brewer.pal(8,"Dark2") # create a "base" (category-level) tree baseTree <- rtree(8) baseTree$tip.label <- letters[8:1] tree1 <- simulateIndTree(baseTree, itips=3, permuteTips=FALSE) tree2 <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) tree2$tip.label <- c(paste0("h_",2:5),paste0("g_",3:6),paste0("f_",c(2,3,7,9)), paste0("e_",c(1,2,5,6)),paste0("d_",3:6),paste0("c_",5:8), paste0("b_",c(3,5,6,9)),paste0("a_",c(1,4,8,9))) tree3 <- simulateIndTree(baseTree, itips=4, tipPercent = 20) tree3NotForPlotting <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) # just for setting colours later tree4 <- simulateIndTree(baseTree, itips=6) tree4NotForPlotting <- simulateIndTree(baseTree, itips=6, permuteTips=FALSE) # create another base tree baseTree2 <- rtree(8, tip.label=letters[8:1]) tree5 <- simulateIndTree(baseTree2, itips=6, permuteTips=FALSE) tree6 <- simulateIndTree(baseTree2, itips=6, tipPercent=30) # set up colour palettes tipcolors3 <- c(rep(pal2[[1]],3),rep(pal2[[2]],3),rep(pal2[[3]],3),rep(pal2[[4]],3),rep(pal2[[5]],3),rep(pal2[[6]],3),rep(pal2[[7]],3),rep(pal2[[8]],3)) tipcolors4 <- c(rep(pal2[[1]],4),rep(pal2[[2]],4),rep(pal2[[3]],4),rep(pal2[[4]],4),rep(pal2[[5]],4),rep(pal2[[6]],4),rep(pal2[[7]],4),rep(pal2[[8]],4)) # colours for 4 tips tipcolors6 <- c(rep(pal2[[1]],6),rep(pal2[[2]],6),rep(pal2[[3]],6),rep(pal2[[4]],6),rep(pal2[[5]],6),rep(pal2[[6]],6),rep(pal2[[7]],6),rep(pal2[[8]],6)) # colours for 6 tips # prepare tip colours for plotting tree3TipOrder <- sapply(tree3$tip.label, function(x) which(tree3NotForPlotting$tip.label==x)) tree4TipOrder <- sapply(tree4$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) tree5TipOrder <- sapply(tree5$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) tree6TipOrder <- sapply(tree6$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) layout(matrix(c(1,4,2,5,3,6), 2,3)) plot(tree1, tip.color=tipcolors3, no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=2) plot(tree2, tip.color=tipcolors4, no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=2) plot(tree3, tip.color=tipcolors4[tree3TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.8) plot(tree4, tip.color=tipcolors6[tree4TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) plot(tree5, tip.color=tipcolors6[tree5TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) plot(tree6, tip.color=tipcolors6[tree6TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) ``` We have created a variety of trees. Trees 1 and 2 have differing numbers of tips, and differing tip labels, but their collapsed forms would be identical. Tree 3 is similar to Trees 1 and 2 but with a few tips permuted, so that the categories are no longer monophyletic. Tree 4 is similar but with more tips permuted. Trees 5 and 6 were created from a different "base" tree so the relationships between the categories are quite different when we compare, for example, Tree 1 and Tree 5. The function `relatedTreeDist` gives a quantitative description of these similarities and differences. First we just put the trees into a list and create a data frame linking individuals to categories: ```{r relatedTreeDist_six_trees} trees <- list(tree1,tree2,tree3,tree4,tree5,tree6) df <- cbind(sort(rep(letters[1:8],9)),sort(paste0(letters[1:8],"_",rep(1:9,8)))) dists <- relatedTreeDist(trees,df) dists ``` We can visualise these distances with a heatmap: ```{r six_heatmap} dists <- as.matrix(dists) colnames(dists) <- rownames(dists) <- c("Tree 1", "Tree 2", "Tree 3", "Tree 4", "Tree 5", "Tree 6") melted_dists <- melt(dists, na.rm=TRUE) ggheatmap <- ggplot(data = melted_dists, aes(Var2, Var1, fill = value))+ geom_tile(color = "darkgrey")+ scale_fill_gradient2(low = "white", high = "firebrick2", name="Tree distance") + theme_minimal() + coord_fixed() ggheatmap + geom_text(aes(Var2, Var1, label = signif(value,2)), color = "black", size = 8) + theme( axis.title.x = element_blank(), axis.title.y = element_blank(), panel.grid.major = element_blank(), panel.border = element_blank(), panel.background = element_blank(), axis.text = element_text(size=12), axis.ticks = element_blank(), legend.position = "none" ) ``` ## Concordance The concordance measure takes a reference tree R whose tips are a set of categories (with no repeats), and a comparable tree T whose tips are individuals from those categories. The measure counts the proportion of tip pairs whose MRCA in T appears at the same place as the MRCA of their categories in R. It takes a value in (0,1]. Full concordance, where the collapsed version of T is identical to R, gives a value of 1. For example, ```{r concordance_basic} catTree <- read.tree(text="(C,(B,A));") indTree1 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,b2),((a3,a2),a1)));") indTree2 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,a2),((a3,b2),a1)));") indTree3 <- read.tree(text="((a3,(a2,a1)),((b1,c2),((c3,b2),(c1,c4))));") plot(catTree, tip.color=pal, edge.width = 4, type="cladogram", label.offset= 0.5, font=4, edge.color=c(pal[[1]],"black",pal[[2]],pal[[3]])) layout(matrix(1:3,1,3)) plot(indTree1, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],2),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[1]],6),"black",rep(pal[[2]],3),rep(pal[[3]],5))) plot(indTree2, tip.color=c(rep(pal[[1]],4),pal[[2]],rep(pal[[3]],2),pal[[2]],pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[1]],6),rep("black",2),pal[[2]],pal[[3]], rep("black",2),pal[[3]],pal[[2]],pal[[3]])) plot(indTree3, tip.color=c(rep(pal[[3]],3),pal[[2]],rep(pal[[1]],2),pal[[2]],rep(pal[[1]],2)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[3]],4),rep("black",2),pal[[2]],pal[[1]], rep("black",2),pal[[1]],pal[[2]],rep(pal[[1]],3))) ``` The first tree has monophyly per category, and the relative positions of those categories are identical to the reference tree. Correspondingly, the concordance is 1: ```{r concordance_T1} df <- cbind(c(rep("A",3),rep("B",2),rep("C",4)),sort(indTree1$tip.label)) treeConcordance(catTree,indTree1,df) ``` The second tree is fairly similar to the reference, with category C monophyletic and basal to the rest. However, the paraphyly of categories A and B gives a concordance less than 1: ```{r concordance_T2} treeConcordance(catTree,indTree2,df) ``` Finally, the third tree has much less in common with the reference and accordingly has lower concordance: ```{r concordance_T3} treeConcordance(catTree,indTree3,df) ``` To give a broader feel for how the concordance measure behaves, we now perform an experiment where we create random trees and compare them to a reference. We permute the tips in the individuals trees by a given percent, and watch the concordance decrease as the permutations increase and the category-level relationships weaken. In the paper we report our findings for `n=10` and `reps=100`, that is, the reference tree has 10 tips, individuals trees have `n*n=100` tips, and we performed 100 repetitions for each level of permutation. To keep the calculations quick we only use `n=5` and `reps=10` here. ```{r concordance_permuations} n <- 5 reps <- 10 reftree <- rtree(n, tip.label=letters[1:n]) indTrees <- lapply(rep(seq(0,100,20),reps), function(x) simulateIndTree(reftree,itips=n,permuteTips=TRUE,tipPercent=x)) df <- cbind(sort(rep(letters[1:n],n)),sort(indTrees[[1]]$tip.label)) concordances <- sapply(indTrees, function(x) treeConcordance(reftree,x,df)) resultsTab <- as.data.frame(cbind(rep(seq(0,100,20),reps),concordances)) colnames(resultsTab) <- c("Percentage","Concordance") resultsTab[,1] <- factor(resultsTab[,1], levels=seq(0,100,20)) plot <- ggplot(resultsTab, aes(x=Percentage, y=Concordance)) plot + geom_boxplot(aes(colour=Percentage)) + theme_bw() + guides(colour=FALSE) + xlab("Percentage of tips permuted") + ylim(c(0,1)) + theme(axis.text = element_text(size=18), axis.title = element_text(size=18)) ```treespace/inst/doc/DengueVignette.R0000644000176200001440000002344613231615606017023 0ustar liggesusers## ----setup, echo=FALSE--------------------------------------------------- # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) library("rgl") knitr::knit_hooks$set(webgl=hook_webgl) ## ----load, message=FALSE, warning=FALSE---------------------------------- library("treespace") library("phangorn") library("adegenet") ## ----load_BEAST_trees---------------------------------------------------- data(DengueTrees) ## ----sample_BEAST_trees-------------------------------------------------- set.seed(123) BEASTtrees <- DengueTrees[sample(1:length(DengueTrees),200)] ## ----load_seqs----------------------------------------------------------- data(DengueSeqs) ## ----make_NJ------------------------------------------------------------- makeTree <- function(x){ tree <- nj(dist.dna(x, model = "TN93")) tree <- root(tree, resolve.root=TRUE, outgroup="D4Thai63") tree } DnjRooted <- makeTree(DengueSeqs) # Note, there is a (small) negative branch length. # We set this to 0 to avoid warnings from the phangorn package later: DnjRooted$edge.length[which(DnjRooted$edge.length < 0)] <- 0 plot(DnjRooted) ## ----make_NJ_boots, results="hide"--------------------------------------- Dnjboots <- boot.phylo(DnjRooted, DengueSeqs, B=100, makeTree, trees=TRUE, rooted=TRUE) Dnjboots ## ----see_NJ_boots-------------------------------------------------------- plot(DnjRooted) drawSupportOnEdges(Dnjboots$BP) ## ----make_ML, results="hide", message=FALSE------------------------------ Dfit.ini <- pml(DnjRooted, as.phyDat(DengueSeqs), k=4) Dfit <- optim.pml(Dfit.ini, optNni=TRUE, optBf=TRUE, optQ=TRUE, optGamma=TRUE, model="GTR") # root: DfitTreeRooted <- root(Dfit$tree, resolve.root=TRUE, outgroup="D4Thai63") ## ----view_ML------------------------------------------------------------- plot(DfitTreeRooted) ## ----make_ML_boots, results="hide"--------------------------------------- # bootstrap supports: DMLboots <- bootstrap.pml(Dfit, optNni=TRUE) # root: DMLbootsrooted <- lapply(DMLboots, function(x) root(x, resolve.root=TRUE, outgroup="D4Thai63")) class(DMLbootsrooted) <- "multiPhylo" ## ----see_ML_boots-------------------------------------------------------- plotBS(DfitTreeRooted, DMLboots, type="phylogram") ## ----run_treespace------------------------------------------------------- # collect the trees into a single object of class multiPhylo: DengueTrees <- c(BEASTtrees, Dnjboots$trees, DMLbootsrooted, DnjRooted, DfitTreeRooted) class(DengueTrees) <- "multiPhylo" # add tree names: names(DengueTrees)[1:200] <- paste0("BEAST",1:200) names(DengueTrees)[201:300] <- paste0("NJ_boots",1:100) names(DengueTrees)[301:400] <- paste0("ML_boots",1:100) names(DengueTrees)[[401]] <- "NJ" names(DengueTrees)[[402]] <- "ML" # create vector corresponding to tree inference method: Dtype <- c(rep("BEAST",200),rep("NJboots",100),rep("MLboots",100),"NJ","ML") # use treespace to find and project the distances: Dscape <- treespace(DengueTrees, nf=5) ## ----simple_plot--------------------------------------------------------- # simple plot: plotGrovesD3(Dscape$pco, groups=Dtype) ## ----make_better_plot---------------------------------------------------- Dcols <- c("#1b9e77","#d95f02","#7570b3") Dmethod <- c(rep("BEAST",200),rep("NJ",100),rep("ML",100),"NJ","ML") Dbootstraps <- c(rep("replicates",400),"NJ","ML") Dhighlight <- c(rep(1,400),2,2) plotGrovesD3(Dscape$pco, groups=Dmethod, colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ## ----make_better_plot_with_labels---------------------------------------- plotGrovesD3(Dscape$pco, groups=Dmethod, treeNames = names(DengueTrees), # add the tree names as labels colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ## ----make_better_plot_with_tooltips-------------------------------------- plotGrovesD3(Dscape$pco, groups=Dmethod, tooltip_text = names(DengueTrees), # add the tree names as tooltip text colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ## ----scree_plot---------------------------------------------------------- barplot(Dscape$pco$eig, col="navy") ## ----load_rgl------------------------------------------------------------ library(rgl) ## ----plot_3D, rgl=TRUE, webgl=TRUE--------------------------------------- Dcols3D <- c(rep(Dcols[[1]],200),rep(Dcols[[2]],100),rep(Dcols[[3]],100),Dcols[[2]],Dcols[[3]]) rgl::plot3d(Dscape$pco$li[,1],Dscape$pco$li[,2],Dscape$pco$li[,3], type="s", size=c(rep(1.5,400),3,3), col=Dcols3D, xlab="", ylab="", zlab="") ## ----NJ_and_ML_overlap--------------------------------------------------- # trees with the same topology as the NJ tree: which(as.matrix(Dscape$D)["NJ",]==0) # trees with the same topology as the ML tree: which(as.matrix(Dscape$D)["ML",]==0) ## ----compare_trees_NJ_v_ML----------------------------------------------- # comparing NJ and ML: plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE) treeDist(DnjRooted,DfitTreeRooted) ## ----compare_trees_NJ_v_ML_recoloured------------------------------------ # comparing NJ and ML: plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) ## ----make_BEAST_median--------------------------------------------------- BEASTmed <- medTree(BEASTtrees) ## ----compare_BEAST_meds-------------------------------------------------- BEASTmed$trees treeDist(BEASTmed$trees[[1]],BEASTmed$trees[[2]]) ## ----save_BEAST_median--------------------------------------------------- BEASTrep <- BEASTmed$trees[[1]] ## ----compare_BEAST_to_other_trees---------------------------------------- # comparing BEAST median and NJ: plotTreeDiff(BEASTrep,DnjRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,DnjRooted) # comparing BEAST median and ML: plotTreeDiff(BEASTrep,DfitTreeRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,DfitTreeRooted) # comparing BEAST median to a random BEAST tree: num <- runif(1,1,200) randomBEASTtree <- BEASTtrees[[num]] plotTreeDiff(BEASTrep, randomBEASTtree, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,randomBEASTtree) ## ----BEASTtrees---------------------------------------------------------- # load the MCC tree data(DengueBEASTMCC) # concatenate with other BEAST trees BEAST201 <- c(BEASTtrees, DengueBEASTMCC) # compare using treespace: BEASTscape <- treespace(BEAST201, nf=5) # simple plot: plotGrovesD3(BEASTscape$pco) ## ----BEASTtrees_clusters------------------------------------------------- # find clusters or 'groves': BEASTGroves <- findGroves(BEASTscape, nclust=4, clustering = "single") ## ----BEASTtrees_meds----------------------------------------------------- # find median tree(s) per cluster: BEASTMeds <- medTree(BEAST201, groups=BEASTGroves$groups) # for each cluster, select a single median tree to represent it: BEASTMedTrees <- c(BEASTMeds$`1`$trees[[1]], BEASTMeds$`2`$trees[[1]], BEASTMeds$`3`$trees[[1]], BEASTMeds$`4`$trees[[1]]) ## ----BEASTtrees_plot, warning=FALSE-------------------------------------- # extract the numbers from the tree list 'BEASTtrees' which correspond to the median trees: BEASTMedTreeNums <-c(which(BEASTGroves$groups==1)[[BEASTMeds$`1`$treenumbers[[1]]]], which(BEASTGroves$groups==2)[[BEASTMeds$`2`$treenumbers[[1]]]], which(BEASTGroves$groups==3)[[BEASTMeds$`3`$treenumbers[[1]]]], which(BEASTGroves$groups==4)[[BEASTMeds$`4`$treenumbers[[1]]]]) # prepare a vector to highlight median and MCC trees highlightTrees <- rep(1,201) highlightTrees[[201]] <- 2 highlightTrees[BEASTMedTreeNums] <- 2 # prepare colours: BEASTcols <- c("#66c2a5","#fc8d62","#8da0cb","#e78ac3") # plot: plotGrovesD3(BEASTscape$pco, groups=as.vector(BEASTGroves$groups), colors=BEASTcols, col_lab="Cluster", symbol_var = highlightTrees, size_range = c(60,600), size_var = highlightTrees, legend_width=0) ## ----BEASTtree_diffs----------------------------------------------------- # differences between the MCC tree and the median from the largest cluster: treeDist(DengueBEASTMCC,BEASTMedTrees[[1]]) plotTreeDiff(DengueBEASTMCC,BEASTMedTrees[[1]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) # differences between the median trees from clusters 1 and 2: treeDist(BEASTMedTrees[[1]],BEASTMedTrees[[2]]) plotTreeDiff(BEASTMedTrees[[1]],BEASTMedTrees[[2]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treespace/inst/doc/TransmissionTreesVignette.html0000644000176200001440000022314613231615662022054 0ustar liggesusers treespace worked example: Transmission trees

treespace worked example: Transmission trees

Michelle Kendall

2018-01-23

This vignette demonstrates the use of treespace to compare a collection of transmission trees, as proposed in Kendall, Ayabina & Colijn, 2016 arXiv:1609.09051.

First we load the package treespace:

library(treespace)

treespace contains three functions for handling and comparing transmission trees:

  1. findMRCIs() which takes a “who infected whom matrix†(the information about infectors and infectees; more on this below) and outputs:

  • sourceCase: the number of the node which is the source case, i.e. the common infector of all cases (outputs a warning if there is more than one source case).

  • mrcis: a matrix where, for each pair of individuals i and j, the entry (i,j) is the node number of their MRCI. Note that if i infected j then this entry is i itself.

  • mrciDepths: a matrix where, for each pair of individuals i and j, the entry (i,j) is the depth of their MRCI, defined as the number of edges from the source case. The source case has depth zero, its direct infectees have depth 1, and so on.

  1. wiwTreeDist() which takes a list of mrciDepths matrices and computes the distances between them. You have to supply the list of sampled cases in which you are interested, and then it takes the Euclidean distance between each pair of matrices restricted to the sampled cases (and written long-hand, as a vector)

  2. wiwMedTree() which takes a list of mrciDepths matrices, the list of sampled cases, an optional list of weights, and outputs the median transmission tree

Examples

We define a “who infected whom matrix†as a matrix of two columns, where the first represents the infectors and the second represents their infectees. For example, a simple transmission chain could be represented like this:

tree1 <- cbind(Infector=1:5,Infectee=2:6) 
tree1
##      Infector Infectee
## [1,]        1        2
## [2,]        2        3
## [3,]        3        4
## [4,]        4        5
## [5,]        5        6

This can be easily visualised as a transmission chain using graph plotting packages such as igraph or visNetwork:

library(igraph)
# set plotting options:
igraph_options(vertex.size=15,
               vertex.color="cyan",
               vertex.label.cex=2,
               edge.color="lightgrey",
               edge.arrow.size=1)

tree1graph <- graph_from_edgelist(tree1)
plot(tree1graph)

Applying the function findMRCIs gives the following:

findMRCIs(tree1)
## $sourceCase
## [1] 1
## 
## $mrcis
##   1 2 3 4 5 6
## 1 1 1 1 1 1 1
## 2 1 2 2 2 2 2
## 3 1 2 3 3 3 3
## 4 1 2 3 4 4 4
## 5 1 2 3 4 5 5
## 6 1 2 3 4 5 6
## 
## $mrciDepths
##   1 2 3 4 5 6
## 1 0 0 0 0 0 0
## 2 0 1 1 1 1 1
## 3 0 1 2 2 2 2
## 4 0 1 2 3 3 3
## 5 0 1 2 3 4 4
## 6 0 1 2 3 4 5

Comparing three simple trees

Suppose we had other hypotheses for the transmission tree which describes who infected whom amongst these six cases:

# a second scenario:
tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6)
tree2
##      Infector Infectee
## [1,]        1        2
## [2,]        5        3
## [3,]        2        4
## [4,]        2        5
## [5,]        3        6
tree2graph <- graph_from_edgelist(tree2)
plot(tree2graph)

# and a third scenario:
tree3 <- cbind(Infector=c(2,2,2,2,6),Infectee=c(1,3,4,6,5)) 
tree3
##      Infector Infectee
## [1,]        2        1
## [2,]        2        3
## [3,]        2        4
## [4,]        2        6
## [5,]        6        5
tree3graph <- graph_from_edgelist(tree3)
plot(tree3graph)

Then we can use treespace functions to make the following comparisons:

m1 <- findMRCIs(tree1) # find the source case, MRCIs and MRCI depths for tree 1
m2 <- findMRCIs(tree2)
m3 <- findMRCIs(tree3)

matList <- list(m1$mrciDepths,m2$mrciDepths,m3$mrciDepths) # create a list of the mrciDepths matrices
matList
## [[1]]
##   1 2 3 4 5 6
## 1 0 0 0 0 0 0
## 2 0 1 1 1 1 1
## 3 0 1 2 2 2 2
## 4 0 1 2 3 3 3
## 5 0 1 2 3 4 4
## 6 0 1 2 3 4 5
## 
## [[2]]
##   1 5 2 3 4 6
## 1 0 0 0 0 0 0
## 5 0 2 1 2 1 2
## 2 0 1 1 1 1 1
## 3 0 2 1 3 1 3
## 4 0 1 1 1 2 1
## 6 0 2 1 3 1 4
## 
## [[3]]
##   2 6 1 3 4 5
## 2 0 0 0 0 0 0
## 6 0 1 0 0 0 1
## 1 0 0 1 0 0 0
## 3 0 0 0 1 0 0
## 4 0 0 0 0 1 0
## 5 0 1 0 0 0 2
wiwTreeDist(matList, sampled=1:6) # find the Euclidean distances between these matrices, where all six cases are sampled
##        1      2
## 2  6.557       
## 3 11.000  7.211

If we had only sampled cases 4, 5 and 6, so that “1â€, “2†and “3†could be regarded as arbitrary names of inferred, unsampled cases, we would compute:

wiwTreeDist(matList, sampled=4:6)
##       1     2
## 2 5.568      
## 3 9.487 5.568

which substantially changes the measures of similarities and differences between the trees.

Comparing many trees using an MDS plot

Finally, we demonstrate comparing a larger set of transmission trees and finding the median:

set.seed(123)
num <- 500

# create a list of 500 random transmission trees with 11 cases, where the source case is fixed as case 1:
treelistSC1 <- lapply(1:num, function(x) {
  edges <- rtree(6)$edge # effectively creating a random transmission scenario
  relabel <- sample(1:11) # create a relabelling so that infections don't all happen in numerical order, but we force the source case to be 1:
  relabel[[which(relabel==1)]] <- relabel[[7]]
  relabel[[7]] <- 1
  relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]])
  relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]])
  cbind(relabelledEdges1,relabelledEdges2)
})

# create 500 more random transmission trees, but where the source case is fixed as case 2:
treelistSC2 <- lapply(1:num, function(x) {
  edges <- rtree(6)$edge 
  relabel <- sample(1:11) 
  relabel[[which(relabel==2)]] <- relabel[[7]]
  relabel[[7]] <- 2
  relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]])
  relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]])
  cbind(relabelledEdges1,relabelledEdges2)
})

# combine:
combinedLists <- c(treelistSC1,treelistSC2)

# get mrciDepths matrices:
matList1000 <- lapply(combinedLists, function(x)
  findMRCIs(x)$mrciDepths
)

# find pairwise tree distances, treating all cases as sampled:
WiwDists1000 <- wiwTreeDist(matList1000, sampled=1:11)

Now that we have a pairwise distance matrix we can use multidimensional scaling (MDS) to view the relative distances between the trees in a 2D projection. We will colour the points in the projection by the “depth†of the corresponding tree, and use symbols to indicate the source case. For “depth†here we simply use the mean of each “mrciDepths†matrix.

wiwMDS <- dudi.pco(WiwDists1000, scannf=FALSE, nf=3)

library(ggplot2)
library(RColorBrewer)

wiwPlot <- ggplot(wiwMDS$li, aes(x=wiwMDS$li[,1],y=wiwMDS$li[,2]))

# prepare aesthetics
depths <- sapply(matList1000, function(x) mean(x))
sourcecase <- c(rep("1",num),rep("2",num))

# prepare colours:
colfunc <- colorRampPalette(brewer.pal(10,"Spectral"), space="Lab")

wiwPlot + 
  geom_point(size=4, colour="gray60", aes(shape=sourcecase)) + 
  geom_point(size=3, aes(colour=depths, shape=sourcecase)) +
  scale_colour_gradientn("Mean of v\n", 
                         colours=colfunc(7),
                         guide = guide_colourbar(barheight=10)) +
  scale_shape_discrete("Source case\n", solid=T, guide = guide_legend(keyheight = 3, keywidth=1.5)) +
  theme_bw(base_size = 12, base_family = "") +
  theme_bw(base_size = 12, base_family = "") +
  theme(
    legend.title = element_text(size=20),
    legend.text = element_text(size=20),
    axis.text.x = element_text(size=20), axis.text.y = element_text(size=20)) +
  xlab("") + ylab("")

The symmetry in the plot corresponds to the different source cases, and the trees are also clearly separated by depth.

Median trees

If our transmission trees corresponded to real data it could be meaningful to find a single representative tree. To find the geometric median tree(s) from a collection, we use the function wiwMedTree:

med <- wiwMedTree(matList1000)

This returns a list with components:

names(med)
## [1] "centre"    "distances" "mindist"   "median"

  • centre is the mean vector (which may not necessarily correspond to a valid transmission tree with a single source case)

  • distances gives the distance of each tree from the centre, as a vector

  • mindist gives the minimum of these distances

  • median gives the number(s) of the median tree(s)

Here the median trees are:

med$median
##  [1]   34   43   57   78   83   84  104  110  116  134  135  177  265  279
## [15]  287  315  316  329  359  363  406  410  444  448  477  489  498  561
## [29]  567  584  600  603  611  615  633  650  654  656  703  741  749  751
## [43]  752  762  783  785  788  794  841  888  919  924  934  955  969 1000

The first one looks like this:

medgraph <- graph_from_edgelist(combinedLists[[med$median[[1]]]])
plot(medgraph)

treespace/inst/doc/tipCategories.R0000644000176200001440000002560313231615734016707 0ustar liggesusers## ----setup, echo=FALSE--------------------------------------------------- # set global chunk options: images will be 7x4 inches knitr::opts_chunk$set(fig.width=7, fig.height=4, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) ## ----setupVisible, message=FALSE----------------------------------------- # load treespace and packages for plotting: library(treespace) library(RColorBrewer) library(ggplot2) library(reshape2) # set colour scheme pal <- brewer.pal(3,"Dark2") ## ----create_examples_table, echo=FALSE----------------------------------- cats <- c("Bacterial sub-types e.g. serogroups", "Species", "Host", "Protein families", "Population groups", "Disjoint features or phenotypes", "Broad taxonomy") indivs <- c("Bacterial isolates", "Orthologous genes", "Deep sequencing reads of pathogen", "Proteins", "Individual organisms", "Individual organisms", "Individual organisms") exTable <- cbind(cats,indivs) colnames(exTable) <- c("Categories", "Individuals") ## ----table1, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'---- require(pander) panderOptions('table.split.table', Inf) pander(exTable, style = 'rmarkdown') ## ----create_trees_for_collapsing_example, echo=FALSE--------------------- tr1 <- read.tree(text="(((c4,c3),(c2,c1)),((b3,(b2,b1)),((a3,a2),a1)));") tr1$tip.label <- c("Patient C read 4","Patient C read 3", "Patient C read 2","Patient C read 1", "Patient B read 3","Patient B read 2", "Patient B read 1", "Patient A read 3","Patient A read 2", "Patient A read 1") tr1Collapsed <- read.tree(text="(C,(B,A));") tr1Collapsed$tip.label <- c("Patient C","Patient B","Patient A") tr2 <- read.tree(text="((((c4,c3),(c2,c1)),b2),(b1,((a3,a2),a1)));") tr2$tip.label <- c("Patient C read 4","Patient C read 3", "Patient C read 2","Patient C read 1", "Patient B read 2","Patient B read 1", "Patient A read 3","Patient A read 2", "Patient A read 1") tr2Collapsed <- read.tree(text="((C,B),(B,A));") tr2Collapsed$tip.label <- c("Patient C","Patient B","Patient B","Patient A") ## ----plot_patient_trees, echo=FALSE-------------------------------------- layout(matrix(1:2,1,2)) plot(tr1, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],3),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, cex=0.8, edge.color=c("black",rep(pal[[1]],6),"black",rep(pal[[2]],5),rep(pal[[3]],5))) plot(tr2, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],2),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, cex=0.8, edge.color=c(rep("black",2),rep(pal[[1]],6),pal[[2]],"black",pal[[2]],"black", rep(pal[[3]],4))) ## ----plot_collapsed_trees, echo=FALSE------------------------------------ layout(matrix(1:2,1,2)) plot(tr1Collapsed, tip.color=c(pal[[1]],pal[[2]],pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset=0.1, font=4, cex=0.8, edge.color=c(pal[[1]],"black",pal[[2]],pal[[3]])) plot(tr2Collapsed, tip.color=c(pal[[1]],rep(pal[[2]],2),pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset=0.1, font=4, cex=0.8, edge.color=c("black",pal[[1]],pal[[2]],"black",pal[[2]],pal[[3]])) ## ----relatedTreeDist----------------------------------------------------- df <- cbind(c(rep("Patient A",3),rep("Patient B",3),rep("Patient C",4)), sort(tr1$tip.label)) df relatedTreeDist(list(tr1,tr2),df)[[1]] ## ----tipsMRCAdepths------------------------------------------------------ tipsMRCAdepths(tr1Collapsed) tipsMRCAdepths(tr2Collapsed) ## ----calculation_of_relatedTreeDist, echo=FALSE-------------------------- MRCAdepths <- cbind(tipsMRCAdepths(tr1Collapsed),c(0.5,0,0.5)) colnames(MRCAdepths) <- c("tip1","tip2","Tree 1", "Tree 2") MRCAdepths ## ----six_comparable_trees, fig.height=12--------------------------------- set.seed(948) # set colour scheme pal2 <- brewer.pal(8,"Dark2") # create a "base" (category-level) tree baseTree <- rtree(8) baseTree$tip.label <- letters[8:1] tree1 <- simulateIndTree(baseTree, itips=3, permuteTips=FALSE) tree2 <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) tree2$tip.label <- c(paste0("h_",2:5),paste0("g_",3:6),paste0("f_",c(2,3,7,9)), paste0("e_",c(1,2,5,6)),paste0("d_",3:6),paste0("c_",5:8), paste0("b_",c(3,5,6,9)),paste0("a_",c(1,4,8,9))) tree3 <- simulateIndTree(baseTree, itips=4, tipPercent = 20) tree3NotForPlotting <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) # just for setting colours later tree4 <- simulateIndTree(baseTree, itips=6) tree4NotForPlotting <- simulateIndTree(baseTree, itips=6, permuteTips=FALSE) # create another base tree baseTree2 <- rtree(8, tip.label=letters[8:1]) tree5 <- simulateIndTree(baseTree2, itips=6, permuteTips=FALSE) tree6 <- simulateIndTree(baseTree2, itips=6, tipPercent=30) # set up colour palettes tipcolors3 <- c(rep(pal2[[1]],3),rep(pal2[[2]],3),rep(pal2[[3]],3),rep(pal2[[4]],3),rep(pal2[[5]],3),rep(pal2[[6]],3),rep(pal2[[7]],3),rep(pal2[[8]],3)) tipcolors4 <- c(rep(pal2[[1]],4),rep(pal2[[2]],4),rep(pal2[[3]],4),rep(pal2[[4]],4),rep(pal2[[5]],4),rep(pal2[[6]],4),rep(pal2[[7]],4),rep(pal2[[8]],4)) # colours for 4 tips tipcolors6 <- c(rep(pal2[[1]],6),rep(pal2[[2]],6),rep(pal2[[3]],6),rep(pal2[[4]],6),rep(pal2[[5]],6),rep(pal2[[6]],6),rep(pal2[[7]],6),rep(pal2[[8]],6)) # colours for 6 tips # prepare tip colours for plotting tree3TipOrder <- sapply(tree3$tip.label, function(x) which(tree3NotForPlotting$tip.label==x)) tree4TipOrder <- sapply(tree4$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) tree5TipOrder <- sapply(tree5$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) tree6TipOrder <- sapply(tree6$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) layout(matrix(c(1,4,2,5,3,6), 2,3)) plot(tree1, tip.color=tipcolors3, no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=2) plot(tree2, tip.color=tipcolors4, no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=2) plot(tree3, tip.color=tipcolors4[tree3TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.8) plot(tree4, tip.color=tipcolors6[tree4TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) plot(tree5, tip.color=tipcolors6[tree5TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) plot(tree6, tip.color=tipcolors6[tree6TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) ## ----relatedTreeDist_six_trees------------------------------------------- trees <- list(tree1,tree2,tree3,tree4,tree5,tree6) df <- cbind(sort(rep(letters[1:8],9)),sort(paste0(letters[1:8],"_",rep(1:9,8)))) dists <- relatedTreeDist(trees,df) dists ## ----six_heatmap--------------------------------------------------------- dists <- as.matrix(dists) colnames(dists) <- rownames(dists) <- c("Tree 1", "Tree 2", "Tree 3", "Tree 4", "Tree 5", "Tree 6") melted_dists <- melt(dists, na.rm=TRUE) ggheatmap <- ggplot(data = melted_dists, aes(Var2, Var1, fill = value))+ geom_tile(color = "darkgrey")+ scale_fill_gradient2(low = "white", high = "firebrick2", name="Tree distance") + theme_minimal() + coord_fixed() ggheatmap + geom_text(aes(Var2, Var1, label = signif(value,2)), color = "black", size = 8) + theme( axis.title.x = element_blank(), axis.title.y = element_blank(), panel.grid.major = element_blank(), panel.border = element_blank(), panel.background = element_blank(), axis.text = element_text(size=12), axis.ticks = element_blank(), legend.position = "none" ) ## ----concordance_basic--------------------------------------------------- catTree <- read.tree(text="(C,(B,A));") indTree1 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,b2),((a3,a2),a1)));") indTree2 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,a2),((a3,b2),a1)));") indTree3 <- read.tree(text="((a3,(a2,a1)),((b1,c2),((c3,b2),(c1,c4))));") plot(catTree, tip.color=pal, edge.width = 4, type="cladogram", label.offset= 0.5, font=4, edge.color=c(pal[[1]],"black",pal[[2]],pal[[3]])) layout(matrix(1:3,1,3)) plot(indTree1, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],2),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[1]],6),"black",rep(pal[[2]],3),rep(pal[[3]],5))) plot(indTree2, tip.color=c(rep(pal[[1]],4),pal[[2]],rep(pal[[3]],2),pal[[2]],pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[1]],6),rep("black",2),pal[[2]],pal[[3]], rep("black",2),pal[[3]],pal[[2]],pal[[3]])) plot(indTree3, tip.color=c(rep(pal[[3]],3),pal[[2]],rep(pal[[1]],2),pal[[2]],rep(pal[[1]],2)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[3]],4),rep("black",2),pal[[2]],pal[[1]], rep("black",2),pal[[1]],pal[[2]],rep(pal[[1]],3))) ## ----concordance_T1------------------------------------------------------ df <- cbind(c(rep("A",3),rep("B",2),rep("C",4)),sort(indTree1$tip.label)) treeConcordance(catTree,indTree1,df) ## ----concordance_T2------------------------------------------------------ treeConcordance(catTree,indTree2,df) ## ----concordance_T3------------------------------------------------------ treeConcordance(catTree,indTree3,df) ## ----concordance_permuations--------------------------------------------- n <- 5 reps <- 10 reftree <- rtree(n, tip.label=letters[1:n]) indTrees <- lapply(rep(seq(0,100,20),reps), function(x) simulateIndTree(reftree,itips=n,permuteTips=TRUE,tipPercent=x)) df <- cbind(sort(rep(letters[1:n],n)),sort(indTrees[[1]]$tip.label)) concordances <- sapply(indTrees, function(x) treeConcordance(reftree,x,df)) resultsTab <- as.data.frame(cbind(rep(seq(0,100,20),reps),concordances)) colnames(resultsTab) <- c("Percentage","Concordance") resultsTab[,1] <- factor(resultsTab[,1], levels=seq(0,100,20)) plot <- ggplot(resultsTab, aes(x=Percentage, y=Concordance)) plot + geom_boxplot(aes(colour=Percentage)) + theme_bw() + guides(colour=FALSE) + xlab("Percentage of tips permuted") + ylim(c(0,1)) + theme(axis.text = element_text(size=18), axis.title = element_text(size=18)) treespace/inst/doc/tipCategories.html0000644000176200001440000063510613231615735017460 0ustar liggesusers Comparing trees by tip label categories

Comparing trees by tip label categories

Michelle Kendall

2018-01-23

We introduce distance measures between trees with ‘related’ tip labels in a recent bioRxiv preprint, Comparing phylogenetic trees according to tip label categories. Here we provide an overview of the measures and present some simple examples.

# load treespace and packages for plotting:
library(treespace)
library(RColorBrewer) 
library(ggplot2)
library(reshape2)
# set colour scheme
pal <- brewer.pal(3,"Dark2")

Suppose you have a tree with taxa labels which correspond to some broad “categories†and you want to compare it to other tree(s) whose taxa also correspond to those categories. If the tip labels of the trees do not match exactly then the other metrics supplied in treespace cannot be applied. The function relatedTreeDist is a version of treeDist (the Kendall Colijn metric) which compares tip labels at their category levels, and treeConcordance is a measure of how “concordant†a tree is to a reference tree linking the categories, as we shall explain below.

Some examples of instances where we may wish to compare trees with such labels:

Categories Individuals
Bacterial sub-types e.g. serogroups Bacterial isolates
Species Orthologous genes
Host Deep sequencing reads of pathogen
Protein families Proteins
Population groups Individual organisms
Disjoint features or phenotypes Individual organisms
Broad taxonomy Individual organisms

Concordance

The concordance measure takes a reference tree R whose tips are a set of categories (with no repeats), and a comparable tree T whose tips are individuals from those categories. The measure counts the proportion of tip pairs whose MRCA in T appears at the same place as the MRCA of their categories in R. It takes a value in (0,1]. Full concordance, where the collapsed version of T is identical to R, gives a value of 1.

For example,

catTree <- read.tree(text="(C,(B,A));")
indTree1 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,b2),((a3,a2),a1)));")
indTree2 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,a2),((a3,b2),a1)));")
indTree3 <- read.tree(text="((a3,(a2,a1)),((b1,c2),((c3,b2),(c1,c4))));")

plot(catTree, tip.color=pal,
     edge.width = 4, type="cladogram",
     label.offset= 0.5, font=4,
     edge.color=c(pal[[1]],"black",pal[[2]],pal[[3]]))

layout(matrix(1:3,1,3))
plot(indTree1, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],2),rep(pal[[3]],3)),
     edge.width = 4, type="cladogram", no.margin=TRUE,
     label.offset= 0.5, font=4, cex=2,
     edge.color=c("black",rep(pal[[1]],6),"black",rep(pal[[2]],3),rep(pal[[3]],5)))
plot(indTree2, tip.color=c(rep(pal[[1]],4),pal[[2]],rep(pal[[3]],2),pal[[2]],pal[[3]]),
     edge.width = 4, type="cladogram", no.margin=TRUE,
     label.offset= 0.5, font=4, cex=2,
     edge.color=c("black",rep(pal[[1]],6),rep("black",2),pal[[2]],pal[[3]],
                  rep("black",2),pal[[3]],pal[[2]],pal[[3]]))
plot(indTree3, tip.color=c(rep(pal[[3]],3),pal[[2]],rep(pal[[1]],2),pal[[2]],rep(pal[[1]],2)),
     edge.width = 4, type="cladogram", no.margin=TRUE,
     label.offset= 0.5, font=4, cex=2,
     edge.color=c("black",rep(pal[[3]],4),rep("black",2),pal[[2]],pal[[1]],
                  rep("black",2),pal[[1]],pal[[2]],rep(pal[[1]],3)))

The first tree has monophyly per category, and the relative positions of those categories are identical to the reference tree. Correspondingly, the concordance is 1:

df <- cbind(c(rep("A",3),rep("B",2),rep("C",4)),sort(indTree1$tip.label))
treeConcordance(catTree,indTree1,df)
## [1] 1

The second tree is fairly similar to the reference, with category C monophyletic and basal to the rest. However, the paraphyly of categories A and B gives a concordance less than 1:

treeConcordance(catTree,indTree2,df)
## [1] 0.8846

Finally, the third tree has much less in common with the reference and accordingly has lower concordance:

treeConcordance(catTree,indTree3,df)
## [1] 0.4615

To give a broader feel for how the concordance measure behaves, we now perform an experiment where we create random trees and compare them to a reference. We permute the tips in the individuals trees by a given percent, and watch the concordance decrease as the permutations increase and the category-level relationships weaken. In the paper we report our findings for n=10 and reps=100, that is, the reference tree has 10 tips, individuals trees have n*n=100 tips, and we performed 100 repetitions for each level of permutation. To keep the calculations quick we only use n=5 and reps=10 here.

n <- 5
reps <- 10
reftree <- rtree(n, tip.label=letters[1:n])
indTrees <- lapply(rep(seq(0,100,20),reps), function(x)
  simulateIndTree(reftree,itips=n,permuteTips=TRUE,tipPercent=x))

df <- cbind(sort(rep(letters[1:n],n)),sort(indTrees[[1]]$tip.label))

concordances <- sapply(indTrees, function(x) treeConcordance(reftree,x,df))

resultsTab <- as.data.frame(cbind(rep(seq(0,100,20),reps),concordances))
colnames(resultsTab) <- c("Percentage","Concordance")
resultsTab[,1] <- factor(resultsTab[,1], levels=seq(0,100,20))
plot <- ggplot(resultsTab, aes(x=Percentage, y=Concordance))

plot + geom_boxplot(aes(colour=Percentage)) + theme_bw() + guides(colour=FALSE) +
  xlab("Percentage of tips permuted") + ylim(c(0,1)) +
  theme(axis.text = element_text(size=18),
        axis.title = element_text(size=18))

treespace/inst/doc/introduction.R0000644000176200001440000001301713231615705016620 0ustar liggesusers## ----setup, echo=FALSE--------------------------------------------------- # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) library("rgl") knitr::knit_hooks$set(webgl=hook_webgl) ## ----install, eval=FALSE------------------------------------------------- # library(devtools) # install_github("thibautjombart/treespace") ## ----install2, eval=FALSE------------------------------------------------ # install.packages("treespace") ## ----load---------------------------------------------------------------- library("treespace") ## ----load_packages, message=FALSE, warning=FALSE------------------------- library("treespace") library("adegenet") library("adegraphics") library("rgl") ## ----treespace----------------------------------------------------------- # generate list of trees set.seed(1) x <- rmtree(10, 20) names(x) <- paste("tree", 1:10, sep = "") # use treespace res <- treespace(x, nf=3) names(res) res ## ----distances----------------------------------------------------------- # table.image table.image(res$D, nclass=30) # table.value with some customization table.value(res$D, nclass=5, method="color", symbol="circle", col=redpal(5)) ## ----plotgroves---------------------------------------------------------- plotGroves(res$pco, lab.show=TRUE, lab.cex=1.5) ## ----plotgrovesD3-------------------------------------------------------- plotGrovesD3(res$pco, treeNames=1:10) ## ----woodmicePlots------------------------------------------------------- data(woodmiceTrees) wm.res <- treespace(woodmiceTrees,nf=3) # PCs are stored in: head(wm.res$pco$li) # plot results plotGrovesD3(wm.res$pco) ## ----findgroves, cache=FALSE--------------------------------------------- wm.groves <- findGroves(wm.res, nclust=6) names(wm.groves) ## ----plotgroves2--------------------------------------------------------- # basic plot plotGrovesD3(wm.groves) # alternative with improved legend and tooltip text, giving the tree numbers: plotGrovesD3(wm.groves, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster") # plot axes 2 and 3. This helps to show why, for example, clusters 2 and 4 have been identified as separate, despite them appearing to overlap when viewing axes 1 and 2. plotGrovesD3(wm.groves, xax=2, yax=3, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster") ## ----plotgroves_3D, rgl=TRUE, webgl=TRUE--------------------------------- # prepare a colour palette: colours <- fac2col(wm.groves$groups, col.pal=funky) plot3d(wm.groves$treespace$pco$li[,1], wm.groves$treespace$pco$li[,2], wm.groves$treespace$pco$li[,3], col=colours, type="s", size=1.5, xlab="", ylab="", zlab="") ## ----shiny_figures, echo=FALSE, fig.retina = NULL------------------------ knitr::include_graphics("figs/treespace3d.png", dpi=72) knitr::include_graphics("figs/treespaceTree.png", dpi=72) knitr::include_graphics("figs/treespaceDensiTree.png", dpi=72) ## ----woodmiceMedian------------------------------------------------------ # get first median tree tre <- medTree(woodmiceTrees)$trees[[1]] # plot tree plot(tre,type="cladogram",edge.width=3, cex=0.8) ## ----woodmiceCluster1, out.width="600px"--------------------------------- # find median trees for the 6 clusters identified earlier: res <- medTree(woodmiceTrees, wm.groves$groups) # there is one output per cluster names(res) # get the first median of each med.trees <- lapply(res, function(e) ladderize(e$trees[[1]])) # plot trees par(mfrow=c(2,3)) for(i in 1:length(med.trees)) plot(med.trees[[i]], main=paste("cluster",i),cex=1.5) ## ----woodmice_plotTreeDiff----------------------------------------------- # Compare median trees from clusters 1 and 2: plotTreeDiff(med.trees[[1]],med.trees[[2]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) # Compare median trees from clusters 1 and 4, and change aesthetics: plotTreeDiff(med.trees[[1]],med.trees[[4]], type="cladogram", use.edge.length=FALSE, treesFacing = TRUE, edge.width=2, colourMethod="palette", palette=spectral) ## ----woodmice-tip-emphasis----------------------------------------------- wm3.res <- treespace(woodmiceTrees,nf=2,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3) # plot results plotGrovesD3(wm3.res$pco) ## ----findgroves-with-emphasis-------------------------------------------- wm3.groves <- findGroves(woodmiceTrees,nf=3,nclust=6,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3) plotGrovesD3(wm3.groves) ## ----figure_construction, echo=FALSE, fig.retina = NULL------------------ knitr::include_graphics("figs/construction.png", dpi=72) ## ----treevec------------------------------------------------------------- # generate a random tree: tree <- rtree(6) # topological vector of mrca distances from root: treeVec(tree) # vector of mrca distances from root when lambda=0.5: treeVec(tree,0.5) # vector of mrca distances as a function of lambda: vecAsFunction <- treeVec(tree,return.lambda.function=TRUE) # evaluate the vector at lambda=0.5: vecAsFunction(0.5) ## ----treedist------------------------------------------------------------ # generate random trees tree_a <- rtree(6) tree_b <- rtree(6) # topological (lambda=0) distance: treeDist(tree_a,tree_b) # branch-length focused (lambda=1) distance: treeDist(tree_a,tree_b,1) treespace/inst/doc/TransmissionTreesVignette.Rmd0000644000176200001440000002012613214177776021635 0ustar liggesusers--- title: "treespace worked example: Transmission trees" author: "Michelle Kendall" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{treespace worked example: Transmission trees} \usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE) options(digits = 4) ``` This vignette demonstrates the use of *treespace* to compare a collection of transmission trees, as proposed in Kendall, Ayabina & Colijn, 2016 [arXiv:1609.09051](http://arxiv.org/abs/1609.09051). First we load the package *treespace*: ```{r load, message=FALSE} library(treespace) ``` *treespace* contains three functions for handling and comparing transmission trees: 1) `findMRCIs()` which takes a "who infected whom matrix" (the information about infectors and infectees; more on this below) and outputs: + `sourceCase`: the number of the node which is the source case, i.e. the common infector of all cases (outputs a warning if there is more than one source case). + `mrcis`: a matrix where, for each pair of individuals i and j, the entry (i,j) is the node number of their MRCI. Note that if i infected j then this entry is i itself. + `mrciDepths`: a matrix where, for each pair of individuals i and j, the entry (i,j) is the *depth* of their MRCI, defined as the number of edges from the source case. The source case has depth zero, its direct infectees have depth 1, and so on. 2) `wiwTreeDist()` which takes a list of `mrciDepths` matrices and computes the distances between them. You have to supply the list of sampled cases in which you are interested, and then it takes the Euclidean distance between each pair of matrices restricted to the sampled cases (and written long-hand, as a vector) 3) `wiwMedTree()` which takes a list of `mrciDepths` matrices, the list of sampled cases, an optional list of weights, and outputs the median transmission tree Examples --------- We define a "who infected whom matrix" as a matrix of two columns, where the first represents the infectors and the second represents their infectees. For example, a simple transmission chain could be represented like this: ```{r tree1} tree1 <- cbind(Infector=1:5,Infectee=2:6) tree1 ``` This can be easily visualised as a transmission chain using graph plotting packages such as *igraph* or *visNetwork*: ```{r igraph_tree1, message=FALSE} library(igraph) # set plotting options: igraph_options(vertex.size=15, vertex.color="cyan", vertex.label.cex=2, edge.color="lightgrey", edge.arrow.size=1) tree1graph <- graph_from_edgelist(tree1) plot(tree1graph) ``` Applying the function `findMRCIs` gives the following: ```{r simple_wiwMRCIs} findMRCIs(tree1) ``` ### Comparing three simple trees Suppose we had other hypotheses for the transmission tree which describes who infected whom amongst these six cases: ```{r trees2_and_3} # a second scenario: tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6) tree2 tree2graph <- graph_from_edgelist(tree2) plot(tree2graph) # and a third scenario: tree3 <- cbind(Infector=c(2,2,2,2,6),Infectee=c(1,3,4,6,5)) tree3 tree3graph <- graph_from_edgelist(tree3) plot(tree3graph) ``` Then we can use *treespace* functions to make the following comparisons: ```{r tree123_comparison} m1 <- findMRCIs(tree1) # find the source case, MRCIs and MRCI depths for tree 1 m2 <- findMRCIs(tree2) m3 <- findMRCIs(tree3) matList <- list(m1$mrciDepths,m2$mrciDepths,m3$mrciDepths) # create a list of the mrciDepths matrices matList wiwTreeDist(matList, sampled=1:6) # find the Euclidean distances between these matrices, where all six cases are sampled ``` If we had only sampled cases 4, 5 and 6, so that "1", "2" and "3" could be regarded as arbitrary names of inferred, unsampled cases, we would compute: ```{r tree123_sampled4:6} wiwTreeDist(matList, sampled=4:6) ``` which substantially changes the measures of similarities and differences between the trees. ### Comparing many trees using an MDS plot Finally, we demonstrate comparing a larger set of transmission trees and finding the median: ```{r trees1000} set.seed(123) num <- 500 # create a list of 500 random transmission trees with 11 cases, where the source case is fixed as case 1: treelistSC1 <- lapply(1:num, function(x) { edges <- rtree(6)$edge # effectively creating a random transmission scenario relabel <- sample(1:11) # create a relabelling so that infections don't all happen in numerical order, but we force the source case to be 1: relabel[[which(relabel==1)]] <- relabel[[7]] relabel[[7]] <- 1 relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]]) relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]]) cbind(relabelledEdges1,relabelledEdges2) }) # create 500 more random transmission trees, but where the source case is fixed as case 2: treelistSC2 <- lapply(1:num, function(x) { edges <- rtree(6)$edge relabel <- sample(1:11) relabel[[which(relabel==2)]] <- relabel[[7]] relabel[[7]] <- 2 relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]]) relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]]) cbind(relabelledEdges1,relabelledEdges2) }) # combine: combinedLists <- c(treelistSC1,treelistSC2) # get mrciDepths matrices: matList1000 <- lapply(combinedLists, function(x) findMRCIs(x)$mrciDepths ) # find pairwise tree distances, treating all cases as sampled: WiwDists1000 <- wiwTreeDist(matList1000, sampled=1:11) ``` Now that we have a pairwise distance matrix we can use multidimensional scaling (MDS) to view the relative distances between the trees in a 2D projection. We will colour the points in the projection by the "depth" of the corresponding tree, and use symbols to indicate the source case. For "depth" here we simply use the mean of each "mrciDepths" matrix. ```{r wiw_MDS1000, message=FALSE} wiwMDS <- dudi.pco(WiwDists1000, scannf=FALSE, nf=3) library(ggplot2) library(RColorBrewer) wiwPlot <- ggplot(wiwMDS$li, aes(x=wiwMDS$li[,1],y=wiwMDS$li[,2])) # prepare aesthetics depths <- sapply(matList1000, function(x) mean(x)) sourcecase <- c(rep("1",num),rep("2",num)) # prepare colours: colfunc <- colorRampPalette(brewer.pal(10,"Spectral"), space="Lab") wiwPlot + geom_point(size=4, colour="gray60", aes(shape=sourcecase)) + geom_point(size=3, aes(colour=depths, shape=sourcecase)) + scale_colour_gradientn("Mean of v\n", colours=colfunc(7), guide = guide_colourbar(barheight=10)) + scale_shape_discrete("Source case\n", solid=T, guide = guide_legend(keyheight = 3, keywidth=1.5)) + theme_bw(base_size = 12, base_family = "") + theme_bw(base_size = 12, base_family = "") + theme( legend.title = element_text(size=20), legend.text = element_text(size=20), axis.text.x = element_text(size=20), axis.text.y = element_text(size=20)) + xlab("") + ylab("") ``` The symmetry in the plot corresponds to the different source cases, and the trees are also clearly separated by depth. ### Median trees If our transmission trees corresponded to real data it could be meaningful to find a single representative tree. To find the geometric median tree(s) from a collection, we use the function `wiwMedTree`: ```{r wiwMedian} med <- wiwMedTree(matList1000) ``` This returns a list with components: ```{r wiwMedian2} names(med) ``` * `centre` is the mean vector (which may not necessarily correspond to a valid transmission tree with a single source case) * `distances` gives the distance of each tree from the centre, as a vector * `mindist` gives the minimum of these distances * `median` gives the number(s) of the median tree(s) Here the median trees are: ```{r wiwMedTree} med$median ``` The first one looks like this: ```{r wiwMedTreePlot} medgraph <- graph_from_edgelist(combinedLists[[med$median[[1]]]]) plot(medgraph) ```treespace/inst/doc/TransmissionTreesVignette.R0000644000176200001440000001150613231615657021310 0ustar liggesusers## ----setup, echo=FALSE--------------------------------------------------- # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE) options(digits = 4) ## ----load, message=FALSE------------------------------------------------- library(treespace) ## ----tree1--------------------------------------------------------------- tree1 <- cbind(Infector=1:5,Infectee=2:6) tree1 ## ----igraph_tree1, message=FALSE----------------------------------------- library(igraph) # set plotting options: igraph_options(vertex.size=15, vertex.color="cyan", vertex.label.cex=2, edge.color="lightgrey", edge.arrow.size=1) tree1graph <- graph_from_edgelist(tree1) plot(tree1graph) ## ----simple_wiwMRCIs----------------------------------------------------- findMRCIs(tree1) ## ----trees2_and_3-------------------------------------------------------- # a second scenario: tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6) tree2 tree2graph <- graph_from_edgelist(tree2) plot(tree2graph) # and a third scenario: tree3 <- cbind(Infector=c(2,2,2,2,6),Infectee=c(1,3,4,6,5)) tree3 tree3graph <- graph_from_edgelist(tree3) plot(tree3graph) ## ----tree123_comparison-------------------------------------------------- m1 <- findMRCIs(tree1) # find the source case, MRCIs and MRCI depths for tree 1 m2 <- findMRCIs(tree2) m3 <- findMRCIs(tree3) matList <- list(m1$mrciDepths,m2$mrciDepths,m3$mrciDepths) # create a list of the mrciDepths matrices matList wiwTreeDist(matList, sampled=1:6) # find the Euclidean distances between these matrices, where all six cases are sampled ## ----tree123_sampled4:6-------------------------------------------------- wiwTreeDist(matList, sampled=4:6) ## ----trees1000----------------------------------------------------------- set.seed(123) num <- 500 # create a list of 500 random transmission trees with 11 cases, where the source case is fixed as case 1: treelistSC1 <- lapply(1:num, function(x) { edges <- rtree(6)$edge # effectively creating a random transmission scenario relabel <- sample(1:11) # create a relabelling so that infections don't all happen in numerical order, but we force the source case to be 1: relabel[[which(relabel==1)]] <- relabel[[7]] relabel[[7]] <- 1 relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]]) relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]]) cbind(relabelledEdges1,relabelledEdges2) }) # create 500 more random transmission trees, but where the source case is fixed as case 2: treelistSC2 <- lapply(1:num, function(x) { edges <- rtree(6)$edge relabel <- sample(1:11) relabel[[which(relabel==2)]] <- relabel[[7]] relabel[[7]] <- 2 relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]]) relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]]) cbind(relabelledEdges1,relabelledEdges2) }) # combine: combinedLists <- c(treelistSC1,treelistSC2) # get mrciDepths matrices: matList1000 <- lapply(combinedLists, function(x) findMRCIs(x)$mrciDepths ) # find pairwise tree distances, treating all cases as sampled: WiwDists1000 <- wiwTreeDist(matList1000, sampled=1:11) ## ----wiw_MDS1000, message=FALSE------------------------------------------ wiwMDS <- dudi.pco(WiwDists1000, scannf=FALSE, nf=3) library(ggplot2) library(RColorBrewer) wiwPlot <- ggplot(wiwMDS$li, aes(x=wiwMDS$li[,1],y=wiwMDS$li[,2])) # prepare aesthetics depths <- sapply(matList1000, function(x) mean(x)) sourcecase <- c(rep("1",num),rep("2",num)) # prepare colours: colfunc <- colorRampPalette(brewer.pal(10,"Spectral"), space="Lab") wiwPlot + geom_point(size=4, colour="gray60", aes(shape=sourcecase)) + geom_point(size=3, aes(colour=depths, shape=sourcecase)) + scale_colour_gradientn("Mean of v\n", colours=colfunc(7), guide = guide_colourbar(barheight=10)) + scale_shape_discrete("Source case\n", solid=T, guide = guide_legend(keyheight = 3, keywidth=1.5)) + theme_bw(base_size = 12, base_family = "") + theme_bw(base_size = 12, base_family = "") + theme( legend.title = element_text(size=20), legend.text = element_text(size=20), axis.text.x = element_text(size=20), axis.text.y = element_text(size=20)) + xlab("") + ylab("") ## ----wiwMedian----------------------------------------------------------- med <- wiwMedTree(matList1000) ## ----wiwMedian2---------------------------------------------------------- names(med) ## ----wiwMedTree---------------------------------------------------------- med$median ## ----wiwMedTreePlot------------------------------------------------------ medgraph <- graph_from_edgelist(combinedLists[[med$median[[1]]]]) plot(medgraph) treespace/inst/doc/DengueVignette.Rmd0000644000176200001440000003301013231610212017314 0ustar liggesusers--- title: "treespace worked example: Dengue trees" author: "Michelle Kendall, Thibaut Jombart" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{treespace worked example: Dengue trees} \usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) library("rgl") knitr::knit_hooks$set(webgl=hook_webgl) ``` This vignette demonstrates the use of *treespace* to compare a collection of trees. For this example we use trees inferred from 17 dengue virus serotype 4 sequences from Lanciotti et al. (1997). We include a sample of trees from BEAST (v1.8), as well as creating neighbour-joining (NJ) and maximum-likelihood (ML) trees. Loading *treespace* and data: ------------- Load the required packages: ```{r load, message=FALSE, warning=FALSE} library("treespace") library("phangorn") library("adegenet") ``` Load BEAST trees: ```{r load_BEAST_trees} data(DengueTrees) ``` We load a random sample of 500 of the trees (from the second half of the posterior) produced using BEAST v1.8 with xml file 4 from Drummond and Rambaut (2007). It uses the standard GTR + Gamma + I substitution model with uncorrelated lognormal-distributed relaxed molecular clock. Each tree has 17 tips. For convenience in our initial analysis we will take a random sample of 200 of these trees; sample sizes can be increased later. ```{r sample_BEAST_trees} set.seed(123) BEASTtrees <- DengueTrees[sample(1:length(DengueTrees),200)] ``` Load nucleotide sequences: ```{r load_seqs} data(DengueSeqs) ``` Creating neighbour-joining and maximum likelihood trees: ------------- Create a neighbour-joining (NJ) tree using the Tamura and Nei (1993) model (see `?dist.dna` for more information) and root it on the outgroup `"D4Thai63"`: ```{r make_NJ} makeTree <- function(x){ tree <- nj(dist.dna(x, model = "TN93")) tree <- root(tree, resolve.root=TRUE, outgroup="D4Thai63") tree } DnjRooted <- makeTree(DengueSeqs) # Note, there is a (small) negative branch length. # We set this to 0 to avoid warnings from the phangorn package later: DnjRooted$edge.length[which(DnjRooted$edge.length < 0)] <- 0 plot(DnjRooted) ``` We use `boot.phylo` to bootstrap the tree: ```{r make_NJ_boots, results="hide"} Dnjboots <- boot.phylo(DnjRooted, DengueSeqs, B=100, makeTree, trees=TRUE, rooted=TRUE) Dnjboots ``` and we can plot the tree again, annotating it with the bootstrap clade support values: ```{r see_NJ_boots} plot(DnjRooted) drawSupportOnEdges(Dnjboots$BP) ``` We create a maximum-likelihood (ML) tree and root it as before: ```{r make_ML, results="hide", message=FALSE} Dfit.ini <- pml(DnjRooted, as.phyDat(DengueSeqs), k=4) Dfit <- optim.pml(Dfit.ini, optNni=TRUE, optBf=TRUE, optQ=TRUE, optGamma=TRUE, model="GTR") # root: DfitTreeRooted <- root(Dfit$tree, resolve.root=TRUE, outgroup="D4Thai63") ``` View the ML tree: ```{r view_ML} plot(DfitTreeRooted) ``` Create bootstrap trees: ```{r make_ML_boots, results="hide"} # bootstrap supports: DMLboots <- bootstrap.pml(Dfit, optNni=TRUE) # root: DMLbootsrooted <- lapply(DMLboots, function(x) root(x, resolve.root=TRUE, outgroup="D4Thai63")) class(DMLbootsrooted) <- "multiPhylo" ``` Plot the ML tree again, with bootstrap support values: ```{r see_ML_boots} plotBS(DfitTreeRooted, DMLboots, type="phylogram") ``` Using *treespace* to compare trees ------------- We now use the function `treespace` to find and plot distances between all these trees: ```{r run_treespace} # collect the trees into a single object of class multiPhylo: DengueTrees <- c(BEASTtrees, Dnjboots$trees, DMLbootsrooted, DnjRooted, DfitTreeRooted) class(DengueTrees) <- "multiPhylo" # add tree names: names(DengueTrees)[1:200] <- paste0("BEAST",1:200) names(DengueTrees)[201:300] <- paste0("NJ_boots",1:100) names(DengueTrees)[301:400] <- paste0("ML_boots",1:100) names(DengueTrees)[[401]] <- "NJ" names(DengueTrees)[[402]] <- "ML" # create vector corresponding to tree inference method: Dtype <- c(rep("BEAST",200),rep("NJboots",100),rep("MLboots",100),"NJ","ML") # use treespace to find and project the distances: Dscape <- treespace(DengueTrees, nf=5) ``` ```{r simple_plot} # simple plot: plotGrovesD3(Dscape$pco, groups=Dtype) ``` The function `plotGrovesD3` produces interactive d3 plots which enable zooming, moving, tooltip text and legend hovering. We now refine the plot with colour-blind friendly colours (selected using [ColorBrewer2](http://colorbrewer2.org/)), bigger points, varying symbols and point opacity to demonstrate the NJ and ML trees, informative legend title and smaller legend width: ```{r make_better_plot} Dcols <- c("#1b9e77","#d95f02","#7570b3") Dmethod <- c(rep("BEAST",200),rep("NJ",100),rep("ML",100),"NJ","ML") Dbootstraps <- c(rep("replicates",400),"NJ","ML") Dhighlight <- c(rep(1,400),2,2) plotGrovesD3(Dscape$pco, groups=Dmethod, colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ``` We can also add tree labels to the plot. Where these overlap, the user can use "drag and drop" to move them around for better visibility. ```{r make_better_plot_with_labels} plotGrovesD3(Dscape$pco, groups=Dmethod, treeNames = names(DengueTrees), # add the tree names as labels colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ``` Alternatively, where labels are too cluttered, it may be preferable not to plot them but to make the tree names available as tooltip text instead: ```{r make_better_plot_with_tooltips} plotGrovesD3(Dscape$pco, groups=Dmethod, tooltip_text = names(DengueTrees), # add the tree names as tooltip text colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ``` The scree plot is available as part of the `treespace` output: ```{r scree_plot} barplot(Dscape$pco$eig, col="navy") ``` We can also view the plot in 3D: ```{r load_rgl} library(rgl) ``` ```{r plot_3D, rgl=TRUE, webgl=TRUE} Dcols3D <- c(rep(Dcols[[1]],200),rep(Dcols[[2]],100),rep(Dcols[[3]],100),Dcols[[2]],Dcols[[3]]) rgl::plot3d(Dscape$pco$li[,1],Dscape$pco$li[,2],Dscape$pco$li[,3], type="s", size=c(rep(1.5,400),3,3), col=Dcols3D, xlab="", ylab="", zlab="") ``` *treespace* analysis ------------- From these plots we can see that *treespace* has identified variation in the trees according to the Kendall Colijn metric ($\lambda=0$, ignoring branch lengths). The NJ and ML bootstrap trees have broadly similar topologies but are different from any of the BEAST trees. We can check whether any bootstrap trees have the same topology as either the NJ or ML tree, as follows: ```{r NJ_and_ML_overlap} # trees with the same topology as the NJ tree: which(as.matrix(Dscape$D)["NJ",]==0) # trees with the same topology as the ML tree: which(as.matrix(Dscape$D)["ML",]==0) ``` This shows that the NJ tree has the same topology as one NJ bootstrap tree and one ML bootstrap tree. The ML tree has the same topology as 15 ML bootstrap trees, but no NJ bootstrap trees. We can compare pairs of trees using the `plotTreeDiff` function to see exactly where their differences arise. Tips with identical ancestry in the two trees are coloured grey, whereas tips with differing ancestry are coloured peach-red, with the colour darkening according to the number of ancestral differences found at each tip. Since we are comparing the trees topologically (ignoring branch lengths, for the moment), we plot with constant branch lengths for clarity. ```{r compare_trees_NJ_v_ML} # comparing NJ and ML: plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE) treeDist(DnjRooted,DfitTreeRooted) ``` We can adjust the plot settings to make the visualisation clearer: ```{r compare_trees_NJ_v_ML_recoloured} # comparing NJ and ML: plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) ``` For pairwise comparisons it is helpful to find a small number of representative trees. We can find a geometric median tree from the BEAST trees using the `medTree` function: ```{r make_BEAST_median} BEASTmed <- medTree(BEASTtrees) ``` There are two median trees, with identical topology: ```{r compare_BEAST_meds} BEASTmed$trees treeDist(BEASTmed$trees[[1]],BEASTmed$trees[[2]]) ``` so we may select one of them as a BEAST representative tree. Note that for a more thorough analysis it may be appropriate to identify clusters among the BEAST trees and select a summary tree from each cluster: we demonstrate this approach later in the vignette. ```{r save_BEAST_median} BEASTrep <- BEASTmed$trees[[1]] ``` ```{r compare_BEAST_to_other_trees} # comparing BEAST median and NJ: plotTreeDiff(BEASTrep,DnjRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,DnjRooted) # comparing BEAST median and ML: plotTreeDiff(BEASTrep,DfitTreeRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,DfitTreeRooted) # comparing BEAST median to a random BEAST tree: num <- runif(1,1,200) randomBEASTtree <- BEASTtrees[[num]] plotTreeDiff(BEASTrep, randomBEASTtree, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,randomBEASTtree) ``` Using *treespace* to analyse the BEAST trees in more detail: ------------- We used TreeAnnotator (Drummond and Rambaut, 2007) to create a Maximum Clade Credibility (MCC) tree from amongst the BEAST trees. ```{r BEASTtrees} # load the MCC tree data(DengueBEASTMCC) # concatenate with other BEAST trees BEAST201 <- c(BEASTtrees, DengueBEASTMCC) # compare using treespace: BEASTscape <- treespace(BEAST201, nf=5) # simple plot: plotGrovesD3(BEASTscape$pco) ``` There appear to be clusters of tree topologies within the BEAST trees. We can use the function `findGroves` to identify clusters: ```{r BEASTtrees_clusters} # find clusters or 'groves': BEASTGroves <- findGroves(BEASTscape, nclust=4, clustering = "single") ``` and to find a median tree per cluster: ```{r BEASTtrees_meds} # find median tree(s) per cluster: BEASTMeds <- medTree(BEAST201, groups=BEASTGroves$groups) # for each cluster, select a single median tree to represent it: BEASTMedTrees <- c(BEASTMeds$`1`$trees[[1]], BEASTMeds$`2`$trees[[1]], BEASTMeds$`3`$trees[[1]], BEASTMeds$`4`$trees[[1]]) ``` We can now make the plot again, highlighting the MCC tree and the four median trees: ```{r BEASTtrees_plot, warning=FALSE} # extract the numbers from the tree list 'BEASTtrees' which correspond to the median trees: BEASTMedTreeNums <-c(which(BEASTGroves$groups==1)[[BEASTMeds$`1`$treenumbers[[1]]]], which(BEASTGroves$groups==2)[[BEASTMeds$`2`$treenumbers[[1]]]], which(BEASTGroves$groups==3)[[BEASTMeds$`3`$treenumbers[[1]]]], which(BEASTGroves$groups==4)[[BEASTMeds$`4`$treenumbers[[1]]]]) # prepare a vector to highlight median and MCC trees highlightTrees <- rep(1,201) highlightTrees[[201]] <- 2 highlightTrees[BEASTMedTreeNums] <- 2 # prepare colours: BEASTcols <- c("#66c2a5","#fc8d62","#8da0cb","#e78ac3") # plot: plotGrovesD3(BEASTscape$pco, groups=as.vector(BEASTGroves$groups), colors=BEASTcols, col_lab="Cluster", symbol_var = highlightTrees, size_range = c(60,600), size_var = highlightTrees, legend_width=0) ``` To understand the differences between the representative trees we can use `plotTreeDiff` again, for example: ```{r BEASTtree_diffs} # differences between the MCC tree and the median from the largest cluster: treeDist(DengueBEASTMCC,BEASTMedTrees[[1]]) plotTreeDiff(DengueBEASTMCC,BEASTMedTrees[[1]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) # differences between the median trees from clusters 1 and 2: treeDist(BEASTMedTrees[[1]],BEASTMedTrees[[2]]) plotTreeDiff(BEASTMedTrees[[1]],BEASTMedTrees[[2]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) ``` References -------------- [1] Drummond, A. J., and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7(1), 214. [2] Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. Journal of General Virology, 78(9), 2279-2286. treespace/inst/doc/introduction.html0000644000176200001440000471121213231615710017365 0ustar liggesusers Exploration of landscapes of phylogenetic trees

Exploration of landscapes of phylogenetic trees

Thibaut Jombart, Michelle Kendall

2018-01-23

treespace implements new methods for the exploration and analysis of distributions of phylogenetic trees for a given set of taxa.

Installing treespace

To install the development version from github:

library(devtools)
install_github("thibautjombart/treespace")

The stable version can be installed from CRAN using:

install.packages("treespace")

Then, to load the package, use:

library("treespace")

Content overview

The main functions implemented in treespace are:

  • treespace: explore landscapes of phylogenetic trees

  • treespaceServer: open up an application in a web browser for an interactive exploration of the diversity in a set of trees

  • findGroves: identify clusters of similar trees

  • plotGroves: scatterplot of groups of trees, and plotGrovesD3 which enables interactive plotting based on d3.js

  • medTree: find geometric median tree(s) to summarise a group of trees

Other functions are central to the computations of distances between trees:

  • treeVec: characterise a tree by a vector

  • treeDist: find the distance between two tree vectors

  • multiDist: find the pairwise distances of a list of trees

  • refTreeDist: find the distances of a list of trees from a reference tree

  • tipDiff: for a pair of trees, list the tips with differing ancestry

  • plotTreeDiff: plot a pair of trees, highlighting the tips with differing ancestry

Distributed datasets include:

  • woodmiceTrees: illustrative set of 201 trees built using the neighbour-joining and bootstrapping example from the woodmice dataset in the ape documentation.

  • DengueTrees: 500 trees sampled from a BEAST posterior set of trees from (Drummond and Rambaut, 2007)

  • DengueSeqs: 17 dengue virus serotype 4 sequences from (Lanciotti et al., 1997), from which the DengueTrees were inferred.

  • DengueBEASTMCC: the maximum clade credibility (MCC) tree from the DengueTrees.

Exploring trees with treespace

We first load treespace, and the packages required for graphics:

library("treespace")
library("adegenet")
library("adegraphics")
library("rgl")

The function treespace defines typologies of phylogenetic trees using a two-step approach:

  1. perform pairwise comparisons of trees using various (Euclidean) metrics; by default, the comparison uses the Kendall and Colijn metric (Kendall and Colijn, 2016) which is described in more detail below; other metrics rely on tip distances implemented in adephylo (Jombart et al., 2010) and phangorn (Schliep 2011).

  2. use Metric Multidimensional Scaling (MDS, aka Principal Coordinates Analysis, PCoA) to summarise pairwise distances between the trees as well as possible into a few dimensions; the output of the MDS is typically visualised using scatterplots of the first few Principal Components (PCs); this step relies on the PCoA implemented in ade4 (Dray and Dufour, 2007).

The function treespace performs both tasks, returning both the matrix of pairwise tree comparisons ($D), and the PCoA ($pco). This can be illustrated using randomly generated trees:

# generate list of trees
set.seed(1)
x <- rmtree(10, 20)
names(x) <- paste("tree", 1:10, sep = "")

# use treespace
res <- treespace(x, nf=3)
names(res)
## [1] "D"   "pco"
res
## $D
##        tree1 tree2 tree3 tree4 tree5 tree6 tree7 tree8 tree9
## tree2  26.00                                                
## tree3  31.06 26.74                                          
## tree4  42.85 42.12 44.44                                    
## tree5  30.66 27.71 27.37 44.79                              
## tree6  36.50 31.18 30.18 41.81 31.59                        
## tree7  34.64 28.71 29.48 40.35 31.11 32.37                  
## tree8  28.97 26.29 24.45 43.74 23.47 30.41 29.00            
## tree9  29.63 27.42 27.48 45.61 26.31 30.89 29.77 24.60      
## tree10 34.87 30.00 29.44 44.97 34.06 31.05 34.41 31.54 32.59
## 
## $pco
## Duality diagramm
## class: pco dudi
## $call: dudi.pco(d = D, scannf = is.null(nf), nf = nf)
## 
## $nf: 3 axis-components saved
## $rank: 9
## eigen values: 142.1 76.52 62.69 49.88 41.07 ...
##   vector length mode    content       
## 1 $cw    9      numeric column weights
## 2 $lw    10     numeric row weights   
## 3 $eig   9      numeric eigen values  
## 
##   data.frame nrow ncol content             
## 1 $tab       10   9    modified array      
## 2 $li        10   3    row coordinates     
## 3 $l1        10   3    row normed scores   
## 4 $co        9    3    column coordinates  
## 5 $c1        9    3    column normed scores
## other elements: NULL

Pairwise tree distances can be visualised using adegraphics:

# table.image
table.image(res$D, nclass=30)

# table.value with some customization
table.value(res$D, nclass=5, method="color", 
            symbol="circle", col=redpal(5))

The best representation of these distances in a 2-dimensional space is given by the first 2 PCs of the MDS. These can be visualised using any scatter plotting tool; here we use the treespace function plotGroves, based on the adegraphics function scatter:

plotGroves(res$pco, lab.show=TRUE, lab.cex=1.5)

Alternatively, plotGrovesD3 creates interactive plots based on d3.js:

plotGrovesD3(res$pco, treeNames=1:10)

Tree labels can be dragged into new positions to avoid problems such as overlapping.

The functionality of treespace can be further illustrated using ape’s dataset woodmouse, from which we built the 201 trees supplied in woodmiceTrees using the neighbour-joining and bootstrapping example from the ape documentation.

data(woodmiceTrees)
wm.res <- treespace(woodmiceTrees,nf=3)

# PCs are stored in:
head(wm.res$pco$li)
##         A1     A2      A3
## 1  -0.9949 -1.363 -0.7918
## 2  -0.6137 -1.014 -0.6798
## 3   2.6667  4.219 -2.9293
## 4 -13.6081  1.854  1.0947
## 5   2.1980  4.176 -3.1960
## 6   3.6013  4.865  2.9853
# plot results
plotGrovesD3(wm.res$pco)

Packages such as adegraphics and ggplot2 can be used to make alternative plots, for example visualising the density of points within the space.

The treespace function multiDist simply performs the pairwise comparison of trees and outputs a distance matrix. This function may be preferable for large datasets, and when principal co-ordinate analysis is not required. It includes an option to save memory at the expense of computation time.

Identifying clusters of trees

Once a typology of trees has been derived using the approach described above, one may want to formally identify clusters of similar trees. One simple approach is:

  1. select a few first PCs of the MDS (retaining signal but getting rid of random noise)

  2. derive pairwise Euclidean distances between trees based on these PCs

  3. use hierarchical clustering to obtain a dendrogram of these trees

  4. cut the dendrogram to obtain clusters

In treespace, the function findGroves implements this approach, offering various clustering options (see ?findGroves). Here we supply the function with our treespace output wm.res since we have already calculated it, but it is also possible to skip the steps above and directly supply findGroves with a multiPhylo list of trees.

wm.groves <- findGroves(wm.res, nclust=6)
names(wm.groves)
## [1] "groups"    "treespace"

Note that when the number of clusters (nclust) is not provided, the function will display a dendrogram and ask for a cut-off height.

The results can be plotted directly using plotGrovesD3 (see ?plotGrovesD3 for options):

# basic plot
plotGrovesD3(wm.groves)
# alternative with improved legend and tooltip text, giving the tree numbers:
plotGrovesD3(wm.groves, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster")
# plot axes 2 and 3. This helps to show why, for example, clusters 2 and 4 have been identified as separate, despite them appearing to overlap when viewing axes 1 and 2.
plotGrovesD3(wm.groves, xax=2, yax=3, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster")

We can also plot in 3D:

# prepare a colour palette:
colours <- fac2col(wm.groves$groups, col.pal=funky)
plot3d(wm.groves$treespace$pco$li[,1],
       wm.groves$treespace$pco$li[,2],
       wm.groves$treespace$pco$li[,3],
       col=colours, type="s", size=1.5,
       xlab="", ylab="", zlab="")

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treespaceServer: a web application for treespace

The functionalities of treespace are also available via a user-friendly web interface, running locally on the default web browser. It can be started by simply typing treespaceServer(). The interface allows you to import trees and run treespace to view and explore the tree space in 2 or 3 dimensions. It is then straightforward to analyse the tree space by varying \(\lambda\), looking for clusters using findGroves and saving results in various formats. Individual trees can be easily viewed, including median trees per cluster (see below). Pairs of trees can be viewed together with their tip-differences highlighted using the function plotTreeDiff, and collections of trees can be seen together using densiTree from the package phangorn. It is fully documented in the help tab.

Finding median trees

When a set of trees have very similar structures, it makes sense to summarize them into a single ‘consensus’ tree. In treespace, this is achieved by finding the median tree for a set of trees according to the Kendall and Colijn metric. That is, we find the tree which is closest to the centre of the set of trees in the tree landscape defined in treespace. This procedure is implemented by the function medTree:

# get first median tree
tre <- medTree(woodmiceTrees)$trees[[1]]

# plot tree
plot(tre,type="cladogram",edge.width=3, cex=0.8)

However, a more complete and accurate summary of the data can be given by finding a summary tree from each cluster. This is achieved using the groups argument of medTree:

# find median trees for the 6 clusters identified earlier:
res <- medTree(woodmiceTrees, wm.groves$groups)

# there is one output per cluster
names(res)
## [1] "1" "2" "3" "4" "5" "6"
# get the first median of each
med.trees <- lapply(res, function(e) ladderize(e$trees[[1]]))

# plot trees
par(mfrow=c(2,3))
for(i in 1:length(med.trees)) plot(med.trees[[i]], main=paste("cluster",i),cex=1.5)

These trees exhibit a number of topological differences, e.g. in the placement of the (1007S,1208S,0909S) clade. To examine the differences between the trees in a pairwise manner, we can use the function plotTreeDiff, for example:

# Compare median trees from clusters 1 and 2:
plotTreeDiff(med.trees[[1]],med.trees[[2]], use.edge.length=FALSE, 
             treesFacing = TRUE, colourMethod = "palette", palette = funky)

# Compare median trees from clusters 1 and 4, and change aesthetics:
plotTreeDiff(med.trees[[1]],med.trees[[4]], type="cladogram", use.edge.length=FALSE, 
             treesFacing = TRUE, edge.width=2, colourMethod="palette", palette=spectral)

Performing this analysis enables the detection of distinct representative trees supported by data.

Note that in this example we supplied the function medTree with the multiPhylo list of trees. A more computationally efficient process (at the expense of using more memory) is to use the option return.tree.vectors in the initial treespace call, and then supply these vectors directly to medTree. In this case, the tree indices are returned by medTree but the trees are not (since they were not supplied).

Emphasising the placement of certain tips or clades

In some analyses it may be informative to emphasise the placement of particular tips or clades within a set of trees. This can be particularly useful in large trees where the study is focused on a smaller clade. Priority can be given to a list of tips using the argument emphasise.tips, whose corresponding values in the vector comparison will be given a weight of emphasise.weight times the others (the default is 2, i.e. twice the weight).

For example, if we wanted to emphasise where the woodmice trees agree and disagree on the placement of the (1007S,1208S,0909S) clade, we can simply emphasise that clade as follows:

wm3.res <- treespace(woodmiceTrees,nf=2,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3)

# plot results
plotGrovesD3(wm3.res$pco)

It can be seen from the scale of the plot and the density of clustering that the trees are now separated into more distinct clusters.

wm3.groves <- findGroves(woodmiceTrees,nf=3,nclust=6,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3)
plotGrovesD3(wm3.groves)

Conversely, where the structure of a particular clade is not of interest (for example, lineages within an outgroup which was only included for rooting purposes), those tips can be given a weight less than 1 so as to give them less emphasis in the comparison. We note that although it is possible to give tips a weighting of 0, we advise caution with this as the underlying function will no longer be guaranteed to be a metric. That is, a distance of 0 between two trees will no longer necessarily imply that the trees are identical. In most cases it would be wiser to assign a very small weighting to tips which are not of interest.

Method: characterising a tree by a vector

Kendall and Colijn proposed a metric for comparing rooted phylogenetic trees (Kendall and COlijn, 2016). Each tree is characterised by a vector which notes the placement of the most recent common ancestor (MRCA) of each pair of tips, as demonstrated in this example:

Specifically, it records the distance between the MRCA of a pair of tips \((i,j)\) and the root in two ways: the number of edges \(m_{i,j}\), and the path length \(M_{i,j}\). It also records the length \(p_i\) of each ‘pendant’ edge between a tip \(i\) and its immediate ancestor. This procedure results in two vectors for a tree \(T\):

\[ m(T) = (m_{1,2}, m_{1,3},...,m_{k-1,k},1,...,1) \]

and

\[ M(T) = (M_{1,2}, M_{1,3},...,M_{k-1,k},p_1,...,p_k). \]

In \(m(T)\) we record the pendant lengths as 1, as each tip is 1 step from its immediate ancestor. We combine \(m\) and \(M\) with a parameter \(\lambda\) between zero and one to weight the contribution of branch lengths, characterising each tree with a vector

\[ v_\lambda(T) = (1-\lambda)m(T) + \lambda M(T). \]

This is implemented as the function treeVec. For example,

# generate a random tree:
tree <- rtree(6)
# topological vector of mrca distances from root:
treeVec(tree)
##  [1] 0 1 0 2 2 0 1 0 0 0 1 1 0 0 3 1 1 1 1 1 1
# vector of mrca distances from root when lambda=0.5:
treeVec(tree,0.5)
##  [1] 0.0000 0.5745 0.0000 1.3690 1.3690 0.0000 0.5570 0.0000 0.0000 0.0000
## [11] 0.5745 0.5745 0.0000 0.0000 2.3082 0.5707 0.9708 0.5497 0.7639 0.7354
## [21] 0.5891
# vector of mrca distances as a function of lambda:
vecAsFunction <- treeVec(tree,return.lambda.function=TRUE)
# evaluate the vector at lambda=0.5:
vecAsFunction(0.5)
##  [1] 0.0000 0.5745 0.0000 1.3690 1.3690 0.0000 0.5570 0.0000 0.0000 0.0000
## [11] 0.5745 0.5745 0.0000 0.0000 2.3082 0.5707 0.9708 0.5497 0.7639 0.7354
## [21] 0.5891

The metric – the distance between two trees – is the Euclidean distance between these vectors:

\[ d_\lambda(T_a, T_b) = || v_\lambda(T_a) - v_\lambda(T_b) ||. \]

This can be found using treeDist:

# generate random trees
tree_a <- rtree(6)
tree_b <- rtree(6)

# topological (lambda=0) distance:
treeDist(tree_a,tree_b) 
## [1] 6.557
# branch-length focused (lambda=1) distance:
treeDist(tree_a,tree_b,1)
## [1] 2.55

References

  • Dray, S. and Dufour, A. B. (2007) The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software 22(4): 1-20.

  • Drummond, A. J. and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7(1), 214.

  • Jombart, T., Balloux, F. and Dray, S. (2010) adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics 26: 1907-1909. DOI: 10.1093/bioinformatics/btq292

  • Kendall, M. and Colijn, C. (2016) Mapping phylogenetic trees to reveal distinct patterns of evolution. Molecular Biology and Evolution, first published online: June 24, 2016. DOI: 10.1093/molbev/msw124

  • Lanciotti, R. S., Gubler, D. J. and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. Journal of General Virology, 78(9), 2279-2286.

  • Schliep, K. P. (2011) phangorn: phylogenetic analysis in R. Bioinformatics 27(4): 592-593.

Authors / Contributors

Authors:

Contributors:

Maintainer of the CRAN version:

treespace/inst/doc/DengueVignette.html0000644000176200001440000423766513231615615017604 0ustar liggesusers treespace worked example: Dengue trees

treespace worked example: Dengue trees

Michelle Kendall, Thibaut Jombart

2018-01-23

This vignette demonstrates the use of treespace to compare a collection of trees. For this example we use trees inferred from 17 dengue virus serotype 4 sequences from Lanciotti et al. (1997). We include a sample of trees from BEAST (v1.8), as well as creating neighbour-joining (NJ) and maximum-likelihood (ML) trees.

Loading treespace and data:

Load the required packages:

library("treespace")
library("phangorn")
library("adegenet")

Load BEAST trees:

data(DengueTrees)

We load a random sample of 500 of the trees (from the second half of the posterior) produced using BEAST v1.8 with xml file 4 from Drummond and Rambaut (2007). It uses the standard GTR + Gamma + I substitution model with uncorrelated lognormal-distributed relaxed molecular clock. Each tree has 17 tips.

For convenience in our initial analysis we will take a random sample of 200 of these trees; sample sizes can be increased later.

set.seed(123)
BEASTtrees <- DengueTrees[sample(1:length(DengueTrees),200)]

Load nucleotide sequences:

data(DengueSeqs)

Creating neighbour-joining and maximum likelihood trees:

Create a neighbour-joining (NJ) tree using the Tamura and Nei (1993) model (see ?dist.dna for more information) and root it on the outgroup "D4Thai63":

makeTree <- function(x){
  tree <- nj(dist.dna(x, model = "TN93"))
  tree <- root(tree, resolve.root=TRUE, outgroup="D4Thai63")
  tree
}
DnjRooted <- makeTree(DengueSeqs)
# Note, there is a (small) negative branch length. 
# We set this to 0 to avoid warnings from the phangorn package later:
DnjRooted$edge.length[which(DnjRooted$edge.length < 0)] <- 0
plot(DnjRooted)

We use boot.phylo to bootstrap the tree:

Dnjboots <- boot.phylo(DnjRooted, DengueSeqs, B=100, 
                   makeTree, trees=TRUE, rooted=TRUE)
Dnjboots

and we can plot the tree again, annotating it with the bootstrap clade support values:

plot(DnjRooted)
drawSupportOnEdges(Dnjboots$BP)

We create a maximum-likelihood (ML) tree and root it as before:

Dfit.ini <- pml(DnjRooted, as.phyDat(DengueSeqs), k=4)
Dfit <- optim.pml(Dfit.ini, optNni=TRUE, optBf=TRUE,
                  optQ=TRUE, optGamma=TRUE, model="GTR")
## Warning: I unrooted the tree
# root:
DfitTreeRooted <- root(Dfit$tree, resolve.root=TRUE, outgroup="D4Thai63")

View the ML tree:

plot(DfitTreeRooted)

Create bootstrap trees:

# bootstrap supports:
DMLboots <- bootstrap.pml(Dfit, optNni=TRUE)
# root:
DMLbootsrooted <- lapply(DMLboots, function(x) root(x, resolve.root=TRUE, outgroup="D4Thai63"))
class(DMLbootsrooted) <- "multiPhylo"

Plot the ML tree again, with bootstrap support values:

plotBS(DfitTreeRooted, DMLboots, type="phylogram")

Using treespace to compare trees

We now use the function treespace to find and plot distances between all these trees:

# collect the trees into a single object of class multiPhylo:
DengueTrees <- c(BEASTtrees, Dnjboots$trees, DMLbootsrooted,
                     DnjRooted, DfitTreeRooted)
class(DengueTrees) <- "multiPhylo"
# add tree names:
names(DengueTrees)[1:200] <- paste0("BEAST",1:200)
names(DengueTrees)[201:300] <- paste0("NJ_boots",1:100)
names(DengueTrees)[301:400] <- paste0("ML_boots",1:100)
names(DengueTrees)[[401]] <- "NJ"
names(DengueTrees)[[402]] <- "ML"
# create vector corresponding to tree inference method:
Dtype <- c(rep("BEAST",200),rep("NJboots",100),rep("MLboots",100),"NJ","ML")

# use treespace to find and project the distances:
Dscape <- treespace(DengueTrees, nf=5)
# simple plot:
plotGrovesD3(Dscape$pco, groups=Dtype)

The function plotGrovesD3 produces interactive d3 plots which enable zooming, moving, tooltip text and legend hovering. We now refine the plot with colour-blind friendly colours (selected using ColorBrewer2), bigger points, varying symbols and point opacity to demonstrate the NJ and ML trees, informative legend title and smaller legend width:

Dcols <- c("#1b9e77","#d95f02","#7570b3")
Dmethod <- c(rep("BEAST",200),rep("NJ",100),rep("ML",100),"NJ","ML")
Dbootstraps <- c(rep("replicates",400),"NJ","ML")
Dhighlight <- c(rep(1,400),2,2)
plotGrovesD3(Dscape$pco, 
             groups=Dmethod, 
             colors=Dcols,
             col_lab="Tree type",
             size_var=Dhighlight,
             size_range = c(100,500),
             size_lab="",
             symbol_var=Dbootstraps,
             symbol_lab="",
             point_opacity=c(rep(0.4,400),1,1), 
             legend_width=80)

We can also add tree labels to the plot. Where these overlap, the user can use “drag and drop†to move them around for better visibility.

plotGrovesD3(Dscape$pco, 
             groups=Dmethod, 
             treeNames = names(DengueTrees), # add the tree names as labels
             colors=Dcols,
             col_lab="Tree type",
             size_var=Dhighlight,
             size_range = c(100,500),
             size_lab="",
             symbol_var=Dbootstraps,
             symbol_lab="",
             point_opacity=c(rep(0.4,400),1,1), 
             legend_width=80)

Alternatively, where labels are too cluttered, it may be preferable not to plot them but to make the tree names available as tooltip text instead:

plotGrovesD3(Dscape$pco, 
             groups=Dmethod, 
             tooltip_text = names(DengueTrees), # add the tree names as tooltip text
             colors=Dcols,
             col_lab="Tree type",
             size_var=Dhighlight,
             size_range = c(100,500),
             size_lab="",
             symbol_var=Dbootstraps,
             symbol_lab="",
             point_opacity=c(rep(0.4,400),1,1), 
             legend_width=80)

The scree plot is available as part of the treespace output:

barplot(Dscape$pco$eig, col="navy")

We can also view the plot in 3D:

library(rgl)
Dcols3D <- c(rep(Dcols[[1]],200),rep(Dcols[[2]],100),rep(Dcols[[3]],100),Dcols[[2]],Dcols[[3]])
rgl::plot3d(Dscape$pco$li[,1],Dscape$pco$li[,2],Dscape$pco$li[,3],
       type="s",
       size=c(rep(1.5,400),3,3), 
       col=Dcols3D,
       xlab="", ylab="", zlab="")

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treespace analysis

From these plots we can see that treespace has identified variation in the trees according to the Kendall Colijn metric (\(\lambda=0\), ignoring branch lengths). The NJ and ML bootstrap trees have broadly similar topologies but are different from any of the BEAST trees. We can check whether any bootstrap trees have the same topology as either the NJ or ML tree, as follows:

# trees with the same topology as the NJ tree:
which(as.matrix(Dscape$D)["NJ",]==0)
## NJ_boots91         NJ 
##        291        401
# trees with the same topology as the ML tree:
which(as.matrix(Dscape$D)["ML",]==0)
##  ML_boots1  ML_boots6 ML_boots26 ML_boots40 ML_boots43 ML_boots48 
##        301        306        326        340        343        348 
## ML_boots51 ML_boots62 ML_boots65 ML_boots69 ML_boots70 ML_boots76 
##        351        362        365        369        370        376 
## ML_boots79         ML 
##        379        402

This shows that the NJ tree has the same topology as one NJ bootstrap tree and one ML bootstrap tree. The ML tree has the same topology as 15 ML bootstrap trees, but no NJ bootstrap trees.

We can compare pairs of trees using the plotTreeDiff function to see exactly where their differences arise. Tips with identical ancestry in the two trees are coloured grey, whereas tips with differing ancestry are coloured peach-red, with the colour darkening according to the number of ancestral differences found at each tip. Since we are comparing the trees topologically (ignoring branch lengths, for the moment), we plot with constant branch lengths for clarity.

# comparing NJ and ML:
plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE)

treeDist(DnjRooted,DfitTreeRooted)
## [1] 13.93

We can adjust the plot settings to make the visualisation clearer:

# comparing NJ and ML:
plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE, 
             treesFacing = TRUE, colourMethod = "palette", palette = funky)

For pairwise comparisons it is helpful to find a small number of representative trees. We can find a geometric median tree from the BEAST trees using the medTree function:

BEASTmed <- medTree(BEASTtrees)

There are two median trees, with identical topology:

BEASTmed$trees
## 2 phylogenetic trees
treeDist(BEASTmed$trees[[1]],BEASTmed$trees[[2]])
## [1] 0

so we may select one of them as a BEAST representative tree. Note that for a more thorough analysis it may be appropriate to identify clusters among the BEAST trees and select a summary tree from each cluster: we demonstrate this approach later in the vignette.

BEASTrep <- BEASTmed$trees[[1]]
# comparing BEAST median and NJ:
plotTreeDiff(BEASTrep,DnjRooted, use.edge.length=FALSE, 
             treesFacing = TRUE, colourMethod = "palette", palette = funky)

treeDist(BEASTrep,DnjRooted)
## [1] 13.27
# comparing BEAST median and ML:
plotTreeDiff(BEASTrep,DfitTreeRooted, use.edge.length=FALSE, 
             treesFacing = TRUE, colourMethod = "palette", palette = funky)

treeDist(BEASTrep,DfitTreeRooted)
## [1] 9.487
# comparing BEAST median to a random BEAST tree:
num <- runif(1,1,200)
randomBEASTtree <- BEASTtrees[[num]]
plotTreeDiff(BEASTrep, randomBEASTtree, use.edge.length=FALSE, 
             treesFacing = TRUE, colourMethod = "palette", palette = funky)

treeDist(BEASTrep,randomBEASTtree)
## [1] 4

Using treespace to analyse the BEAST trees in more detail:

We used TreeAnnotator (Drummond and Rambaut, 2007) to create a Maximum Clade Credibility (MCC) tree from amongst the BEAST trees.

# load the MCC tree
data(DengueBEASTMCC)
# concatenate with other BEAST trees
BEAST201 <- c(BEASTtrees, DengueBEASTMCC)
# compare using treespace:
BEASTscape <- treespace(BEAST201, nf=5)
# simple plot:
plotGrovesD3(BEASTscape$pco)

There appear to be clusters of tree topologies within the BEAST trees. We can use the function findGroves to identify clusters:

# find clusters or 'groves':
BEASTGroves <- findGroves(BEASTscape, nclust=4, clustering = "single")

and to find a median tree per cluster:

# find median tree(s) per cluster:
BEASTMeds <- medTree(BEAST201, groups=BEASTGroves$groups)
# for each cluster, select a single median tree to represent it:
BEASTMedTrees <- c(BEASTMeds$`1`$trees[[1]],
                   BEASTMeds$`2`$trees[[1]],
                   BEASTMeds$`3`$trees[[1]],
                   BEASTMeds$`4`$trees[[1]])

We can now make the plot again, highlighting the MCC tree and the four median trees:

# extract the numbers from the tree list 'BEASTtrees' which correspond to the median trees: 
BEASTMedTreeNums <-c(which(BEASTGroves$groups==1)[[BEASTMeds$`1`$treenumbers[[1]]]],
                     which(BEASTGroves$groups==2)[[BEASTMeds$`2`$treenumbers[[1]]]],
                     which(BEASTGroves$groups==3)[[BEASTMeds$`3`$treenumbers[[1]]]],
                     which(BEASTGroves$groups==4)[[BEASTMeds$`4`$treenumbers[[1]]]])
# prepare a vector to highlight median and MCC trees
highlightTrees <- rep(1,201)
highlightTrees[[201]] <- 2
highlightTrees[BEASTMedTreeNums] <- 2
# prepare colours:
BEASTcols <- c("#66c2a5","#fc8d62","#8da0cb","#e78ac3")

# plot:
plotGrovesD3(BEASTscape$pco,
          groups=as.vector(BEASTGroves$groups),
          colors=BEASTcols,
          col_lab="Cluster",
          symbol_var = highlightTrees,
          size_range = c(60,600),
          size_var = highlightTrees,
          legend_width=0)

To understand the differences between the representative trees we can use plotTreeDiff again, for example:

# differences between the MCC tree and the median from the largest cluster:
treeDist(DengueBEASTMCC,BEASTMedTrees[[1]])
## [1] 2
plotTreeDiff(DengueBEASTMCC,BEASTMedTrees[[1]], use.edge.length=FALSE, 
             treesFacing = TRUE, colourMethod = "palette", palette = funky)

# differences between the median trees from clusters 1 and 2:
treeDist(BEASTMedTrees[[1]],BEASTMedTrees[[2]])
## [1] 10.63
plotTreeDiff(BEASTMedTrees[[1]],BEASTMedTrees[[2]], use.edge.length=FALSE, 
             treesFacing = TRUE, colourMethod = "palette", palette = funky)

References

[1] Drummond, A. J., and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7(1), 214.

[2] Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. Journal of General Virology, 78(9), 2279-2286.

treespace/inst/doc/introduction.Rmd0000644000176200001440000004115713214177777017164 0ustar liggesusers--- title: "Exploration of landscapes of phylogenetic trees" author: "Thibaut Jombart, Michelle Kendall" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{treespace: exploration of landscapes of phylogenetic trees} \usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) library("rgl") knitr::knit_hooks$set(webgl=hook_webgl) ``` *treespace* implements new methods for the exploration and analysis of distributions of phylogenetic trees for a given set of taxa. Installing *treespace* ------------- To install the development version from github: ```{r install, eval=FALSE} library(devtools) install_github("thibautjombart/treespace") ``` The stable version can be installed from CRAN using: ```{r install2, eval=FALSE} install.packages("treespace") ``` Then, to load the package, use: ```{r load} library("treespace") ``` Content overview ------------- The main functions implemented in *treespace* are: * __`treespace`__: explore landscapes of phylogenetic trees * __`treespaceServer`__: open up an application in a web browser for an interactive exploration of the diversity in a set of trees * __`findGroves`__: identify clusters of similar trees * __`plotGroves`__: scatterplot of groups of trees, and __`plotGrovesD3`__ which enables interactive plotting based on d3.js * __`medTree`__: find geometric median tree(s) to summarise a group of trees Other functions are central to the computations of distances between trees: * __`treeVec`__: characterise a tree by a vector * __`treeDist`__: find the distance between two tree vectors * __`multiDist`__: find the pairwise distances of a list of trees * __`refTreeDist`__: find the distances of a list of trees from a reference tree * __`tipDiff`__: for a pair of trees, list the tips with differing ancestry * __`plotTreeDiff`__: plot a pair of trees, highlighting the tips with differing ancestry Distributed datasets include: * __`woodmiceTrees`__: illustrative set of 201 trees built using the neighbour-joining and bootstrapping example from the woodmice dataset in the ape documentation. * __`DengueTrees`__: 500 trees sampled from a BEAST posterior set of trees from (Drummond and Rambaut, 2007) * __`DengueSeqs`__: 17 dengue virus serotype 4 sequences from (Lanciotti *et al*., 1997), from which the `DengueTrees` were inferred. * __`DengueBEASTMCC`__: the maximum clade credibility (MCC) tree from the `DengueTrees`. Exploring trees with *treespace* -------------- We first load *treespace*, and the packages required for graphics: ```{r load_packages, message=FALSE, warning=FALSE} library("treespace") library("adegenet") library("adegraphics") library("rgl") ``` The function `treespace` defines typologies of phylogenetic trees using a two-step approach: 1. perform pairwise comparisons of trees using various (Euclidean) metrics; by default, the comparison uses the Kendall and Colijn metric (Kendall and Colijn, 2016) which is described in more detail below; other metrics rely on tip distances implemented in *adephylo* (Jombart *et al.*, 2010) and *phangorn* (Schliep 2011). 2. use Metric Multidimensional Scaling (MDS, aka Principal Coordinates Analysis, PCoA) to summarise pairwise distances between the trees as well as possible into a few dimensions; the output of the MDS is typically visualised using scatterplots of the first few Principal Components (PCs); this step relies on the PCoA implemented in *ade4* (Dray and Dufour, 2007). The function `treespace` performs both tasks, returning both the matrix of pairwise tree comparisons (`$D`), and the PCoA (`$pco`). This can be illustrated using randomly generated trees: ```{r treespace} # generate list of trees set.seed(1) x <- rmtree(10, 20) names(x) <- paste("tree", 1:10, sep = "") # use treespace res <- treespace(x, nf=3) names(res) res ``` Pairwise tree distances can be visualised using *adegraphics*: ```{r distances} # table.image table.image(res$D, nclass=30) # table.value with some customization table.value(res$D, nclass=5, method="color", symbol="circle", col=redpal(5)) ``` The best representation of these distances in a 2-dimensional space is given by the first 2 PCs of the MDS. These can be visualised using any scatter plotting tool; here we use the *treespace* function `plotGroves`, based on the *adegraphics* function `scatter`: ```{r plotgroves} plotGroves(res$pco, lab.show=TRUE, lab.cex=1.5) ``` Alternatively, `plotGrovesD3` creates interactive plots based on d3.js: ```{r plotgrovesD3} plotGrovesD3(res$pco, treeNames=1:10) ``` Tree labels can be dragged into new positions to avoid problems such as overlapping. The functionality of `treespace` can be further illustrated using *ape*'s dataset *woodmouse*, from which we built the 201 trees supplied in `woodmiceTrees` using the neighbour-joining and bootstrapping example from the *ape* documentation. ```{r woodmicePlots} data(woodmiceTrees) wm.res <- treespace(woodmiceTrees,nf=3) # PCs are stored in: head(wm.res$pco$li) # plot results plotGrovesD3(wm.res$pco) ``` Packages such as *adegraphics* and *ggplot2* can be used to make alternative plots, for example visualising the density of points within the space. The *treespace* function `multiDist` simply performs the pairwise comparison of trees and outputs a distance matrix. This function may be preferable for large datasets, and when principal co-ordinate analysis is not required. It includes an option to save memory at the expense of computation time. Identifying clusters of trees -------------- Once a typology of trees has been derived using the approach described above, one may want to formally identify clusters of similar trees. One simple approach is: 1. select a few first PCs of the MDS (retaining signal but getting rid of random noise) 2. derive pairwise Euclidean distances between trees based on these PCs 3. use hierarchical clustering to obtain a dendrogram of these trees 4. cut the dendrogram to obtain clusters In *treespace*, the function `findGroves` implements this approach, offering various clustering options (see `?findGroves`). Here we supply the function with our `treespace` output `wm.res` since we have already calculated it, but it is also possible to skip the steps above and directly supply `findGroves` with a multiPhylo list of trees. ```{r findgroves, cache=FALSE} wm.groves <- findGroves(wm.res, nclust=6) names(wm.groves) ``` Note that when the number of clusters (`nclust`) is not provided, the function will display a dendrogram and ask for a cut-off height. The results can be plotted directly using `plotGrovesD3` (see `?plotGrovesD3` for options): ```{r plotgroves2} # basic plot plotGrovesD3(wm.groves) # alternative with improved legend and tooltip text, giving the tree numbers: plotGrovesD3(wm.groves, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster") # plot axes 2 and 3. This helps to show why, for example, clusters 2 and 4 have been identified as separate, despite them appearing to overlap when viewing axes 1 and 2. plotGrovesD3(wm.groves, xax=2, yax=3, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster") ``` We can also plot in 3D: ```{r plotgroves_3D, rgl=TRUE, webgl=TRUE} # prepare a colour palette: colours <- fac2col(wm.groves$groups, col.pal=funky) plot3d(wm.groves$treespace$pco$li[,1], wm.groves$treespace$pco$li[,2], wm.groves$treespace$pco$li[,3], col=colours, type="s", size=1.5, xlab="", ylab="", zlab="") ``` `treespaceServer`: a web application for *treespace* -------------- The functionalities of `treespace` are also available via a user-friendly web interface, running locally on the default web browser. It can be started by simply typing `treespaceServer()`. The interface allows you to import trees and run `treespace` to view and explore the tree space in 2 or 3 dimensions. It is then straightforward to analyse the tree space by varying $\lambda$, looking for clusters using `findGroves` and saving results in various formats. Individual trees can be easily viewed, including median trees per cluster (see below). Pairs of trees can be viewed together with their tip-differences highlighted using the function `plotTreeDiff`, and collections of trees can be seen together using `densiTree` from the package *phangorn*. It is fully documented in the *help* tab. ```{r shiny_figures, echo=FALSE, fig.retina = NULL} knitr::include_graphics("figs/treespace3d.png", dpi=72) knitr::include_graphics("figs/treespaceTree.png", dpi=72) knitr::include_graphics("figs/treespaceDensiTree.png", dpi=72) ``` Finding median trees -------------- When a set of trees have very similar structures, it makes sense to summarize them into a single 'consensus' tree. In `treespace`, this is achieved by finding the *median tree* for a set of trees according to the Kendall and Colijn metric. That is, we find the tree which is closest to the centre of the set of trees in the tree landscape defined in `treespace`. This procedure is implemented by the function `medTree`: ```{r woodmiceMedian} # get first median tree tre <- medTree(woodmiceTrees)$trees[[1]] # plot tree plot(tre,type="cladogram",edge.width=3, cex=0.8) ``` However, a more complete and accurate summary of the data can be given by finding a summary tree from each cluster. This is achieved using the `groups` argument of `medTree`: ```{r woodmiceCluster1, out.width="600px"} # find median trees for the 6 clusters identified earlier: res <- medTree(woodmiceTrees, wm.groves$groups) # there is one output per cluster names(res) # get the first median of each med.trees <- lapply(res, function(e) ladderize(e$trees[[1]])) # plot trees par(mfrow=c(2,3)) for(i in 1:length(med.trees)) plot(med.trees[[i]], main=paste("cluster",i),cex=1.5) ``` These trees exhibit a number of topological differences, e.g. in the placement of the **(1007S,1208S,0909S)** clade. To examine the differences between the trees in a pairwise manner, we can use the function `plotTreeDiff`, for example: ```{r woodmice_plotTreeDiff} # Compare median trees from clusters 1 and 2: plotTreeDiff(med.trees[[1]],med.trees[[2]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) # Compare median trees from clusters 1 and 4, and change aesthetics: plotTreeDiff(med.trees[[1]],med.trees[[4]], type="cladogram", use.edge.length=FALSE, treesFacing = TRUE, edge.width=2, colourMethod="palette", palette=spectral) ``` Performing this analysis enables the detection of distinct representative trees supported by data. Note that in this example we supplied the function `medTree` with the multiPhylo list of trees. A more computationally efficient process (at the expense of using more memory) is to use the option `return.tree.vectors` in the initial `treespace` call, and then supply these vectors directly to `medTree`. In this case, the tree indices are returned by `medTree` but the trees are not (since they were not supplied). Emphasising the placement of certain tips or clades -------------- In some analyses it may be informative to emphasise the placement of particular tips or clades within a set of trees. This can be particularly useful in large trees where the study is focused on a smaller clade. Priority can be given to a list of tips using the argument `emphasise.tips`, whose corresponding values in the vector comparison will be given a weight of `emphasise.weight` times the others (the default is 2, i.e. twice the weight). For example, if we wanted to emphasise where the woodmice trees agree and disagree on the placement of the **(1007S,1208S,0909S)** clade, we can simply emphasise that clade as follows: ```{r woodmice-tip-emphasis} wm3.res <- treespace(woodmiceTrees,nf=2,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3) # plot results plotGrovesD3(wm3.res$pco) ``` It can be seen from the scale of the plot and the density of clustering that the trees are now separated into more distinct clusters. ```{r findgroves-with-emphasis} wm3.groves <- findGroves(woodmiceTrees,nf=3,nclust=6,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3) plotGrovesD3(wm3.groves) ``` Conversely, where the structure of a particular clade is not of interest (for example, lineages within an outgroup which was only included for rooting purposes), those tips can be given a weight less than 1 so as to give them less emphasis in the comparison. We note that although it is possible to give tips a weighting of 0, we advise caution with this as the underlying function will no longer be guaranteed to be a metric. That is, a distance of 0 between two trees will no longer necessarily imply that the trees are identical. In most cases it would be wiser to assign a very small weighting to tips which are not of interest. Method: characterising a tree by a vector -------------- Kendall and Colijn proposed a [metric](http://dx.doi.org/10.1093/molbev/msw124) for comparing rooted phylogenetic trees (Kendall and COlijn, 2016). Each tree is characterised by a vector which notes the placement of the most recent common ancestor (MRCA) of each pair of tips, as demonstrated in this example: ```{r figure_construction, echo=FALSE, fig.retina = NULL} knitr::include_graphics("figs/construction.png", dpi=72) ``` Specifically, it records the distance between the MRCA of a pair of tips $(i,j)$ and the root in two ways: the number of edges $m_{i,j}$, and the path length $M_{i,j}$. It also records the length $p_i$ of each 'pendant' edge between a tip $i$ and its immediate ancestor. This procedure results in two vectors for a tree $T$: $$ m(T) = (m_{1,2}, m_{1,3},...,m_{k-1,k},1,...,1) $$ and $$ M(T) = (M_{1,2}, M_{1,3},...,M_{k-1,k},p_1,...,p_k). $$ In $m(T)$ we record the pendant lengths as 1, as each tip is 1 step from its immediate ancestor. We combine $m$ and $M$ with a parameter $\lambda$ between zero and one to weight the contribution of branch lengths, characterising each tree with a vector $$ v_\lambda(T) = (1-\lambda)m(T) + \lambda M(T). $$ This is implemented as the function __`treeVec`__. For example, ```{r treevec} # generate a random tree: tree <- rtree(6) # topological vector of mrca distances from root: treeVec(tree) # vector of mrca distances from root when lambda=0.5: treeVec(tree,0.5) # vector of mrca distances as a function of lambda: vecAsFunction <- treeVec(tree,return.lambda.function=TRUE) # evaluate the vector at lambda=0.5: vecAsFunction(0.5) ``` The metric -- the distance between two trees -- is the Euclidean distance between these vectors: $$ d_\lambda(T_a, T_b) = || v_\lambda(T_a) - v_\lambda(T_b) ||. $$ This can be found using __`treeDist`__: ```{r treedist} # generate random trees tree_a <- rtree(6) tree_b <- rtree(6) # topological (lambda=0) distance: treeDist(tree_a,tree_b) # branch-length focused (lambda=1) distance: treeDist(tree_a,tree_b,1) ``` References -------------- * Dray, S. and Dufour, A. B. (2007) The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software 22(4): 1-20. * Drummond, A. J. and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7(1), 214. * Jombart, T., Balloux, F. and Dray, S. (2010) adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics 26: 1907-1909. DOI: 10.1093/bioinformatics/btq292 * Kendall, M. and Colijn, C. (2016) Mapping phylogenetic trees to reveal distinct patterns of evolution. Molecular Biology and Evolution, first published online: June 24, 2016. DOI: 10.1093/molbev/msw124 * Lanciotti, R. S., Gubler, D. J. and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. Journal of General Virology, 78(9), 2279-2286. * Schliep, K. P. (2011) phangorn: phylogenetic analysis in R. Bioinformatics 27(4): 592-593. Authors / Contributors -------------- Authors: * [Thibaut Jombart](https://sites.google.com/site/thibautjombart/) * [Michelle Kendall](http://www.imperial.ac.uk/people/m.kendall) Contributors: * [Jacob Almagro-Garcia](http://www.well.ox.ac.uk/jacob-almagro-garcia) * [Caroline Colijn](http://www.imperial.ac.uk/people/c.colijn) Maintainer of the CRAN version: * [Michelle Kendall](http://www.imperial.ac.uk/people/m.kendall) treespace/tests/0000755000176200001440000000000013164413033013365 5ustar liggesuserstreespace/tests/testthat.R0000644000176200001440000000005613227101615015351 0ustar liggesuserslibrary(testthat) test_check("treespace") treespace/tests/testthat/0000755000176200001440000000000013231640221015221 5ustar liggesuserstreespace/tests/testthat/testbasics.R0000644000176200001440000001527113227102464017525 0ustar liggesusers############################ # create some test objects ############################ tree_a <- rtree(100) tree_b <- rtree(100) n <- 10 # number of trees for multiphylo object trees <- rmtree(n,100) l <- runif(1) # a random value for lambda ############################ # test that evaluating at lambda immediately, or via the function, gives the same result ############################ test_that("treeVec calculated at lambda equals treeVec function evaluated at lambda", { expect_equal(treeVec(tree_a,l),treeVec(tree_a,return.lambda.function=TRUE)(l)) }) test_that("treeDist calculated at lambda equals treeDist function evaluated at lambda", { expect_equal(treeDist(tree_a,tree_b,l),treeDist(tree_a,tree_b,return.lambda.function=TRUE)(l)) }) test_that("multiDist calculated at lambda equals multiDist function evaluated at lambda", { expect_equal(multiDist(trees,l),multiDist(trees,return.lambda.function=TRUE)(l)) }) test_that("refTreeDist calculated at lambda equals refTreeDist function evaluated at lambda", { expect_equal(refTreeDist(tree_a,trees,l),refTreeDist(tree_a,trees,return.lambda.function=TRUE)(l)) }) ############################ # test that functions match as they should, including when tips are emphasised ############################ test_that("treeDist equals Euclidean distance between corresponding vectors", { expect_equal(treeDist(tree_a,tree_b), sqrt(sum((treeVec(tree_a) - treeVec(tree_b))^2))) expect_equal(treeDist(tree_a,tree_b,emphasise.tips = c("t1","t2")), sqrt(sum((treeVec(tree_a,emphasise.tips = c("t1","t2")) - treeVec(tree_b,emphasise.tips = c("t1","t2")))^2))) }) test_that("treeDist equals corresponding entry of multiDist", { expect_equal(treeDist(trees[[1]],trees[[2]]), multiDist(trees)[[1]]) expect_equal(treeDist(trees[[1]],trees[[2]],emphasise.tips = c("t1","t2")), multiDist(trees,emphasise.tips = c("t1","t2"))[[1]]) }) test_that("treeDist equals corresponding entry of refTreeDist", { expect_equal(treeDist(tree_a,trees[[1]]), refTreeDist(tree_a,trees)[[1]]) expect_equal(treeDist(tree_a,trees[[1]],emphasise.tips = c("t1","t2")), refTreeDist(tree_a,trees,emphasise.tips = c("t1","t2"))[[1]]) }) test_that("multiDist equals the distance matrix from treespace", { treedistMatrix <- treespace(trees,nf=2)$D treedistMatrix0.5 <- treespace(trees,nf=2,lambda=l)$D multidistMatrixFunction <- multiDist(trees,return.lambda.function=TRUE) expect_equal(multidistMatrixFunction(0)[[n]],treedistMatrix[[n]]) expect_equal(multidistMatrixFunction(l)[[n]],treedistMatrix0.5[[n]]) expect_equal(treespace(trees,nf=2,emphasise.tips=c("t1","t2"))$D[[n]],multiDist(trees,emphasise.tips=c("t1","t2"))[[n]]) }) test_that("medTree results are consistent with treeVec", { geom <- medTree(trees) expect_equal(geom$mindist,sqrt(sum((geom$centre - treeVec(geom$trees[[1]]))^2))) # mindist, centre and median are internally consistent, and consistent with treeVec expect_equal(geom$mindist,min(geom$distances)) # mindist equals the minimum entry in `distances' }) test_that("medTree results are consistent whether the trees or their vectors are supplied", { expect_equal(medTree(trees)$mindist,medTree(treespace(trees,nf=2, return.tree.vectors = TRUE)$vectors)$mindist) }) ############################ # test consistency amongst "related tip" and concordance functions ############################ # create some test trees catTree <- rtree(5) indTree1 <- simulateIndTree(catTree, permuteTips = FALSE) indTree2 <- simulateIndTree(catTree, permuteTips = FALSE) df <- cbind(sort(rep(catTree$tip.label,5)),sort(indTree1$tip.label)) test_that("indTrees are fully concordant with catTree", { expect_equal(treeConcordance(catTree,indTree1,df),1) expect_equal(treeConcordance(catTree,indTree2,df),1) }) test_that("indTree and catTree should be identical by relatedTreeDist measure", { expect_equal(relatedTreeDist(list(indTree1,indTree2),df)[[1]],0) }) test_that("collapsing indTrees gets catTree back", { expect_equal(treeDist(makeCollapsedTree(indTree1,df),catTree),0) expect_equal(treeDist(makeCollapsedTree(indTree2,df),catTree),0) }) ############################ # test that save_memory versions match non-save_memory versions ############################ test_that("save_memory version of multiDist equals normal multiDist", { expect_equal(multiDist(trees,save.memory=TRUE), multiDist(trees)) expect_equal(multiDist(trees,l,save.memory=TRUE), multiDist(trees,l)) }) # NOTE: The outputs are different classes. Would like to be able to remove "as.numeric" here test_that("save_memory version of medTree equals normal medTree", { expect_equal(medTree(trees,save.memory=TRUE)$centre, as.numeric(medTree(trees)$centre)) }) ############################ # test for errors and warnings ############################ test_that("error is given if lambda is outside of [0,1]", { expect_error(treeVec(tree_a,-1)) expect_error(treeVec(tree_a,2)) expect_error(treeDist(tree_a,tree_b,-1)) expect_error(treeDist(tree_a,tree_b,2)) expect_error(multiDist(trees,-1)) expect_error(multiDist(trees,2)) expect_error(medTree(trees,-1)) expect_error(medTree(trees,2)) }) test_that("error is given if input is not of class phylo / multiphylo", { expect_error(treeVec(trees)) expect_error(treeDist(trees)) expect_error(multiDist(tree_a)) expect_error(medTree(tree_a)) expect_error(findGroves(tree_a)) }) test_that("error is given if input tree is unrooted", { unrootedtree <- read.tree(text="(A:1,B:1,C:1);") # an unrooted tree expect_error(treeVec(unrootedtree)) }) test_that("warning is given if tree edge lengths are not defined and lambda>0 or return.lambda.function=T, then they are set to 1", { newicktree <- read.tree(text="((A,B),C);") # a tree without defined edge lengths expect_warning(treeVec(newicktree, lambda=0.5)) expect_warning(treeVec(newicktree, return.lambda.function = TRUE)) }) test_that("error is given if trees have different tip labels", { tree_c <- rtree(99) tree_d <- tree_a tree_d$tip.label <- 1:100 # note that tree_a has tip labels t1, t2, ... expect_error(treeDist(tree_a,tree_c)) expect_error(treeDist(tree_a,tree_d)) }) test_that("error is given if weights vector is not of length n", { expect_error(medTree(trees,weights=rep(1,n+1))) expect_error(medTree(trees,weights=rep(1,n+1),return.lambda.function=TRUE)) }) test_that("warning is given for the combination return.lambda.function=TRUE, save.memory=TRUE", { expect_warning(multiDist(trees,return.lambda.function=TRUE, save.memory=TRUE)) expect_warning(medTree(trees,return.lambda.function=TRUE, save.memory=TRUE)) }) treespace/src/0000755000176200001440000000000013231615755013024 5ustar liggesuserstreespace/src/RcppExports.cpp0000644000176200001440000000367313231615755016032 0ustar liggesusers// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include using namespace Rcpp; // updateDistancesWithCombinations void updateDistancesWithCombinations(NumericVector& length_root_distances, NumericVector& topological_root_distances, IntegerVector& left_partition, IntegerVector& right_partition, IntegerVector& index_offsets, double distance_to_root, int edges_to_root); RcppExport SEXP _treespace_updateDistancesWithCombinations(SEXP length_root_distancesSEXP, SEXP topological_root_distancesSEXP, SEXP left_partitionSEXP, SEXP right_partitionSEXP, SEXP index_offsetsSEXP, SEXP distance_to_rootSEXP, SEXP edges_to_rootSEXP) { BEGIN_RCPP Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector& >::type length_root_distances(length_root_distancesSEXP); Rcpp::traits::input_parameter< NumericVector& >::type topological_root_distances(topological_root_distancesSEXP); Rcpp::traits::input_parameter< IntegerVector& >::type left_partition(left_partitionSEXP); Rcpp::traits::input_parameter< IntegerVector& >::type right_partition(right_partitionSEXP); Rcpp::traits::input_parameter< IntegerVector& >::type index_offsets(index_offsetsSEXP); Rcpp::traits::input_parameter< double >::type distance_to_root(distance_to_rootSEXP); Rcpp::traits::input_parameter< int >::type edges_to_root(edges_to_rootSEXP); updateDistancesWithCombinations(length_root_distances, topological_root_distances, left_partition, right_partition, index_offsets, distance_to_root, edges_to_root); return R_NilValue; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"_treespace_updateDistancesWithCombinations", (DL_FUNC) &_treespace_updateDistancesWithCombinations, 7}, {NULL, NULL, 0} }; RcppExport void R_init_treespace(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } treespace/src/CPP_update_combinations.cpp0000644000176200001440000000301513231615755020260 0ustar liggesusers #include using namespace Rcpp; // [[Rcpp::export]] void updateDistancesWithCombinations(NumericVector& length_root_distances, NumericVector& topological_root_distances, IntegerVector& left_partition, IntegerVector& right_partition, IntegerVector& index_offsets, double distance_to_root, int edges_to_root) { // Iterate through all combinations. for(int i=0; i < left_partition.size(); ++i) { for(int j=0; j < right_partition.size(); ++j) { int first_leaf = left_partition[i]; int second_leaf = right_partition[j]; // Because of the symmetric distances. if(left_partition[i] > right_partition[j]) { first_leaf = right_partition[j]; second_leaf = left_partition[i]; } // Roll the index (notice we take into account C++ indices here, starting at 0). int combination_index = index_offsets[first_leaf-1] + (second_leaf - first_leaf) - 1; // Update the vectors. length_root_distances[combination_index] = distance_to_root; topological_root_distances[combination_index] = edges_to_root; } } } treespace/NAMESPACE0000644000176200001440000000342613231606333013451 0ustar liggesusers# Generated by roxygen2: do not edit by hand export(.render.server.info) export(findGroves) export(findMRCIs) export(linearMrca) export(makeCollapsedTree) export(medTree) export(multiDist) export(plotGroves) export(plotGrovesD3) export(plotTreeDiff) export(refTreeDist) export(relatedTreeDist) export(simulateIndTree) export(tipDiff) export(tipsMRCAdepths) export(treeConcordance) export(treeDist) export(treeVec) export(treespace) export(treespaceServer) export(wiwMedTree) export(wiwTreeDist) import(ape) import(scatterD3) import(shiny) importFrom(MASS,Shepard) importFrom(RLumShiny,jscolorInput) importFrom(Rcpp,evalCpp) importFrom(ade4,cailliez) importFrom(ade4,dudi.pco) importFrom(adegenet,.readExt) importFrom(adegenet,bluepal) importFrom(adegenet,funky) importFrom(adegenet,lightseasun) importFrom(adegenet,num2col) importFrom(adegenet,transp) importFrom(adegraphics,insert) importFrom(adegraphics,s.class) importFrom(adegraphics,s.label) importFrom(adegraphics,s1d.barchart) importFrom(adephylo,distTips) importFrom(combinat,combn) importFrom(combinat,combn2) importFrom(compiler,cmpfun) importFrom(distory,dist.multiPhylo) importFrom(fields,rdist) importFrom(grDevices,colorRampPalette) importFrom(graphics,abline) importFrom(graphics,layout) importFrom(graphics,plot) importFrom(htmlwidgets,saveWidget) importFrom(parallel,mcmapply) importFrom(phangorn,Children) importFrom(phangorn,Descendants) importFrom(phangorn,KF.dist) importFrom(phangorn,RF.dist) importFrom(phangorn,path.dist) importFrom(phangorn,wRF.dist) importFrom(phytools,paste.tree) importFrom(shinyBS,bsTooltip) importFrom(stats,as.dist) importFrom(stats,cutree) importFrom(stats,dist) importFrom(stats,hclust) importFrom(utils,packageDescription) useDynLib(treespace) treespace/data/0000755000176200001440000000000013213471620013135 5ustar liggesuserstreespace/data/DengueTrees.RData0000644000176200001440000037553413164413031016302 0ustar liggesusersý7zXZi"Þ6!ÏXÌâ‰?ïþ])TW"änRÊŸ’Øáí)-t¦^JiíÈ–o Ö\@ÒwRéKŒ4æy‹4žéøYúµÿšÒ† ׳mû>ÜØÈ iövô^4–üWä³xm¢R$Á Töù®Û*•|6ÆúôB|MßoÔäµ?k˜ÛeÓý²ìgÒÔbºþ$Œ©l®N:Vþ­Ü>Ÿ¶ˆI{@Oz5~Þåx¶>ÐËgùÖùÍ2âÏT _£3{Ñ°Wfù†Õ “r5FÃÿøLEØCÜxW4ø^ª _EËKÓíCˆ&÷ÌøMn@‘ï»xZá¿«ˆ  F]Ûì0F¤¸ÞŠB· H‹ÎqM¨ŠlHË84ÉÅħM *—`EZ¢\€~¶`Ø1È3½Pý^4/\ßV•š¬Þ}Zr&xÚFiÕM’4 `² ç±3z›¯]¦~ %èͲ ÛÍ ½(°‚œ#­Ø'S¿jC¡à!ª›·07ØŽ‹aš À âÝÏpöÈÖ:&+6TˆËUšœ`Ü]ä™;Ý>ÑÁ‹U·~,X™†'ŸÏ;<¤ÞŸ›Áðââ®Lèö„*d’©þ™I,¤ëúˆ’…^4”æfÌ;ñõ¹t4À<÷w»@²Sëáªé@ŽŒ«CÖíy®¦1´ÝL¶i8n”´*,¯™éú[ Ë÷]È AVµx Jelp!qÁèªæÖö%€d>k$!ã¤Áˆ3hÇU=OwŒ^ùâ­R‡Á ¦¶¢èGTAWü˜41>&¡-/ëãqÌ.E¿} †nN‹%N_®¯» rbñÜ%±Y U˶Œ®!OϙŅ¢ ­F“£Ñh:öaE ǵ8õÕJ%øB¸ˆ¨SRóá2ëÚ™iÒ%ÁØAGVÅ qE·+AK¹ví¬§í§“d˦´öJ›jÄe,UøƒrÊó%~%ÃF‘ÅR¯¨+¤ 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øîòùîòìaqàñq:ðýÕëwW§'åÓåÏjÿxñ÷þÕ»³ãtäÉÅ_ßÂ~•Ž<}³¿Üÿzr\ytR9Ër?}6Î\žÿ°»úewz–Žü´{³ÿcú¸ num_leaves)) { for(p in 2:length(annotated_nodes[[child]]$partitions)) aggregated_partitions <- c(aggregated_partitions, annotated_nodes[[child]]$partitions[[p]]) } # Update the branch length on the child. annotated_nodes[[child]]$root_distance <- edge_lengths[i] # We have not visited this internal node before. if(parent > length(annotated_nodes) || is.null(annotated_nodes[[parent]])) { # Assume the first time we get the left child partition. annotated_nodes[[parent]] <- list(root_distance=NULL, edges_to_root=1, partitions=list(aggregated_partitions)) } # This is not the first time we have visited the node. else { # We store the next partition of leaves. annotated_nodes[[parent]]$partitions[[length(annotated_nodes[[parent]]$partitions)+1]] <- aggregated_partitions } } # Update the distance to the root at the root (i.e. 0) # And the number of edges to the root (i.e. 0). annotated_nodes[[num_leaves+1]]$root_distance <- 0 annotated_nodes[[num_leaves+1]]$edges_to_root <- 0 # Top down, compute distances to the root for each node. for(i in num_edges:1) { parent <- edges[i,1] child <- edges[i,2] # If the child is a leaf we translate the index for the sorted labels. if(child <= num_leaves) child <- tip_order[child] annotated_nodes[[child]]$root_distance <- annotated_nodes[[child]]$root_distance + annotated_nodes[[parent]]$root_distance annotated_nodes[[child]]$edges_to_root <- annotated_nodes[[child]]$edges_to_root + annotated_nodes[[parent]]$edges_to_root } # Distance vectors vector_length <- (num_leaves*(num_leaves-1)/2) + num_leaves length_root_distances <- double(vector_length) topological_root_distances <- integer(vector_length) # Fill-in the leaves (notice the involved index translation for leaves). # Consensus version: final k entries are the heights of leaves, not pendant lengths for (i in (vector_length-num_leaves+1):vector_length) { topological_root_distances[[i]] <- annotated_nodes[[i-vector_length+num_leaves]]$edges_to_root } length_root_distances[(vector_length-num_leaves+1):vector_length] <- edge_lengths[match(1:num_leaves, edges[,2])][order(tree$tip.label)] # Instead of computing the lexicographic order for the combination pairs assume we # are filling in a symmetric distance matrix (using only the triangular upper part). # We just need to "roll" the matrix indices into the vector indices. # Examples for (k=5) # The combination c(1,4) would be located at position 3 on the vector. # The combination c(2,1) would be located at position 1 on the vector because d(2,1) = d(1,2). # The combination c(2,3) would be located at position 5 on the vector. index_offsets <- c(0, cumsum((num_leaves-1):1)) # This is the slow part, we compute both vectors as gain would be marginal. sapply(annotated_nodes, function(node) { # We skip leaves and the root (if the MRCA for M groups of leaves is at the root # all combinations of leaves -among different groups- have 0 as distance to the root). # For large trees this can spare us of computing a lot of combinations. # Example: In a perfectly balanced binary tree (N/2 leaves at each side of the root), # at the root we'd save (N/2) * (N/2) combinations to update. Worst case scenario is completely # unbalanced tree (N-1,1), we'd save in that case only N-1 combinations. # Make sure we are not visiting a leaf or the root. if(length(node$partitions) > 1 && node$root_distance > 0) { # Update all combinations for pairs of leaves from different groups. num_groups <- length(node$partitions) for(group_a in 1:(num_groups-1)) { for(group_b in (group_a+1):num_groups) { updateDistancesWithCombinations(length_root_distances, topological_root_distances, node$partitions[[group_a]], node$partitions[[group_b]], index_offsets, node$root_distance, node$edges_to_root) } } } }) # make tip label vector tips_in_order <- tree$tip.label[order(tree$tip.label)] label_pairs <- t(combn(tips_in_order,2, fun=c(), simplify=TRUE)) #self_pairs <- rbind(tips_in_order,tips_in_order) tipsMRCAvec <- cbind(label_pairs,topological_root_distances[1:(num_leaves*(num_leaves-1)/2)]) colnames(tipsMRCAvec) <- c("tip1","tip2","rootdist") return(tipsMRCAvec) } treespace/R/findGroves.R0000644000176200001440000000702613164413031014660 0ustar liggesusers##' #' Identify clusters of similar trees #' #' This function uses hierarchical clustering on principal components output by \code{\link{treespace}} to identify groups of similar trees. Clustering relies on \code{\link{hclust}}, using Ward's method by default. #' #' @param x an object of the class multiPhylo or the output of the function \code{treespace} #' @param method (ignored if x is from \code{treespace}) this specifies a function which outputs the summary of a tree in the form of a vector. Defaults to \code{treeVec}. #' @param nf (ignored if x is from \code{treespace}) the number of principal components to retain #' @param clustering a character string indicating the clustering method to be used; defaults to Ward's method; see argument \code{method} in \code{?hclust} for more details. #' @param nclust an integer indicating the number of clusters to find; if not provided, an interactive process based on cutoff threshold selection is used. #' @param ... further arguments to be passed to \code{treespace} #' #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @import ape #' @importFrom stats hclust #' @importFrom stats dist #' @importFrom stats cutree #' @importFrom graphics plot #' @importFrom graphics abline #' #' #' @seealso \code{\link{plotGroves}} to display results #' #' @return #' A list containing: #' \itemize{ #' \item groups: a factor defining groups of trees #' \item treespace: the output of treespace #' } #' #' @examples #' #' if(require("adegenet") && require("adegraphics")){ #' ## load data #' data(woodmiceTrees) #' #' ## run findGroves: treespace+clustering #' res <- findGroves(woodmiceTrees, nf=5, nclust=6) #' #' ## plot results on first 2 axes #' PCs <- res$treespace$pco$li #' s.class(PCs, fac=res$groups, col=funky(6)) #' #' ## using plotGroves #' plotGroves(res) #' } #' #' #' @export findGroves <- function(x, method="treeVec", nf=NULL, clustering="ward.D2", nclust=NULL, ...){ ## CHECK input type ## if (inherits(x, "multiPhylo")) { ## GET OUTPUT OF TREESPACE ## type <- "multiPhylo_object" res <- treespace(x, method=method, nf=nf, ...) } else if (inherits(x, "list")) { # test if it is an output from treespace inherits(x$D,"dist") inherits(x$pco,c("pco","dudi")) type <- "treespace_output" res <- x } else stop("x should be a multiphylo object or output of function treespace") ## GET CLUSTERS ## ## hierharchical clustering clust <- hclust(dist(res$pco$li), method=clustering) ## select nclust interactively if needed if(is.null(nclust)){ ans <- NA continue <- TRUE while(is.na(ans) || continue){ plot(clust) cat("\nPlease define a cutoff point: ") ans <- as.double(readLines(n = 1)) abline(h=ans, col="royalblue", lty=2) cat("\nAre you happy with this choice (y/n): ") continue <- as.character(readLines(n = 1))!="y" } grp <- cutree(clust, h=ans) } else { ## cut tree grp <- cutree(clust, k=nclust) } ## BUILD RESULT AND RETURN ## # retrieve tree names: if (type=="multiPhylo_object") names(grp) <- names(x) if (type=="treespace_output") names(grp) <- colnames(as.matrix(x$D)) out <- list(groups=factor(grp), treespace=res) return(out) } # end findGroves treespace/R/metrics.R0000644000176200001440000005576513207515317014245 0ustar liggesusers#' Linear MRCA function #' #' Function to make the most recent common ancestor (MRCA) matrix of a tree, where entry (i,j) gives the MRCA of tips i and j. #' The function is linear, exploiting the fact that the tree is rooted. #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param tree an object of the class \code{phylo} which should be rooted. #' @param k (optional) number of tips in tree, for faster computation #' #' @importFrom phangorn Descendants #' @importFrom phangorn Children #' @importFrom combinat combn #' @importFrom compiler cmpfun #' #' @examples #' #' ## generate a random tree #' x <- rtree(6) #' #' ## create matrix of MRCAs: entry (i,j) is the node number of the MRCA of tips i and j #' linearMrca(x,6) #' #' #' @export linearMrca <- function(tree,k=0) { # k is number of tips, which can be passed to the function to save on computation if(!is.rooted(tree)){stop("This function requires the tree to be rooted")} if (k==0) {k <- length(tree$tip.label)} M <- matrix(0, nrow=k, ncol=k); # initialise matrix T <- tree$Nnode # total number of internal nodes # traverse internal nodes from root down for (tmp in (k+1):(k+T)){ # find the children of tmp. Then tmp is the MRCA of all pairs of tips descending from different children tmp.desc <- Children(tree,tmp) Desc <- sapply(1:length(tmp.desc), function(x) Descendants(tree,tmp.desc[[x]],type="tips")) if (length(tmp.desc)==2) { # tmp is the MRCA of tips descending from child one and tips from child two I <- Desc[[1]]; J <- Desc[[2]] for (i in I) { for (j in J) { M[i,j] <- M[j,i] <- tmp } } } else { # for each pair of children of tmp, tmp is the MRCA of their descendant tips pairs <- combn(length(Desc),2) for (p in 1:length(pairs[1,])) { for (i in Desc[[pairs[1,p]]]) { for (j in Desc[[pairs[2,p]]]) { M[i,j] <- M[j,i] <- tmp } } } } } diag(M) <- 1:k # we define the diagonal elements of M to be the tips themselves return(M) } linearMrca <- compiler::cmpfun(linearMrca) # compile #' Tree vector function #' #' Function which takes an object of class phylo and outputs the vector for the Kendall Colijn metric. #' The elements of the vector are numeric if \code{return.lambda.function=FALSE} (default), #' and otherwise they are functions of lambda. #' #' @author Jacob Almagro-Garcia \email{nativecoder@@gmail.com} #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param tree an object of the class \code{phylo} #' @param lambda a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}. #' @param return.lambda.function If true, a function that can be invoked with different lambda values is returned. This function returns the vector of metric values for the given lambda. #' @param emphasise.tips an optional list of tips whose entries in the tree vector should be emphasised. Defaults to \code{NULL}. #' @param emphasise.weight applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised. #' #' @return The vector of values according to the metric, or a function that produces the vector given a value of lambda. #' #' @import ape #' @importFrom Rcpp evalCpp #' @importFrom combinat combn2 #' @useDynLib treespace #' #' @examples #' #' ## generate a random tree #' tree <- rtree(6) #' ## topological vector of mrca distances from root: #' treeVec(tree) #' ## vector of mrca distances from root when lambda=0.5: #' treeVec(tree,0.5) #' ## vector of mrca distances as a function of lambda: #' vecAsFunction <- treeVec(tree,return.lambda.function=TRUE) #' ## evaluate the vector at lambda=0.5: #' vecAsFunction(0.5) #' #' #' @export treeVec <- function(tree, lambda=0, return.lambda.function=FALSE, emphasise.tips=NULL, emphasise.weight=2) { if(lambda<0 || lambda>1) stop("Pick lambda in [0,1]") if(class(tree)!="phylo") stop("Tree should be of class phylo") if(is.rooted(tree)!=TRUE) stop("Metric is for rooted trees only") # check edge lengths are defined; if not, assign them all to be "1". # If edge lengths are going to be involved in the vector, i.e. lambda>0 or the vector is a function of lambda, output a warning about this. # With thanks to GitHub user hyanwong for pointing out the unnecessary warning in issue #2. if (is.null(tree$edge.length)) { tree$edge.length <- rep(1,length(tree$edge)) if ((return.lambda.function==TRUE)||(lambda>0)) { warning("Tree edge lengths are not defined, setting edges to have length 1") } } num_leaves <- length(tree$tip.label) num_edges <- nrow(tree$edge) # We work with ordered labels, using this vector to transform indices. tip_order <- match(1:num_leaves, order(tree$tip.label)) # Ordering the edges by first column places the root at the bottom. # Descendants will be placed always before parents. edge_order <- ape::reorder.phylo(tree, "postorder", index.only=T) edges <- tree$edge[edge_order,] edge_lengths <- tree$edge.length[edge_order] # To emphasise the position of certain tips, if needed: if (is.null(emphasise.tips)==FALSE){ # check tip labels are recognised: unknownTips <- setdiff(emphasise.tips,tree$tip.label) if (length(unknownTips)>0) {stop(paste('Tip "',unknownTips,'" not recognised. ', sep=''))} # translate important tip label names into order emphasise.tips.order <- tip_order[which(tree$tip.label%in%emphasise.tips)] # find the positions where these tips appear in the k choose 2 elements of the final vector pairs <- combn2(1:num_leaves) emphasise.vector.elements <- union(which(pairs[,1]%in%emphasise.tips.order ),which(pairs[,2]%in%emphasise.tips.order )) # make a vector to multiply positions concerning important tips by chosen weighting tip.weighting <- c(sapply(1:length(pairs[,1]), function(x) if(x%in%emphasise.vector.elements){emphasise.weight} else{1}), sapply(1:num_leaves, function(y) if(y%in%emphasise.tips.order){emphasise.weight} else{1})) } else{tip.weighting <- rep(1,0.5*num_leaves*(num_leaves+1))} # We annotated the nodes of the tree in this list. In two passes we are going to # compute the partition each node induces in the tips (bottom-up pass) and the distance # (in branch length and number of branches) from the root to each node (top-down pass). annotated_nodes <- list() # Bottom up (compute partitions, we store the branch lengths to compute distances # to the root on the way down). for(i in 1:num_edges) { parent <- edges[i,1] child <- edges[i,2] # Initialization (leaves). if(child <= num_leaves) { # We translate the index for the sorted labels. child <- tip_order[child] # Leaves have as children themselves. annotated_nodes[[child]] <- list(root_distance=NULL, edges_to_root=1, partitions=list(child)) } # Aggregate the children partitions (only if we are not visiting a leaf). aggregated_partitions <- annotated_nodes[[child]]$partitions[[1]] if((child > num_leaves)) { for(p in 2:length(annotated_nodes[[child]]$partitions)) aggregated_partitions <- c(aggregated_partitions, annotated_nodes[[child]]$partitions[[p]]) } # Update the branch length on the child. annotated_nodes[[child]]$root_distance <- edge_lengths[i] # We have not visited this internal node before. if(parent > length(annotated_nodes) || is.null(annotated_nodes[[parent]])) { # Assume the first time we get the left child partition. annotated_nodes[[parent]] <- list(root_distance=NULL, edges_to_root=1, partitions=list(aggregated_partitions)) } # This is not the first time we have visited the node. else { # We store the next partition of leaves. annotated_nodes[[parent]]$partitions[[length(annotated_nodes[[parent]]$partitions)+1]] <- aggregated_partitions } } # Update the distance to the root at the root (i.e. 0) # And the number of edges to the root (i.e. 0). annotated_nodes[[num_leaves+1]]$root_distance <- 0 annotated_nodes[[num_leaves+1]]$edges_to_root <- 0 # Top down, compute distances to the root for each node. for(i in num_edges:1) { parent <- edges[i,1] child <- edges[i,2] # If the child is a leaf we translate the index for the sorted labels. if(child <= num_leaves) child <- tip_order[child] annotated_nodes[[child]]$root_distance <- annotated_nodes[[child]]$root_distance + annotated_nodes[[parent]]$root_distance annotated_nodes[[child]]$edges_to_root <- annotated_nodes[[child]]$edges_to_root + annotated_nodes[[parent]]$edges_to_root } # Distance vectors vector_length <- (num_leaves*(num_leaves-1)/2) + num_leaves length_root_distances <- double(vector_length) topological_root_distances <- integer(vector_length) # Fill-in the leaves (notice the involved index translation for leaves). topological_root_distances[(vector_length-num_leaves+1):vector_length] <- 1 length_root_distances[(vector_length-num_leaves+1):vector_length] <- edge_lengths[match(1:num_leaves, edges[,2])][order(tree$tip.label)] # Instead of computing the lexicographic order for the combination pairs assume we # are filling in a symmetric distance matrix (using only the triangular upper part). # We just need to "roll" the matrix indices into the vector indices. # Examples for (k=5) # The combination c(1,4) would be located at position 3 on the vector. # The combination c(2,1) would be located at position 1 on the vector because d(2,1) = d(1,2). # The combination c(2,3) would be located at position 5 on the vector. index_offsets <- c(0, cumsum((num_leaves-1):1)) # This is the slow part, we compute both vectors as gain would be marginal. sapply(annotated_nodes, function(node) { # We skip leaves and the root (if the MRCA for M groups of leaves is at the root # all combinations of leaves -among different groups- have 0 as distance to the root). # For large trees this can spare us of computing a lot of combinations. # Example: In a perfectly balanced binary tree (N/2 leaves at each side of the root), # at the root we'd save (N/2) * (N/2) combinations to update. Worst case scenario is completely # unbalanced tree (N-1,1), we'd save in that case only N-1 combinations. # Make sure we are not visiting a leaf or the root. if(length(node$partitions) > 1 && node$root_distance > 0) { # Update all combinations for pairs of leaves from different groups. num_groups <- length(node$partitions) for(group_a in 1:(num_groups-1)) { for(group_b in (group_a+1):num_groups) { updateDistancesWithCombinations(length_root_distances, topological_root_distances, node$partitions[[group_a]], node$partitions[[group_b]], index_offsets, node$root_distance, node$edges_to_root) } } } }) if(!return.lambda.function) return(tip.weighting * (lambda * length_root_distances + (1-lambda) * topological_root_distances)) else { return(function(l) { if(l<0 || l>1) stop("Pick lambda in [0,1]") return(tip.weighting * (l * length_root_distances + (1-l) * topological_root_distances)) }) } } #' Metric function #' #' Comparison of two trees using the Kendall Colijn metric #' #' @author Jacob Almagro-Garcia \email{nativecoder@@gmail.com} #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param tree.a an object of the class \code{phylo} #' @param tree.b an object of the class \code{phylo} (with the same tip labels as tree.a) #' @param lambda a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}. #' @param return.lambda.function If true, a function that can be invoked with different lambda values is returned. #' This function returns the vector of metric values for the given lambda. #' @param emphasise.tips an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}. #' @param emphasise.weight applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised. #' #' @return The distance between the two trees according to the metric for the given value of lambda, or a function that produces the distance given a value of lambda. #' #' #' @import ape #' #' #' @examples #' #' ## generate random trees #' tree.a <- rtree(6) #' tree.b <- rtree(6) #' treeDist(tree.a,tree.b) # lambda=0 #' treeDist(tree.a,tree.b,1) # lambda=1 #' dist.func <- treeDist(tree.a,tree.b,return.lambda.function=TRUE) # distance as a function of lambda #' dist.func(0) # evaluate at lambda=0. Equivalent to treeDist(tree.a,tree.b). #' ## We can see how the distance changes when moving from focusing on topology to length: #' plot(sapply(seq(0,1,length.out=100), function(x) dist.func(x)), type="l",ylab="",xlab="") #' #' ## The distance may also change if we emphasise the position of certain tips: #' plot(sapply(tree.a$tip.label, function(x) treeDist(tree.a,tree.b,emphasise.tips=x)), #' xlab="Tip number",ylab="Distance when vector entries corresponding to tip are doubled") #' #' #' @export treeDist <- function(tree.a, tree.b, lambda=0, return.lambda.function=FALSE, emphasise.tips=NULL, emphasise.weight=2) { if(length(tree.a$tip.label) != length(tree.b$tip.label)) stop("Trees must have the same number of tips") if(setequal(tree.a$tip.label,tree.b$tip.label) == FALSE) stop("Trees must have the same tip label sets") metric_a <- treeVec(tree.a, lambda, return.lambda.function, emphasise.tips, emphasise.weight) metric_b <- treeVec(tree.b, lambda, return.lambda.function, emphasise.tips, emphasise.weight) if(!return.lambda.function) { return(sqrt(sum((metric_a - metric_b)^2))) } else { return(function(l) { return(sqrt(sum((metric_a(l) - metric_b(l))^2))) }) } } #' Metric function for \code{multiPhylo} input #' #' Comparison of a list of trees using the Kendall Colijn metric. Output is given as a pairwise distance matrix. This is equivalent to the \code{$D} output from \code{treespace} but may be preferable for large datasets, and when principal co-ordinate analysis is not required. It includes an option to save memory at the expense of computation time. #' #' @author Jacob Almagro-Garcia \email{nativecoder@@gmail.com} #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param trees an object of the class \code{multiPhylo} containing the trees to be compared #' @param lambda a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}. #' @param return.lambda.function If true, a function that can be invoked with different lambda values is returned. #' This function returns the matrix of metric values for the given lambda. #' @param save.memory A flag that saves a lot of memory but increases the execution time (not compatible with return.lambda.function=TRUE). #' @param emphasise.tips an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}. #' @param emphasise.weight applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised. #' #' @return The pairwise tree distance matrix or a function that produces the distance matrix given a value for lambda. #' #' #' @import ape #' @importFrom fields rdist #' @importFrom stats as.dist #' #' #' @examples #' #' ## generate 10 random trees, each with 6 tips #' trees <- rmtree(10,6) #' #' ## pairwise distance matrix when lambda=0 #' multiDist(trees) #' #' ## pairwise distance matrix as a function of lambda: #' m <- multiDist(trees, return.lambda.function=TRUE) #' #' ## evaluate at lambda=0. Equivalent to multiDist(trees). #' m0 <- m(0) #' #' ## save memory by recomputing each tree vector for each pairwise tree comparison (for fixed lambda): #' m0.5 <- multiDist(trees,0.5,save.memory=TRUE) #' #' #' @export multiDist <- function(trees, lambda=0, return.lambda.function=FALSE, save.memory=FALSE, emphasise.tips=NULL, emphasise.weight=2) { if(!inherits(trees, "multiPhylo")) stop("trees should be a multiphylo object") num_trees <- length(trees) if(num_trees<2) { stop("multiDist expects at least two trees") } # make name labels well defined if(is.null(names(trees))) names(trees) <- 1:num_trees else if(length(unique(names(trees)))!=num_trees){ warning("duplicates detected in tree labels - using generic names") names(trees) <- 1:num_trees } lab <- names(trees) # check all trees have same tip labels for (i in 1:num_trees) { if (!setequal(trees[[i]]$tip.label,trees[[1]]$tip.label)) { stop(paste0("Tree ",lab[[i]]," has different tip labels from the first tree.")) } } # Working with numbers (no functions). if(!return.lambda.function) { # Here we speed up the computation by storing all vectors (a lot of memory for big trees). if(!save.memory) { # Compute the metric vector for all trees. tree_metrics <- t(sapply(trees, function(tree) {treeVec(tree, lambda, F, emphasise.tips, emphasise.weight)})) distances <- rdist(tree_metrics) } # To save memory we recompute the vectors for each tree comparison (way slower but we don't eat a ton of memory). else { distances <- matrix(0.0, num_trees, num_trees) sapply(1:(num_trees-1), function(i) { sapply((i+1):num_trees, function(j) { distances[i,j] <<- distances[j,i] <<- treeDist(trees[[i]], trees[[j]], lambda, F, emphasise.tips, emphasise.weight) }) }) } return(as.dist(distances)) } # Working with functions. else { if(save.memory) warning("save.memory=TRUE is incompatible with return.lambda.function=TRUE, setting save.memory=FALSE") # Compute the list of metric functions for all trees. tree_metric_functions <- sapply(trees, function(tree) {treeVec(tree, lambda, T, emphasise.tips, emphasise.weight)}) # Inner function that we'll return, computes the distance matrix given lambda. compute_distance_matrix_function <- function(l) { distances <- matrix(0.0, num_trees, num_trees) sapply(1:(num_trees-1), function(i) { sapply((i+1):num_trees, function(j) { distances[i,j] <<- distances[j,i] <<- sqrt(sum((tree_metric_functions[[i]](l) - tree_metric_functions[[j]](l))^2)) }) }) return(as.dist(distances)) } return(compute_distance_matrix_function) } } #' Metric function for comparing a reference \code{phylo} to \code{multiPhylo} input #' #' Comparison of a single reference tree to a list of trees using the Kendall Colijn metric. Output is given as a vector of distances from the reference tree. #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param refTree a tree of class \code{phylo}, the "reference tree". #' @param trees an object of the class \code{multiPhylo} containing the trees to be compared to the reference tree #' @param lambda a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}. #' @param return.lambda.function If true, a function that can be invoked with different lambda values is returned. #' This function returns the vector of metric values for the given lambda. #' @param emphasise.tips an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}. #' @param emphasise.weight applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised. #' #' @return The vector of distances, where entry i corresponds to the distance between the i'th tree and the reference tree, or a function that produces the vector of distances given a value for lambda. #' #' #' @import ape #' @importFrom stats as.dist #' #' #' @examples #' #' ## generate a single reference tree with 6 tips #' refTree <- rtree(6) #' #' ## generate 10 random trees, each with 6 tips #' trees <- rmtree(10,6) #' #' ## find the distances from each of the 10 random trees to the single reference tree #' refTreeDist(refTree,trees) #' #' @export refTreeDist <- function(refTree, trees, lambda=0, return.lambda.function=FALSE, emphasise.tips=NULL, emphasise.weight=2) { if(!inherits(refTree, "phylo")) stop("refTree should be a phylo object") if(!inherits(trees, "multiPhylo")) stop("trees should be a multiphylo object") num_trees <- length(trees) # make name labels well defined if(is.null(names(trees))) names(trees) <- 1:num_trees else if(length(unique(names(trees)))!=num_trees){ warning("duplicates detected in tree labels - using generic names") names(trees) <- 1:num_trees } lab <- names(trees) # check all trees have same tip labels for (i in 1:num_trees) { if (!setequal(trees[[i]]$tip.label,refTree$tip.label)) { stop(paste0("Tree ",lab[[i]]," has different tip labels from the reference tree.")) } } # Working with numbers (no functions). if(!return.lambda.function) { # compute reference tree vector, which will be used repeatedly refVec <- treeVec(refTree, lambda, F, emphasise.tips, emphasise.weight) # for each tree, compute its vector and store Euclidean distance from refVec distances <- sapply(trees, function(x) { tmpVec <- treeVec(x, lambda, F, emphasise.tips, emphasise.weight) sqrt(sum((refVec-tmpVec)^2)) }) return(as.vector(distances)) } # Working with functions. else { # compute reference tree vector function refVec <- treeVec(refTree, lambda, T, emphasise.tips, emphasise.weight) # Compute the list of metric functions for all trees. treeVecs <- sapply(trees, function(x) treeVec(x, lambda, T, emphasise.tips, emphasise.weight)) # Inner function that we'll return, computes the distance matrix given lambda. compute_distance_vector_function <- function(l) { sapply(1:num_trees, function(x) sqrt(sum((refVec(l)-treeVecs[[x]](l))^2))) } return(compute_distance_vector_function) } } treespace/R/medTree.R0000644000176200001440000002366213164413031014143 0ustar liggesusers#' Geometric median tree function #' #' Finds the geometric median of a set of trees according to the Kendall Colijn metric. #' #' @author Jacob Almagro-Garcia \email{nativecoder@@gmail.com} #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} #' #' @param x A list of trees of the class multiPhylo, for which the median tree will be computed, \cr #' OR a matrix of tree vectors as given by \code{treespace$vectors}. #' @param groups an optional factor defining groups of trees; if provided, one median tree will be found for each group. #' @param lambda a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE} or if the vectors are already supplied as the object \code{x}. #' @param weights A vector of weights for the trees. Defaults to a vector of 1's so that all trees are equally weighted, but can be used to encode likelihood, posterior probabilities or other characteristics. #' @param emphasise.tips an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}. #' @param emphasise.weight applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised. #' @param return.lambda.function If true, a function that can be invoked with different lambda values is returned. #' This function returns the vector of metric values for the given lambda. Ignored if the tree vectors are already supplied as the object \code{x}. #' @param save.memory A flag that saves a lot of memory but increases the execution time (not compatible with return.lambda.function=TRUE). Ignored if the tree vectors are already supplied as the object \code{x}. #' #' @return A list of five objects: #' \itemize{ #' \item $centre is the "central vector", that is, the (weighted) mean of the tree vectors (which typically does not correspond to a tree itself); #' \item $distances gives the distance of each tree from the central vector; #' \item $mindist is the minimum of these distances; #' \item $treenumbers gives the numbers (and, if supplied, names) of the "median tree(s)", that is, the tree(s) which achieve this minimum distance to the centre; #' \item $trees if trees were supplied then this returns the median trees as a multiPhylo object. #' } #' If groups are provided, then one list is returned for each group. #' If \code{return.lambda.function=TRUE} then a function is returned that produces this list for a given value of lambda. #' #' #' @import ape #' #' #' @examples #' #' ## EXAMPLE WITH WOODMICE DATA #' data(woodmiceTrees) #' #' ## LOOKING FOR A SINGLE MEDIAN #' ## get median tree(s) #' res <- medTree(woodmiceTrees) #' res #' #' ## plot first tree #' med.tree <- res$trees[[1]] #' plot(med.tree) #' #' ## LOOKING FOR MEDIANS IN SEVERAL CLUSTERS #' ## identify 6 clusters #' groves <- findGroves(woodmiceTrees, nf=3, nclust=6) #' #' ## find median trees #' res.with.grp <- medTree(woodmiceTrees, groves$groups) #' #' ## there is one output per cluster #' names(res.with.grp) #' #' ## get the first median of each #' med.trees <- lapply(res.with.grp, function(e) ladderize(e$trees[[1]])) #' #' ## plot trees #' par(mfrow=c(2,3)) #' for(i in 1:length(med.trees)) plot(med.trees[[i]], main=paste("cluster",i)) #' #' ## highlight the differences between a pair of median trees #' plotTreeDiff(med.trees[[1]],med.trees[[5]]) #' #' @export medTree <- function(x, groups=NULL, lambda=0, weights=NULL, emphasise.tips=NULL, emphasise.weight=2, return.lambda.function=FALSE, save.memory=FALSE) { ## CHECK input type ## if (inherits(x, "multiPhylo")) { type <- "multiPhylo_object" } else if (inherits(x, "matrix")) { # x is a matrix of tree vectors type <- "tree_vectors" } else stop("x should be a multiphylo object or a matrix of tree vectors") if (type=="multiPhylo_object") { ## DEFINE MAIN FUNCTION FINDING MEDIAN TREE ## findMedianPhylo <- function(trees, weights){ ## checks, general variables num_trees <- length(trees) num_leaves <- length(trees[[1]]$tip.label) if(is.null(weights)) weights <- rep(1, num_trees) if(length(weights)!=num_trees) stop("Length of vector of weights must be the same as number of trees") ## Working with numbers (no functions). if(!return.lambda.function) { ## Here we speed up the computation by storing all vectors (a lot of memory for big trees). if(!save.memory) { ## Compute the metric vector for all trees. tree_metrics <- t(sapply(trees, function(tree) {treeVec(tree, lambda, emphasise.tips, emphasise.weight, return.lambda.function=F)})) ## Compute the centre metric vector by weighting the metric vector of each tree. centre <- (weights %*% tree_metrics)/num_trees ## Distances to the centre. distances <- apply(tree_metrics, 1, function(m){sqrt(sum((m-centre)^2))}) ## Get the indices for the median tree(s). min_distance <- min(distances) median_trees <- which(min_distance == distances) return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees, trees=trees[median_trees])) } ## To save memory we recompute the vectors on the fly (way slower but we don't eat a ton of memory). ## We'll need a first pass to compute the centre and a second pass to compute distances. else { ## First pass: compute the centre. centre <- rep(0,(num_leaves*(num_leaves-1)/2) + num_leaves) for(i in 1:num_trees) { centre <- centre + treeVec(trees[[i]], lambda, F) * weights[i] } centre <- centre/num_trees ## Second pass: compute the distances. distances <- rep(NA,num_trees) for(i in 1:num_trees) { distances[i] <- sqrt(sum((treeVec(trees[[i]], lambda, F) - centre)^2)) } ## Get the indices for the median tree(s). min_distance <- min(distances) median_trees <- which(min_distance == distances) return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees, trees=trees[median_trees])) } } ## Working with functions. else { if(save.memory) warning("save.memory=TRUE is incompatible with return.lambda.function=TRUE, setting save.memory=FALSE") ## Compute the list of metric functions for all trees. tree_metric_functions <- sapply(trees, function(tree) {treeVec(tree, lambda, emphasise.tips, emphasise.weight, return.lambda.function=T)}) ## Inner function that we'll return, computes the distance matrix given lambda. compute_median_tree_function <- function(l) { ## Compute the tree metrics for the given lambda. tree_metrics <- t(sapply(tree_metric_functions, function(tmf){tmf(l)})) ## Compute the centre metric vector by weighting the metric vector of each tree. centre <- (weights %*% tree_metrics)/num_trees ## Distances to the centre. distances <- apply(tree_metrics, 1, function(m){sqrt(sum((m-centre)^2))}) ## Get the indices for the median tree(s). min_distance <- min(distances) median_trees <- which(min_distance == distances) return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees, trees=trees[median_trees])) } return(compute_median_tree_function) } } # end findMedian ## APPLY FUNCTION TO TREES ## if(is.null(groups)){ ## no groups provided out <- findMedianPhylo(x, weights) } else { ## groups provided out <- tapply(x, groups, findMedianPhylo, weights) } } # end if multiPhylo object if (type=="tree_vectors"){ ## Can define a much simpler version of the function to find a median tree ## findMedianVectors <- function(vectors, weights){ ## checks, general variables num_trees <- length(vectors[,1]) if(is.null(weights)) {weights <- rep(1,num_trees)} if(length(weights)!=num_trees) stop("Length of vector of weights must be the same as number of tree vectors") tree_metrics <- vectors ## Compute the centre metric vector by weighting the metric vector of each tree. centre <- (weights %*% tree_metrics)/num_trees ## Distances to the centre. distances <- apply(tree_metrics, 1, function(m){sqrt(sum((m-centre)^2))}) ## Get the indices for the median tree(s). min_distance <- min(distances) median_trees <- which(min_distance == distances) ## Note we cannot return $trees because the trees were not supplied! return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees)) } # end findMedianVectors ## APPLY FUNCTION TO TREES ## if(is.null(groups)){ ## no groups provided out <- findMedianVectors(x, weights) } else { ## groups provided # need to first convert the vector matrix into a list: mylist <- lapply(1:length(x[,1]), function(a) x[a,]) # and then coerce back into matrix within the function. # Room for improvement here! out <- tapply(mylist, groups, function(a) {findMedianVectors(t(sapply(a, function(b) b)), weights)}) } } ## RETURN ## return(out) } ## end medTree treespace/R/makeCollapsedTree.R0000644000176200001440000000750713225446251016152 0ustar liggesusers#' Collapse a tree into a single tip per category #' #' Reduce a tree with many tips into a tree with a single tip per category. #' Where a category's tips form a monophyletic clade, the clade is replaced by a single tip labelled by that category. #' Where a category's tips are paraphyletic, the largest clade for that category is treated as above, and all other tips pruned. #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @import ape #' @importFrom phangorn Descendants #' #' @param tree an object of the class \code{phylo}: the tree to collapse. #' @param df a two-column data frame linking tip labels (column 2) with their corresponding categories (column 1). #' @param warnings a logical determining whether a warning should be given if there are paraphyletic categories (default TRUE) #' #' @return #' A tree (class \code{phylo}) whose tip labels are exactly the set of unique categories from \code{df}. #' #' @seealso \code{\link{treeConcordance}} \code{\link{simulateIndTree}} #' #' @examples #' # simulate a tree which is monophyletic per category #' tree <- simulateIndTree(rtree(5), permuteTips=FALSE) #' #' df <- cbind(sort(rep(rtree(5)$tip.label,5)),sort(tree$tip.label)) #' palette <- c("red","blue","black","green","purple")#' #' tipCols <- palette[as.factor(sapply(tree$tip.label, function(l) df[which(df[,2]==l),1]))] #' plot(tree, tip.color=tipCols) #' collapsedTree <- makeCollapsedTree(tree,df) #' plot(collapsedTree, tip.color=palette[as.factor(collapsedTree$tip.label)]) #' #' # simulate a tree which is paraphyletic per category #' tree <- simulateIndTree(rtree(5), tipPercent=20) #' tipCols <- palette[as.factor(sapply(tree$tip.label, function(l) df[which(df[,2]==l),1]))] #' plot(tree, tip.color=tipCols) #' collapsedTree <- makeCollapsedTree(tree,df) #' plot(collapsedTree, tip.color=palette[as.factor(collapsedTree$tip.label)]) #' #' @export makeCollapsedTree <- function(tree,df,warnings=TRUE){ # check whether tips are monophyletic per category, # if not, pick a "representative" tip for each category from the largest clade categories <- unique(df[,1]) l <- length(tree$tip.label) n <- Nnode(tree) tipDescendants <- Descendants(tree, type="tips") inds_by_category <- split(df[,2], factor(df[,1], categories)) category_is_mono <- sapply(inds_by_category, function(i) if (length(i)==1) return(TRUE) else any(sapply(tipDescendants, function(j) setequal(i,tree$tip.label[j]) )) ) mono_categories <- categories[category_is_mono] tipsToKeep <- vector() if (any(!category_is_mono)) { if(warnings==TRUE) print("Note: the tree was not monophyletic per category") for (c in categories[!category_is_mono]){ tips <- inds_by_category[[c]] # find clade sizes of nodes whose descendant tips all belong to this category clade_sizes_of_this_category <- sapply(1:(n+l), function(x) { tmp <- length(intersect(tree$tip.label[tipDescendants[[x]]],tips)) # number of descendants of x which are tips from this category if (tmp==length(tipDescendants[[x]])) return(tmp) # if all tip descendants are from this category, return the number of them else return(0) }) # keep one tip from the largest clade: tipsToKeep <- union(tipsToKeep,tree$tip.label[tipDescendants[[which.max(clade_sizes_of_this_category)[[1]]]]][[1]]) } } tipsToKeep <- union(tipsToKeep, sapply(inds_by_category[category_is_mono], function(t) t[[1]])) tipsToDrop <- setdiff(tree$tip.label, tipsToKeep) collapsedTree <- drop.tip(tree, tip=tipsToDrop, collapse.singles = TRUE) # pruning step collapsedTree$tip.label <- as.character(sapply(collapsedTree$tip.label, function(t) df[which(df[,2]==t),1])) # relabel the tips by category name return(collapsedTree) } treespace/R/data.R0000644000176200001440000001015113164413031013454 0ustar liggesusers#' Bootstrap trees from woodmouse dataset #' #' These trees were created using the neighbour-joining and bootstrapping #' example from the ape documentation. #' #' #' @name woodmiceTrees #' @docType data #' @format A multiPhylo object containing 201 trees, each with 15 tips #' @references 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. \emph{Molecular Ecology}, 12, #' 685-697 #' @source A set of 15 sequences of the mitochondrial gene cytochrome b of the #' woodmouse (Apodemus sylvaticus) which is a subset of the data analysed by #' Michaux et al. (2003). The full data set is available through GenBank #' (accession numbers AJ511877 to AJ511987) #' @keywords datasets #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} NULL #' BEAST analysis of seasonal influenza (A/H3N2) #' #' These trees were created using BEAST on hemagglutinin (HA) segments #' of seasonal influenza A/H3N2 samples collected in New-York city (US) between 2000 and 2003. This data comes from the influenza BEAST tutorial distributed at: #' http://beast.bio.ed.ac.uk/tutorials #' #' Only the first 200 trees (out of 10,000) were retained. #' #' @name fluTrees #' @docType data #' @format A multiPhylo object containing 200 trees, each with 165 tips #' @references http://beast.bio.ed.ac.uk/tutorials #' @source http://beast.bio.ed.ac.uk/tutorials #' @keywords datasets #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} NULL #' Dengue fever sequences #' #' 17 dengue virus serotype 4 sequences from Lanciotti et al. (1997) #' #' @name DengueSeqs #' @docType data #' @format A DNAbin object containing 17 DNA sequences, each of length 1485. #' @references Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) #' Molecular evolution and phylogeny of dengue-4 viruses. #' \emph{Journal of General Virology}, 78(9), 2279-2286. #' @source http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-7-214 #' @keywords datasets #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} NULL #' BEAST analysis of Dengue fever #' #' These trees were created using one of the \code{xml} files provided with the original BEAST paper by Drummond and Rambaut (2007). #' They provide an example of 17 dengue virus serotype 4 sequences from Lanciotti et al. (1997) (available as \code{\link{DengueSeqs}}) and \code{xml} files with varying priors for model and clock rate. #' Here we include a random sample of 500 of the trees (from the second half of the posterior) produced using BEAST v1.8 with the standard GTR + Gamma + I substitution model with uncorrelated lognormal-distributed relaxed molecular clock (file 4). #' #' @name DengueTrees #' @docType data #' @format A multiPhylo object containing 500 trees, each with 17 tips #' @references Drummond, A. J., and Rambaut, A. (2007) #' BEAST: Bayesian evolutionary analysis by sampling trees. #' \emph{BMC Evolutionary Biology}, 7(1), 214. #' #' Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) #' Molecular evolution and phylogeny of dengue-4 viruses. #' \emph{Journal of General Virology}, 78(9), 2279-2286. #' @source http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-7-214 #' @keywords datasets #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} NULL #' Dengue fever BEAST MCC tree #' #' The maximum clade credibility (MCC) tree from \code{\link{DengueTrees}} #' #' @name DengueBEASTMCC #' @docType data #' @format A phylo object #' @references Drummond, A. J., and Rambaut, A. (2007) #' BEAST: Bayesian evolutionary analysis by sampling trees. #' \emph{BMC Evolutionary Biology}, 7(1), 214. #' #' Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) #' Molecular evolution and phylogeny of dengue-4 viruses. #' \emph{Journal of General Virology}, 78(9), 2279-2286. #' @source http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-7-214 #' @keywords datasets #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} NULLtreespace/R/treeConcordance.R0000644000176200001440000001012113227077655015660 0ustar liggesusers#' Tree concordance #' #' This function calculates the concordance between a category tree and an individuals tree #' #' @param catTree object of class phylo #' @param indTree object of class phylo #' @param df data frame specifying to which category each individual belongs. Each row gives an individual (column 2) and its corresponding category (column 1) #' #' @import ape #' #' @examples #' # create an example category tree #' catTree <- read.tree(text="(C:1,(B:1,A:1):1);") #' plot(catTree) #' #' # make individuals tree with complete concordance: #' indTree1 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,b2),((a3,a2),a1)));") #' plot(indTree1) #' #' #' # create data frame linking categories with individuals #' df <- cbind(c(rep("A",3),rep("B",2),rep("C",4)),sort(indTree1$tip.label)) #' #' treeConcordance(catTree,indTree1,df) #' #' # make a less concordant tree: #' indTree2 <- read.tree(text="((((c4,c3),(c2,c1)),b2),(b1,((a3,a2),a1)));") #' plot(indTree2) #' treeConcordance(catTree,indTree2,df) #' #' # simulate larger example: #' catTree <- rtree(10) #' indTree3 <- simulateIndTree(catTree, tipPercent=10) #' df <- cbind(sort(rep(catTree$tip.label,5)),sort(indTree3$tip.label)) #' plot(indTree3) #' treeConcordance(catTree,indTree3,df) #' #' @seealso \code{\link{simulateIndTree}} \code{\link{makeCollapsedTree}} #' #' @export treeConcordance <- function(catTree,indTree,df) { # find the number of pairs of different individuals: catTree_leaves <- length(catTree$tip.label) indTree_leaves <- length(indTree$tip.label) if (length(unique(catTree$tip.label))!=catTree_leaves) stop("Each tip in the category tree must have a unique label") if (length(unique(indTree$tip.label))!=indTree_leaves) stop("Each tip in the individuals tree must have a unique label") catTree_pairs <- catTree_leaves * (catTree_leaves-1)/2 indTree_pairs <- indTree_leaves * (indTree_leaves -1)/2 # function to find category corresponding to individual: sigma <- function(g) { s_row <- which(df[,2]==g) s <- as.character(df[s_row,1]) if (length(s)!= 1) stop("Problem with matching individual to category: check data frame") return(s) } # check category tree has exactly one tip for every category in df, # and that each category is represented at least once in indTree: categories <- unique(df[,1]) # category names # create a list of vectors: categories with their individuals. Essentially another, easier to parse, representation of df inds_by_category <- split(df[,2], factor(df[,1], categories)) if (length(categories)!=catTree_leaves) stop("The number of categories in df does not match the number of leaves in catTree") for (c in categories) { if (!(c %in% catTree$tip.label)) stop(paste0("Category ",c," from is missing from the catTree")) if (length(intersect(inds_by_category[[c]],indTree$tip.label))==0) stop(paste0("indTree is missing any individuals from category ",c)) } # get the tip MRCA depths for both trees catTreeDepths <- tipsMRCAdepths(catTree) indTreeDepths <- tipsMRCAdepths(indTree) # now go through the individuals tree vector, for each column: # look up corresponding categories in df using sigma # find root distance in category tree vector # record whether they match or not paircount=0 con <- vector() for (i in 1:indTree_pairs){ # get the names of the two tips indTree_tip1 <- indTreeDepths[i,1] indTree_tip2 <- indTreeDepths[i,2] # find their category-level labels catTree_tips <- c(sigma(indTree_tip1), sigma(indTree_tip2)) # only count the tips with different category-level labels catTree_are_diff <- catTree_tips[1]!=catTree_tips[2] if (catTree_are_diff) { catTree_dist <- catTreeDepths[ intersect(which(catTreeDepths[,1] %in% catTree_tips), which(catTreeDepths[,2] %in% catTree_tips)) ,3] indTree_dist <- indTreeDepths[i,3] con[[i]] <- indTree_dist==catTree_dist paircount <- paircount + 1 } else { con[[i]] <- 0 } } return(sum(con)/paircount) } treespace/R/plotTreeDiff.R0000644000176200001440000002742613213502647015156 0ustar liggesusers#' Find tip position differences #' #' Find the topologicial differences between two trees with the same tip labels. The function returns a data frame of the tips and the number of differences in their ancestry between the two trees. #' Called by \code{\link{plotTreeDiff}}, which highlights the differing tips in a plot of the two trees. #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @import ape #' @importFrom combinat combn2 #' @importFrom adegenet num2col #' #' @param tr1 an object of the class \code{phylo}: the first tree to compare. #' @param tr2 an object of the class \code{phylo}: the second tree to compare. #' @param vec1 an optional input, the result of \code{treeVec(tr1, lambda=0)}, to speed up the computation. #' @param vec2 an optional input, the result of \code{treeVec(tr2, lambda=0)}, to speed up the computation. #' @param sizeOfDifferences a logical (default FALSE) specifying whether the size of the differences in the vectors per tip is also computed #' #' @return #' A data frame of the tree tips and the number of ancestral differences between them in the two trees, in order of increasing difference. #' A tip is said to have zero difference if each of its ancestral nodes admits the same tip partition in the two trees. #' #' @seealso \code{\link{medTree}} \code{\link{plotTreeDiff}} #' #' @examples #' ## simple example on trees with five tips: #' tr1 <- read.tree(text="((A:1,B:1):1,((C:1,D:1):1,E:1):1):1;") #' tr2 <- read.tree(text="((A:1,B:1):1,(C:1,(D:1,E:1):1):1):1;") #' tipDiff(tr1,tr2) #' #' ## example on larger woodmice trees #' data(woodmiceTrees) #' tipDiff(woodmiceTrees[[1]],woodmiceTrees[[2]]) #' #' @export tipDiff <- function(tr1,tr2,vec1=NULL,vec2=NULL,sizeOfDifferences=FALSE) { l <- length(tr1$tip.label) lchoose2 <- l*(l-1)/2 if( l != length(tr2$tip.label)) stop("Trees must have the same number of tips") if(setequal(tr1$tip.label,tr2$tip.label) == FALSE) stop("Trees must have the same tip label sets") # get vec1 if (is.null(vec1)) { vec1 <- treeVec(tr1) # emphasise.tips, emphasise.weight) } if (is.null(vec2)) { vec2 <- treeVec(tr2) # emphasise.tips, emphasise.weight) } # trim pendant edge entries from vectors vec1 <- vec1[1:lchoose2] # emphasise.tips, emphasise.weight) vec2 <- vec2[1:lchoose2] # find the positions where the vectors are different vecDiff <- (vec1!=vec2) # combine the tip labels (in order) and whether the vectors are different orderedLabels <- tr1$tip.label[order(tr1$tip.label)] treedf <- cbind.data.frame(combn2(orderedLabels),vecDiff) # extract the "TRUE" rows treeDiffsDf <- treedf[vecDiff,] tipsWithDifferencesRepeated <- unlist(list(treeDiffsDf[,1],treeDiffsDf[,2])) # list the tips with differences, possibly (probably) with repeats tipsWithDifferencesNames <- unique(tipsWithDifferencesRepeated) # get the names of the tips with differences (each listed just once) # find the number of times each tip appears tipSignificance <- sapply(orderedLabels, function(x) if (x %in% tipsWithDifferencesNames) length(which(tipsWithDifferencesRepeated==x)) else 0 ) # prepare output as data frame of tips and their differences orderOfSignificance <- order(tipSignificance) tipDiff <- cbind.data.frame(orderedLabels[orderOfSignificance],tipSignificance[orderOfSignificance]) rownames(tipDiff) <- NULL colnames(tipDiff) <- c("Tip","No. of differences") if (sizeOfDifferences) { # also compute the sizes of the differences treedfSizes <- cbind.data.frame(treedf[vecDiff,],abs(vec1[vecDiff]-vec2[vecDiff])) tipSizeOfDifferences <- sapply(orderedLabels[orderOfSignificance], function(x) sum(treedfSizes[which(treedfSizes[,1]==x),4]) + sum(treedfSizes[which(treedfSizes[,2]==x),4]) ) tipDiff <- cbind.data.frame(tipDiff,tipSizeOfDifferences) rownames(tipDiff) <- NULL colnames(tipDiff) <- c("Tip","No. of differences","Size of differences") } return(tipDiff) } #' Plot tree differences #' #' Highlight the topological differences between two trees, plotted side by side. #' This function is useful for comparing representative "median" trees - see \code{\link{medTree}}. #' It relies on the function \code{\link{tipDiff}}. #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @import ape #' @importFrom adegenet lightseasun #' @importFrom adegenet num2col #' @importFrom grDevices colorRampPalette #' @importFrom graphics layout #' #' @param tr1 an object of the class \code{phylo}: the first tree to plot. #' @param tr2 an object of the class \code{phylo}: the second tree to plot. #' @param tipDiff an optional input, the result of \code{\link{tipDiff}}. Supplying this will save time if calling \code{plotTreeDiff} repeatedly, for example with different aesthetics. #' @param vec1 an optional input, the result of \code{treeVec(tr1, lambda=0)}. This argument is ignored if \code{tipDiff} is supplied; otherwise supplying this will save time if calling \code{plotTreeDiff} repeatedly, for example with different aesthetics. #' @param vec2 an optional input, the result of \code{treeVec(tr2, lambda=0)}. This argument is ignored if \code{tipDiff} is supplied; otherwise supplying this will save time if calling \code{plotTreeDiff} repeatedly, for example with different aesthetics. #' @param sizeOfDifferences a logical (default FALSE) specifying whether the size of the tip differences should be used, or just a count of the number of differences (see \code{tipDiff}) #' @param tipMatch a logical (default TRUE) specifying whether the second tree should be rotated so that, as far as possible, each of its tips lies opposite its equivalent in the first tree #' @param treesFacing a logical (default FALSE) specifying whether the trees should be plotted facing each other - that is, with the second tree plotted "leftwards". #' @param baseCol the colour used for tips with identical ancestry in the two trees. Defaults to "grey". #' @param colourMethod the method to use for colouring. Default is "ramp", corresponding to the original implementation, where the function \code{colorRampPalette} is used to create a palette which ranges from \code{col1} to \code{col2}. For large trees this can be hard to interpret, and method \code{palette} may be preferred, which permits the selection of a palette to use in \code{adegenet}'s function \code{num2col}. #' @param col1 the first colour used to define the colour spectrum for tips with differences. This colour will be used for tips with minor differences. Defaults to "peachpuff". Ignored if \code{colourMethod="palette"}. #' @param col2 the second colour used to define the colour spectrum for tips with differences. This colour will be used for tips with major differences. Defaults to "red2". Ignored if \code{colourMethod="palette"}. #' @param palette the colour palette to be used if \code{colourMethod="palette"}. For a list of available palettes see \code{?num2col}. #' @param ... further arguments passed to \code{\link{plot.phylo}} #' #' @return #' A plot of the two trees side by side. Tips are coloured in the following way: #' \itemize{ #' \item if each ancestor of a tip in tree 1 occurs in tree 2 with the same partition of tip descendants, then the tip is coloured grey (or supplied "baseCol") #' \item if not, the tip gets coloured pale orange to red on a scale according to how many differences there are amongst its most recent common ancestors with other tips. The colour spectrum can be changed according to preference. #' } #' #' @seealso \code{\link{medTree}}, \code{\link{tipDiff}} #' #' @examples #' ## simple example on trees with five tips: #' tr1 <- read.tree(text="((A:1,B:1):1,((C:1,D:1):1,E:1):1):1;") #' tr2 <- read.tree(text="((A:1,B:1):1,(C:1,(D:1,E:1):1):1):1;") #' plotTreeDiff(tr1,tr2) #' #' ## example on larger woodmice trees #' data(woodmiceTrees) #' tr1 <- woodmiceTrees[[1]] #' tr2 <- woodmiceTrees[[57]] # for example #' #' # find the tip differences in advance, to avoid recalculating with each plot #' wmTipDiff <- tipDiff(tr1,tr2, sizeOfDifferences=TRUE) #' plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, tipMatch=TRUE) #' #' ## change aesthetics: #' # trees facing each other: #' plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, treesFacing=TRUE) #' #' # radial plots, and change colours: #' plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, #' baseCol="grey2", col1="cyan", col2="navy", #' edge.width=2, type="radial", cex=0.5, font=2) #' # cladogram plots, and use colour palette from adegenet to see differences more clearly: #' plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, #' treesFacing=TRUE, baseCol="black", colourMethod="palette", #' edge.width=2, type="cladogram", cex=0.5, font=2) #' #' # including the size of the differences highlights tip "No0906s" a little more: #' # (this is typically a more informative plot in cases where many tips have the #' # same difference count, for example when a whole clade has been shifted "up" #' # or "down" the tree but its internal topology remains the same.) #' #' plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, sizeOfDifferences=TRUE, #' treesFacing=TRUE, baseCol="black", colourMethod="palette", #' edge.width=2, type="cladogram", cex=0.5, font=2) #' #' @export plotTreeDiff <- function(tr1,tr2,tipDiff=NULL,vec1=NULL,vec2=NULL, sizeOfDifferences=FALSE, tipMatch=TRUE, treesFacing=FALSE, baseCol="grey",col1="peachpuff",col2="red2",colourMethod="ramp",palette=lightseasun, ...) { l <- length(tr1$tip.label) if (tipMatch) { tr2 <- rotateConstr(tr2, tr1$tip.label) } if (is.null(tipDiff)){ # call tipDiff tipDiff <- tipDiff(tr1,tr2,vec1,vec2, sizeOfDifferences) } if (!sizeOfDifferences) { # find the number of times each tip appears, in the order that the tip labels are read tipSignificance1 <- sapply(tr1$tip.label, function(x) tipDiff[which(tipDiff[,1]==x),2]) tipSignificance2 <- sapply(tr2$tip.label, function(x) tipDiff[which(tipDiff[,1]==x),2]) } else { # find the size of the tip differences, in the order that the tip labels are read tipSignificance1 <- sapply(tr1$tip.label, function(x) tipDiff[which(tipDiff[,1]==x),3]) tipSignificance2 <- sapply(tr2$tip.label, function(x) tipDiff[which(tipDiff[,1]==x),3]) } # combine: significances <- c(tipSignificance1,tipSignificance2) if (colourMethod=="ramp") { colfunc <- colorRampPalette(c(col1,col2)) if (min(significances)==0) { # make sure tips with no differences are coloured baseCol tipSignificance1 <- tipSignificance1 + 1 tipSignificance2 <- tipSignificance2 + 1 newSignificances <- c(tipSignificance1,tipSignificance2) if (max(significances)==0) {numCols <- 0} else {numCols <- max(significances) - min(newSignificances) + 1} pal <- c(baseCol,colfunc(numCols)) } else { numCols <- max(significances) - min(significances) + 1 pal <- colfunc(numCols) } tipCols1 <- pal[as.factor(tipSignificance1)] tipCols2 <- pal[as.factor(tipSignificance2)] } else { tipCols1 <- num2col(tipSignificance1, col.pal=palette) tipCols2 <- num2col(tipSignificance2, col.pal=palette) if (min(significances)==0) { # make sure tips with no differences are coloured baseCol tipCols1[which(tipSignificance1==0)] <- baseCol tipCols2[which(tipSignificance2==0)] <- baseCol } } if (treesFacing) dir <- "leftwards" else dir <- "rightwards" # plot layout(matrix(1:2, 1, 2)) plot.phylo(tr1, tip.color=tipCols1, no.margin=TRUE, ...) plot.phylo(tr2, tip.color=tipCols2, no.margin=TRUE, direction=dir, ...) } treespace/R/treespace.R0000644000176200001440000001516013231614767014541 0ustar liggesusers#' #' Phylogenetic tree exploration #' #' Compares phylogenetic trees using a choice of metrics / measures, and maps their pairwise distances into a small number of dimensions for easy visualisation and identification of clusters. #' #' @param x an object of the class multiPhylo #' @param method the method for summarising the tree as a vector. #' Choose from: #' \code{treeVec} (default) the Kendall Colijn metric vector #' \code{BHV} the Billera, Holmes Vogtmann metric using \code{dist.multiPhylo} from package \code{distory} #' \code{KF} the Kuhner Felsenstein metric (branch score distance) using \code{KF.dist} from package \code{phangorn} (Note: this considers the trees as unrooted) #' \code{RF} the Robinson Foulds metric using \code{RF.dist} from package \code{phangorn} (Note: this considers the trees as unrooted and issues a corresponding warning) #' \code{wRF} the weighted Robinson Foulds metric using \code{wRF.dist} from package \code{phangorn} (Note: this considers the trees as unrooted and issues a corresponding warning) #' \code{nNodes} the Steel & Penny tip-tip path difference metric, (topological, ignoring branch lengths), using \code{path.dist} from package \code{phangorn} (Note: this considers the trees as unrooted) #' \code{patristic} the Steel & Penny tip-tip path difference metric, using branch lengths, calling \code{path.dist} from package \code{phangorn} (Note: this considers the trees as unrooted) #' others inherited from \code{distTips} in \code{adephylo}: #' \itemize{ #' \item \code{Abouheif}: performs Abouheif's test. See Pavoine et al. (2008) and \code{adephylo}. #' \item \code{sumDD}: sum of direct descendants of all nodes on the path, related to Abouheif's test. See \code{adephylo}. #' } #' @param nf the number of principal components to retain #' @param lambda a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised in the Kendall Colijn metric. #' @param return.tree.vectors option to also return the tree vectors. Note that this can use a lot of memory so defaults to \code{FALSE}. #' @param processors value (default 1) to be passed to mcmapply specifying the number of cores to use. Must be 1 on Windows (see \code{mcmapply} for more details). #' @param ... further arguments to be passed to \code{method}. #' #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @import ape #' @importFrom ade4 dudi.pco cailliez #' @importFrom adephylo distTips #' @importFrom distory dist.multiPhylo #' @importFrom fields rdist #' @importFrom phangorn KF.dist #' @importFrom phangorn path.dist #' @importFrom phangorn RF.dist #' @importFrom phangorn wRF.dist #' @importFrom parallel mcmapply #' #' @examples #' #' ## generate list of trees #' x <- rmtree(10, 20) #' names(x) <- paste("tree", 1:10, sep = "") #' #' ## use treespace #' res <- treespace(x, nf=3) #' table.paint(as.matrix(res$D)) #' scatter(res$pco) #' #' data(woodmiceTrees) #' woodmiceDists <- treespace(woodmiceTrees,nf=3) #' plot(woodmiceDists$pco$li[,1],woodmiceDists$pco$li[,2]) #' woodmicedf <- woodmiceDists$pco$li #' if(require(ggplot2)){ #' woodmiceplot <- ggplot(woodmicedf, aes(x=A1, y=A2)) # create plot #' woodmiceplot + geom_density2d(colour="gray80") + # contour lines #' geom_point(size=6, shape=1, colour="gray50") + # grey edges #' geom_point(size=6, alpha=0.2, colour="navy") + # transparent blue points #' xlab("") + ylab("") + theme_bw(base_family="") # remove axis labels and grey background #' } #' #' \dontrun{ #' if(require(rgl)){ #' plot3d(woodmicedf[,1], woodmicedf[,2], woodmicedf[,3], type="s", size=1.5, #' col="navy", alpha=0.5, xlab="", ylab="", zlab="") #' } #' } #' #' #' @export treespace <- function(x, method="treeVec", nf=NULL, lambda=0, return.tree.vectors=FALSE, processors=1, ...){ ## CHECKS ## if(!inherits(x, "multiPhylo")) stop("x should be a multiphylo object") num_trees <- length(x) # number of trees ## fix potential bug with input of two trees if(num_trees<3) { stop("treespace expects at least three trees. The function treeDist is suitable for comparing two trees.") } # check for user supplying invalid options (these gave unhelpful error messages before) dots <- list(...) if(!is.null(dots$return.lambda.function)) stop("return.lambda.function is not compatible with treespace. Consider using multiDist instead.") if(!is.null(dots$save.memory)) stop("save.memory is not compatible with treespace. Consider using multiDist instead.") # make name labels well defined if(is.null(names(x))) names(x) <- 1:num_trees else if(length(unique(names(x)))!=num_trees){ warning("duplicates detected in tree labels - using generic names") names(x) <- 1:num_trees } lab <- names(x) # check all trees have same tip labels for (i in 1:num_trees) { if (!setequal(x[[i]]$tip.label,x[[1]]$tip.label)) { stop(paste0("Tree ",lab[[i]]," has different tip labels from the first tree.")) } } ## GET DISTANCES BETWEEN TREES, according to method ## ## get summary vectors then compute pairwise distances ## if (method=="treeVec") { df <- t(mcmapply(treeVec, x, lambda=lambda, MoreArgs=dots, mc.cores=processors)) ## get pairwise Euclidean distances ## D <- as.dist(rdist(df)) } else if(method %in% c("Abouheif","sumDD")){ df <- t(mcmapply(adephylo::distTips, x, method=method, MoreArgs=dots, mc.cores=processors)) ## get pairwise Euclidean distances ## D <- as.dist(rdist(df)) } else if(method=="patristic"){ D <- path.dist(x, use.weight=TRUE) } else if(method=="nNodes"){ D <- path.dist(x, use.weight=FALSE) } else if(method=="RF"){ D <- RF.dist(x) ## make the distance Euclidean D <- ade4::cailliez(D, print=FALSE) } else if(method=="wRF"){ D <- wRF.dist(x) ## make the distance Euclidean D <- ade4::cailliez(D, print=FALSE) } else if(method=="KF"){ D <- KF.dist(x) } else if(method=="BHV"){ D <- dist.multiPhylo(x) ## make the distance Euclidean D <- ade4::cailliez(D, print=FALSE) } ## restore labels attr(D,"Labels") <- lab ## perform PCoA/MDS ## pco <- dudi.pco(D, scannf=is.null(nf), nf=nf) ## BUILD RESULT AND RETURN ## if (return.tree.vectors==TRUE) { out <- list(D=D, pco=pco, vectors=df) } else { out <- list(D=D, pco=pco) } return(out) } # end treespace treespace/R/transmissionTrees.R0000644000176200001440000002252213205026553016311 0ustar liggesusers#' Find MRCIs #' #' Function to find the most recent common infector (MRCI) matrix from "who infected whom" information. #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param wiw a two-column matrix where the first column gives the infectors and the second column gives the infectees; each row corresponds to a transmission event from an infector to an infectee. #' #' @return Returns three objects: #' \itemize{ #' \item \code{sourceCase}: the number of the node which is the source case, i.e. the common infector of all cases (outputs a warning if there is more than one source case). #' \item \code{mrcis}: a matrix where, for each pair of individuals i and j, the entry (i,j) is the node number of their MRCI. Note that if i infected j then this entry is i itself. #' \item \code{mrciDepths}: a matrix where, for each pair of individuals i and j, the entry (i,j) is the depth of their MRCI, defined as the number of edges from the source case. The source case has depth zero, its direct infectees have depth 1, and so on. #' } #' #' @importFrom compiler cmpfun #' @importFrom combinat combn2 #' #' @examples #' #' ## a simple who infected whom matrix: #' tree1 <- cbind(Infector=1:5,Infectee=2:6) #' findMRCIs(tree1) #' #' #' @export findMRCIs <- compiler::cmpfun(function(wiw) { # expect wiw to be a "who infected whom" matrix: column 1 is infectors, column 2 infectees if (class(wiw) != "matrix") stop("The who infected whom information supplied should be of class matrix.") # convert whatever the wiw entries are into integers, but preserve the names for later wiwCopy <- matrix(0,nrow(wiw),2) wiwNames <- unique(c(wiw[,1],wiw[,2])) wiwCopy[,1] <- match(wiw[,1],wiwNames) wiwCopy[,2] <- match(wiw[,2],wiwNames) wiw <- wiwCopy initial <- min(wiw) # the number of the first case # if the first case is "0" we'll get problems - add one to everything if (initial==0) { wiw <- wiw + 1 initial <- min(wiw) } l <- max(wiw) # largest node number (note, not necessarily the "last" case) # make a reference list where entry i gives all the direct descendant(s) of case i DirectDesc <- lapply(initial:l, function(x) { wiw[which(wiw[,1]==x),2] # direct infectee(s) of x, if any }) numDDs <- sapply(DirectDesc, length) AllDesc <- lapply(initial:l, function(x) { # for each node x: DDs <- DirectDesc[[x]] # find direct descendants tmpNumDDs <- length(DDs) # store the number of them allDs <- rep(NA, length(initial:l)) # start a vector of all descendants of x, including x itself allDs[[1]] <- x while(tmpNumDDs!=0){ allDs[which(is.na(allDs))[1:tmpNumDDs]] <- DDs DDs <- do.call(c, DirectDesc[DDs]) # now find direct descendants of these, as a vector... tmpNumDDs <- length(DDs) } return(allDs[!is.na(allDs)]) }) M <- matrix(0, nrow=l, ncol=l) # initialise matrix # traverse nodes from initial case, down the "tree" for (tmp in initial:l){ # tmp is the MRCA of all its descendants with itself # (if tmp has no descendants, this syntax is still ok) for (i in AllDesc[[tmp]]) { M[tmp,i] <- M[i,tmp] <- tmp } # get the direct cases infected by tmp. If there are two or more, then tmp is the MRCA of all pairs of cases descending from different DDs tmpDDs <- DirectDesc[[tmp]] numTmpDDs <- length(tmpDDs) if (numTmpDDs>1) { # then tmp is the MRCI of descendant one and descendant two, and of each pair comprised of a descendant from one and a descendant from two pairs <- combn2(tmpDDs) for (j in 1:length(pairs[,1])) { M[AllDesc[[pairs[j,1]]],AllDesc[[pairs[j,2]]]] <- M[AllDesc[[pairs[j,2]]],AllDesc[[pairs[j,1]]]] <- tmp } } # end if loop }# end for tmp loop diag(M) <- initial:l # we define the diagonal elements of M to be the nodes themselves # note: if there are multiple source cases then there will still be zero entries in the matrix, which haven't been overwritten since the initialisation step # if there is a single source case, it's the one whose entry in AllDescs is initial:l if (min(M)==0) { warning("No single source case; returning source of longest transmission chain") transChainLengths <- sapply(AllDesc, function(x) length(x)) sourceOfLongestChain <- which(transChainLengths==max(transChainLengths)) sourceCase <- sourceOfLongestChain } else { sourceCase <- NA count <- 1 while(is.na(sourceCase)){ isSource <- setequal(AllDesc[[count]],initial:l) if (isSource) {sourceCase <- count} count <- count + 1 } } # finally, we want to know the "depth" of each case (distance from source case) # and we want a version of M where the entries are the depths of the mrcis, rather than the IDs of the mrcis depths <- rep(NA, l) count <- 0 depths[[sourceCase]] <- count SCdescs <- unlist(DirectDesc[[sourceCase]], FALSE, FALSE) while(length(SCdescs)!=0){ count <- count + 1 depths[SCdescs] <- count SCdescs <- unlist(DirectDesc[SCdescs], FALSE, FALSE) } D <- matrix(ncol=l, nrow=l, depths[M]) # create matrix D of depths M <- matrix(ncol=l, nrow=l, wiwNames[M]) # convert M into the node names # re-associate the node names with the columns and rows of the matrices colnames(M) <- rownames(M) <- colnames(D) <- rownames(D) <- wiwNames return(list(sourceCase=wiwNames[[sourceCase]],mrcis=M,mrciDepths=D)) }) #' Transmission tree distance #' #' Function to find the distance between transmission trees by comparing their MRCI depth matrices; to be precise, by finding the Euclidean distance between the tree vectors, restricted to their sampled node entries. #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param matList a list of matrices, each of which is the output of \code{findMRCIs$mrciDepths} #' @param sampled a vector of node IDs which corresponds to those nodes which are sampled cases. Default is to treat all nodes as sampled cases. #' #' @return Returns a distance matrix, where entry (i,j) is the transmission tree distance between matrices i and j in \code{matList} #' #' @importFrom compiler cmpfun #' @importFrom fields rdist #' #' @examples #' # create some simple "who infected whom" scenarios: #' tree1 <- cbind(Infector=1:5,Infectee=2:6) #' tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6) #' tree3 <- cbind(Infector=c(2,2,3,4,5),Infectee=c(1,3,4,5,6)) #' # create list of the MRCI depth matrices: #' matList <- lapply(list(tree1,tree2,tree3), function(x) findMRCIs(x)$mrciDepths) #' #' # transmission tree distance, assuming all cases are sampled: #' wiwTreeDist(matList) #' # transmission tree distance when cases 1, 2 and 4 are sampled: #' wiwTreeDist(matList, sampled=c(1,2,4)) #' #' @export wiwTreeDist <- compiler::cmpfun(function(matList, sampled=NULL) { if (is.null(sampled)) {sampled <- 1:length(matList[[1]][1,])} matVecs <- lapply(matList, function(x) as.vector(x[sampled,sampled])) df <- t(data.frame(matVecs)) ## get pairwise Euclidean distances ## D <- as.dist(rdist(df)) return(D) }) #' Median transmission tree #' #' Function to find the median of a list of transmission scenarios #' #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @param matList a list of matrices, each of which is the output of \code{findMRCIs$mrciDepths} #' @param sampled a vector of node IDs which corresponds to those nodes which are sampled cases #' @param weights optional vector of weights to correspond to the entries of matList #' #' @return Returns three objects: #' \itemize{ #' \item \code{centre}: the mean of the matList entries, restricted to the sampled cases #' \item \code{distances}: for each entry of matList, its distance from \code{centre} #' \item \code{mindist}: the minimum of \code{distances} #' \item \code{median}: the number of the median entry of matList, i.e. the one(s) which achieve the \code{mindist} from the \code{centre}. #' } #' #' @importFrom compiler cmpfun #' @importFrom combinat combn2 #' #' @examples #' # create some simple "who infected whom" scenarios: #' tree1 <- cbind(Infector=1:5,Infectee=2:6) #' tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6) #' tree3 <- cbind(Infector=c(2,2,3,4,5),Infectee=c(1,3,4,5,6)) #' # create list of the MRCI depth matrices: #' matList <- lapply(list(tree1,tree2,tree3), function(x) findMRCIs(x)$mrciDepths) #' #' # median tree, assuming all cases are sampled: #' wiwMedTree(matList) #' # median tree when cases 1, 2 and 4 are sampled: #' wiwMedTree(matList, sampled=c(1,2,4)) #' #' @export wiwMedTree <- compiler::cmpfun(function(matList, sampled=NULL, weights=NULL){ l <- length(matList) if (is.null(sampled)) {sampled <- 1:length(matList[[1]][1,])} if (is.null(weights)) {weights <- rep(1,l)} matVecs <- t(sapply(matList, function(x) as.vector(x[sampled,sampled]))) centre <- weights %*% matVecs/l ## Distances to the centre. distances <- apply(matVecs, 1, function(m){sqrt(sum((m-centre)^2))}) ## Get the indices for the median wiw(s). min_distance <- min(distances) median_wiw <- which(min_distance == distances) return(list(centre=centre, distances=distances, mindist=min_distance, median=median_wiw)) }) treespace/R/RcppExports.R0000644000176200001440000000103113205027036015033 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 updateDistancesWithCombinations <- function(length_root_distances, topological_root_distances, left_partition, right_partition, index_offsets, distance_to_root, edges_to_root) { invisible(.Call('_treespace_updateDistancesWithCombinations', PACKAGE = 'treespace', length_root_distances, topological_root_distances, left_partition, right_partition, index_offsets, distance_to_root, edges_to_root)) } treespace/R/relatedTreeDist.R0000644000176200001440000001240013225505116015632 0ustar liggesusers#' Tree distance when trees have "related" tips #' #' This function calculates the distances between trees whose tips belong to the same categories but are not necessarily identically labelled #' #' @param trees an object of class multiphylo #' @param df a data frame specifying to which category each individual (from all the trees) belongs. Each row gives: an individual (column 2) and its corresponding category (column 1) #' @param checkTrees a logical (default TRUE) specifying whether the trees should be checked. When TRUE, error messages will be helpful in locating problematic trees, that is, any trees with repeated tip labels, or any trees with missing categories. #' #' @import ape #' @importFrom combinat combn #' @importFrom fields rdist #' #' @examples #' # we will simulate some trees as an example, each "based" on the same tree: #' baseTree <- rtree(5) #' baseTree$tip.label <- letters[5:1] #' plot(baseTree) #' #' tree1 <- simulateIndTree(baseTree, itips=3, permuteTips=FALSE) #' tree2 <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) #' tree3 <- simulateIndTree(baseTree, itips=4, permuteTips=TRUE, tipPercent=20) #' tree4 <- simulateIndTree(baseTree, itips=4, permuteTips=TRUE, tipPercent=60) #' tree5 <- simulateIndTree(baseTree, itips=4, permuteTips=TRUE, tipPercent=100) #' # combine: #' trees <- list(tree1,tree2,tree3,tree4,tree5) #' #' df <- cbind(sort(rep(letters[1:5],4)),sort(paste0(letters[1:5],"_",rep(1:4,5)))) #' head(df) #' #' # Find distances: #' relatedTreeDist(trees,df) #' #' # Note that trees 1 and 2 have different numbers of tips but the relationships between those tips #' # are identical at the category level, hence the related tree distance is 0. #' # We can see that the distances between trees increase the more the trees are permuted. #' #' #' @export relatedTreeDist <- function(trees,df,checkTrees=TRUE){ l <- length(trees) # find numbers of leaves and leaf pairs for each tree. # If checkTrees=TRUE, check that each tree has no repeated tip labels. tree_leaves <- lapply(1:l, function(x) { tip_labels <- trees[[x]]$tip.label if ((checkTrees=TRUE)&&(anyDuplicated(tip_labels))) stop(paste0("Each tip in tree ",x," must have a unique label")) else tip_labels }) tree_leaf_nums <- sapply(tree_leaves, function(x) length(x)) # to avoid re-computation, make sure each row of df is unique df <- unique(df) categories <- unique(df[,1]) # category names # create a list of vectors: categories with their individuals. Essentially another, easier to parse, representation of df inds_by_category <- split(df[,2], factor(df[,1], categories)) if (checkTrees==TRUE) { # check each category is represented at least once in each tree: for (c in 1:length(categories)) { inds <- inds_by_category[[c]] for (i in 1:l) { if(length(intersect(inds,tree_leaves[[i]]))==0) stop(paste0("Tree ",i," is missing any individuals from category ",categories[[c]])) } } } # find the tip-tip MRCA depths of all individuals, in each tree allTreesTipMRCADepths <- lapply(trees, function(x) tipsMRCAdepths(x)) # get the pairs of categories i,j, just by number cat_pair_nums <- t(combn(1:length(categories),2, fun=c(), simplify=TRUE)) # for each tree k and each pair of categories i,j, # vk_{i,j} = the average depth of the corresponding leaf pairs in tree_k vectors <- sapply(1:l, function(t) { # vector for each tree tree_vec <- vector() leaf_pairs <- choose(tree_leaf_nums[[t]],2) # number of leaf pairs in this tree for (x in 1:length(cat_pair_nums[,1])) { # running through the pairs of categories: cati_num <- cat_pair_nums[x,1] catj_num <- cat_pair_nums[x,2] # list all the pairs of individuals, one from each category, WHICH OCCUR IN THIS TREE # (otherwise there's a potential for a huge slow-down) ind_pairs <- expand.grid( intersect(inds_by_category[[cati_num]],tree_leaves[[t]]), intersect(inds_by_category[[catj_num]],tree_leaves[[t]])) # we are still fixing x, i.e. the pair of categories, # and now we're going to look through all the pairs of individuals from those categories in this tree count <- 1 depths <- vector() for (y in 1:length(ind_pairs[,1])) { # look for matches to first individual; look through those for matches to second # (note, row.match, intersect were slower) possible_rows <- c(which(allTreesTipMRCADepths[[t]][,1]==ind_pairs[y,1]), which(allTreesTipMRCADepths[[t]][,2]==ind_pairs[y,1])) # rows which include the first individual ijrow <- possible_rows[[c(which(allTreesTipMRCADepths[[t]][possible_rows,1]==ind_pairs[y,2]), which(allTreesTipMRCADepths[[t]][possible_rows,2]==ind_pairs[y,2]))]] # row which includes the first and second individual depths[[count]] <- as.integer(allTreesTipMRCADepths[[t]][ijrow,3]) count <- count + 1 } # for tree t and category pair x, vector[[t]][[x]] is the mean depth of tip pairs in tree t belonging to category pair x tree_vec[[x]] <- mean(depths) } return(tree_vec) }) dist <- rdist(t(data.frame(vectors))) return(as.dist(dist)) } treespace/R/servers.R0000644000176200001440000000434513164413031014244 0ustar liggesusers#' #' Web-based tree explorer #' #' This function opens up an application in a web browser for an interactive exploration of the diversity in a set of trees. #' For further details please see the "help" tab within the application. #' #' @seealso For convenience, \code{treespaceServer} is also available as a separate web app which can be used from any browser (it is not necessary to have \R installed): {\url{http://shiny.imperial-stats-experimental.co.uk/users/mlkendal/treespace/}} #' #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} #' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com} #' #' @import ape #' @import scatterD3 #' @import shiny #' @importFrom ade4 dudi.pco #' @importFrom adegraphics s1d.barchart #' @importFrom adegraphics s.class #' @importFrom adegraphics s.label #' @importFrom adephylo distTips #' @importFrom distory dist.multiPhylo #' @importFrom fields rdist #' @importFrom htmlwidgets saveWidget #' @importFrom MASS Shepard #' @importFrom phangorn Children #' @importFrom phangorn Descendants #' @importFrom RLumShiny jscolorInput #' @importFrom shinyBS bsTooltip #' @importFrom utils packageDescription #' #' #' #' @export treespaceServer <- function(){ ## RUN APP runApp(system.file("shiny",package="treespace")) return(invisible()) } #' #' Auxiliary functions #' #' These functions are not supposed to be used by the user. #' #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} #' #' #' @importFrom adegenet .readExt #' #' @export .render.server.info <- function(){ renderPrint( { cat("\n== R version ==\n") print(R.version) cat("\n== Date ==\n") print(date()) cat("\n== treespace version ==\n") print(packageDescription("treespace", fields=c("Package", "Version", "Date", "Built"))) cat("\n== shiny version ==\n") print(packageDescription("shiny", fields=c("Package", "Version", "Date", "Built"))) cat("\n== attached packages ==\n") print(search()) } ) # end renderPrint } # end .render.server.info treespace/R/simulateIndTree.R0000644000176200001440000000371513225435366015666 0ustar liggesusers#' Simulate randomised "individuals" tree #' #' This function takes in a "category" tree and outputs a simulated corresponding "individuals" tree, for testing the concordance measure. #' #' @param catTree object of class phylo, the category-level tree #' @param itips number of individual tips to assign per category #' @param permuteCat logical specifying whether to permute the category labels on the category tree before grafting on individual tips. Defaults to FALSE. #' @param permuteTips logical specifying whether to permute the individual tip labels after building the individual level tree based on the category tree. Defaults to TRUE. #' @param tipPercent number specifying the percentage of tips to be permuted. Defaults to 100, ignored if permuteTips=FALSE. #' #' @import ape #' @importFrom phytools paste.tree #' #' @examples #' tree <- simulateIndTree(rtree(3)) #' plot(tree) #' #' @seealso \code{\link{treeConcordance}} \code{\link{makeCollapsedTree}} #' #' @export simulateIndTree <- function(catTree,itips=5,permuteCat=FALSE,permuteTips=TRUE,tipPercent=100) { bigTree <- catTree # copy of catTree, to have smaller trees grafted on to it if (permuteCat){ bigTree$tip.label <- sample(bigTree$tip.label) # permute tips on category tree, for more discordance } for (t in catTree$tip.label) { bigTree$tip.label[[which(bigTree$tip.label==t)]] <- "NA" # paste.tree will graft a clade into the receptor tree at the "sticky tip" labeled with "NA" toglue <- rtree(itips) toglue$tip.label <- paste0(t,"_",1:itips) toglue$root.edge <- 0 bigTree <- paste.tree(tr1=bigTree,tr2=toglue) } if (permuteTips) { N <- length(bigTree$tip.label) if ((tipPercent<0)||(tipPercent>100)) stop("tipPercent should be a number between 0 and 100") p <- floor(N*tipPercent/100) samp <- sample(1:N,p) tipsToSwap <- bigTree$tip.label[samp] bigTree$tip.label[samp] <- sample(tipsToSwap) } return(bigTree) } treespace/R/plotGroves.R0000644000176200001440000002153313164413031014715 0ustar liggesusers#' #' Scatterplot of groups of trees #' #' This function displays the scatterplot of the Multidimensional #' Scaling (MDS) output by treespace, superimposing group information #' (derived by \code{\link{findGroves}}) using colors. #' #' This function relies on \code{\link[adegraphics]{s.class}} #' from the \code{adegraphics} package. #' #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} #' #' @importFrom adegraphics s.class #' @importFrom adegraphics s.label #' @importFrom adegraphics s1d.barchart #' @importFrom adegraphics insert #' @importFrom adegenet funky #' @importFrom adegenet bluepal #' @importFrom adegenet transp #' #' @param x a list returned by \code{\link{findGroves}} or a MDS with class \code{dudi} #' @param groups a factor defining groups of trees #' @param xax a number indicating which principal component to be used as 'x' axis #' @param yax a number indicating which principal component to be used as 'y' axis #' @param type a character string indicating which type of graph to use #' @param col.pal a color palette to be used for the groups #' @param bg the background color #' @param lab.show a logical indicating whether labels should be displayed #' @param lab.col a color for the labels #' @param lab.cex the size of the labels #' @param lab.optim a logical indicating whether label positions should be optimized to avoid overlap; better display but time-consuming for large datasets #' @param point.cex the size of the points #' @param scree.pal a color palette for the screeplot #' @param scree.size a size factor for the screeplot, between 0 and 1 #' @param scree.posi either a character string or xy coordinates indicating the position of the screeplot. #' @param ... further arguments passed to \code{\link{s.class}} #' #' @return #' An \code{adegraphics} object (class: \code{ADEgS}) #' #' @seealso #' \code{\link{findGroves}} to find any clusters in the tree landscape #' \code{\link[adegraphics]{s.class}} #' #' #' @examples #' #' \dontrun{ #' if(require("adegenet") && require("adegraphics")){ #' ## load data #' data(woodmiceTrees) #' #' ## run findGroves: treespace+clustering #' res <- findGroves(woodmiceTrees, nf=5, nclust=6) #' #' ## basic plot #' plotGroves(res) #' #' ## adding labels #' plotGroves(res, lab.show=TRUE) #' #' ## customizing #' plotGroves(res, lab.show=TRUE, #' bg="black", lab.col="white", scree.size=.35) #' #' ## customizing #' plotGroves(res, type="ellipse", lab.show=TRUE, #' lab.optim=FALSE, scree.size=.35) #' #' ## example with no group information #' plotGroves(res$treespace$pco) #' #' ## adding labels #' plotGroves(res$treespace$pco, lab.show=TRUE, lab.cex=2) #' #' } #' } #' #' @export plotGroves <- function(x, groups=NULL, xax=1, yax=2, type=c("chull","ellipse"), col.pal=funky, bg="white", lab.show=FALSE, lab.col="black", lab.cex=1, lab.optim=TRUE, point.cex=1, scree.pal=NULL, scree.size=.2, scree.posi=c(.02,.02), ...){ ## HANDLE ARGUMENTS ## ## checks type <- match.arg(type) if(is.null(scree.pal)) scree.pal <- function(n) rev(bluepal(n)) ## x is a list returned by findGroves if(is.list(x) && !is.data.frame(x) && !inherits(x,"dudi")){ if(is.null(x$treespace)) stop("if x is a list, it should contain a slot $treespace") groups <- x$groups x <- x$treespace$pco } ## x is a dudi object if(inherits(x,"dudi")){ eig <- x$eig x <- x$li } ## groups missing - just s.label if(is.null(groups)) { ## with labels if(lab.show){ out <- s.label(x, xax=xax, yax=yax, plabels=list(optim=lab.optim, col=lab.col, cex=lab.cex), ppoints=list(cex=point.cex), pbackground.col=bg, pgrid.text.col=lab.col, plot=FALSE, ...) } else { ## just points out <- s.label(x, xax=xax, yax=yax, plabels=list(optim=FALSE,cex=0), ppoints=list(cex=point.cex, col=lab.col), pbackground.col=bg, pgrid.text.col=lab.col, plot=FALSE, ...) } } else { ## if groups are provided if(!is.factor(groups)) groups <- factor(groups) n.lev <- length(levels(groups)) ## MAKE GRAPH ## ## base scatterplot if(type=="chull"){ out <- s.class(x, xax=xax, yax=yax, fac=groups, col=col.pal(n.lev), ellipseSize=0, chullSize=1, pbackground.col=bg, ppoints.cex=point.cex, pgrid.text.col=lab.col, plot=FALSE, ...) } if(type=="ellipse"){ out <- s.class(x, xax=xax, yax=yax, fac=groups, col=col.pal(n.lev), ellipseSize=1, pbackground.col=bg, ppoints.cex=point.cex, pgrid.text.col=lab.col, plot=FALSE, ...) } ## add labels if(lab.show){ out <- out + s.label(x, plabel.optim=lab.optim, plabel.col=lab.col, ppoints.cex=0, plabels.cex=lab.cex) } } ## add inset if(!is.null(scree.posi[1]) && !is.na(scree.posi[1]) && scree.posi[1]!="none"){ screeplot <- s1d.barchart(c(0,eig), p1d.horizontal=FALSE, ppolygons.col=scree.pal(length(eig)+1), pbackground=list(col=transp("white"), box=TRUE), layout.width=list(left.padding=2), pgrid.draw=FALSE, plot=FALSE) out <- insert(screeplot, out, posi=scree.posi, ratio=scree.size, plot=FALSE) } ## RETURN ## return(out) } # end plotGroves ###################### ### ScatterD3 version ###################### #' #' Scatterplot of groups of trees using \code{scatterD3} #' #' This function displays the scatterplot of the Multidimensional #' Scaling (MDS) output by treespace, superimposing group information #' (derived by \code{\link{findGroves}}) using colors. #' \code{scatterD3} enables interactive plotting based on d3.js, including zooming, panning and fading effects in the legend. #' #' @author Thibaut Jombart \email{thibautjombart@@gmail.com} #' #' @import scatterD3 #' @importFrom adegenet funky #' @importFrom adegenet bluepal #' @importFrom adegenet transp #' #' @param x a list returned by \code{\link{findGroves}} or a MDS with class \code{dudi} #' @param groups a factor defining groups of trees. If x is a list returned by \code{\link{findGroves}} these will be detected automatically. #' @param xax a number indicating which principal component to be used as 'x' axis #' @param yax a number indicating which principal component to be used as 'y' axis #' @param treeNames if a list of tree names or labels are given, these will be plotted alongside the points. Their size can be altered using \code{labels_size} - see \code{?scatterD3} for more information. #' @param xlab the label for the 'x' axis. Defaults to use the value of 'xax' #' @param ylab the label for the 'y' axis. Defaults to use the value of 'yax' #' @param symbol_var a factor by which to vary the symbols in the plot #' @param ... further arguments passed to \code{\link{scatterD3}} #' #' @return #' A \code{scatterD3} plot #' #' #' @seealso #' \code{\link{findGroves}} to find any clusters in the tree landscape #' #' #' @examples #' #' \dontrun{ #' if(require("adegenet") && require("scatterD3")){ #' ## load data #' data(woodmiceTrees) #' #' ## run findGroves: treespace+clustering #' res <- findGroves(woodmiceTrees, nf=5, nclust=6) #' #' ## basic plot #' plotGrovesD3(res) #' #' ## adding tree labels #' plotGrovesD3(res, treeNames=1:201) #' #' ## customizing: vary the colour and the symbol by group #' plotGrovesD3(res, symbol_var=res$groups) #' #' ## example with no group information #' plotGrovesD3(res$treespace$pco) #' } #' } #' #' @export plotGrovesD3 <- function(x, groups=NULL, xax=1, yax=2, treeNames=NULL, symbol_var=NULL, xlab=paste0("Axis ",xax), ylab=paste0("Axis ",yax), ...){ ## HANDLE ARGUMENTS ## ## checks ## x is a list returned by findGroves if(is.list(x) && !is.data.frame(x) && !inherits(x,"dudi")){ if(is.null(x$treespace)) stop("if x is a list, it should contain a slot $treespace") groups <- x$groups x <- x$treespace$pco$li } ## x is a dudi object if(inherits(x,"dudi")){ eig <- x$eig x <- x$li } scatterD3(x[,xax],x[,yax], lab=treeNames, col_var=groups, symbol_var=symbol_var, xlab=xlab, ylab=ylab, ...) } # end plotGrovesD3 treespace/vignettes/0000755000176200001440000000000013231615741014240 5ustar liggesuserstreespace/vignettes/tipCategories.Rmd0000644000176200001440000003635513231611237017516 0ustar liggesusers--- title: "Comparing trees by tip label categories" author: "Michelle Kendall" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{treespace worked example: comparing by tip categories} \usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be 7x4 inches knitr::opts_chunk$set(fig.width=7, fig.height=4, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) ``` We introduce distance measures between trees with 'related' tip labels in a recent bioRxiv preprint, [*Comparing phylogenetic trees according to tip label categories*](https://doi.org/10.1101/251710). Here we provide an overview of the measures and present some simple examples. ```{r setupVisible, message=FALSE} # load treespace and packages for plotting: library(treespace) library(RColorBrewer) library(ggplot2) library(reshape2) # set colour scheme pal <- brewer.pal(3,"Dark2") ``` Suppose you have a tree with taxa labels which correspond to some broad "categories" and you want to compare it to other tree(s) whose taxa also correspond to those categories. If the tip labels of the trees do not match exactly then the other metrics supplied in `treespace` cannot be applied. The function `relatedTreeDist` is a version of `treeDist` (the Kendall Colijn metric) which compares tip labels at their category levels, and `treeConcordance` is a measure of how "concordant" a tree is to a reference tree linking the categories, as we shall explain below. Some examples of instances where we may wish to compare trees with such labels: ```{r create_examples_table, echo=FALSE} cats <- c("Bacterial sub-types e.g. serogroups", "Species", "Host", "Protein families", "Population groups", "Disjoint features or phenotypes", "Broad taxonomy") indivs <- c("Bacterial isolates", "Orthologous genes", "Deep sequencing reads of pathogen", "Proteins", "Individual organisms", "Individual organisms", "Individual organisms") exTable <- cbind(cats,indivs) colnames(exTable) <- c("Categories", "Individuals") ``` ```{r table1, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'} require(pander) panderOptions('table.split.table', Inf) pander(exTable, style = 'rmarkdown') ``` ## Related tree distance We begin by demonstrating the measure between trees with related tip sets, `relatedTreeDist`. As an example, suppose we have trees created from deep-sequencing reads from patients A, B and C as follows: ```{r create_trees_for_collapsing_example, echo=FALSE} tr1 <- read.tree(text="(((c4,c3),(c2,c1)),((b3,(b2,b1)),((a3,a2),a1)));") tr1$tip.label <- c("Patient C read 4","Patient C read 3", "Patient C read 2","Patient C read 1", "Patient B read 3","Patient B read 2", "Patient B read 1", "Patient A read 3","Patient A read 2", "Patient A read 1") tr1Collapsed <- read.tree(text="(C,(B,A));") tr1Collapsed$tip.label <- c("Patient C","Patient B","Patient A") tr2 <- read.tree(text="((((c4,c3),(c2,c1)),b2),(b1,((a3,a2),a1)));") tr2$tip.label <- c("Patient C read 4","Patient C read 3", "Patient C read 2","Patient C read 1", "Patient B read 2","Patient B read 1", "Patient A read 3","Patient A read 2", "Patient A read 1") tr2Collapsed <- read.tree(text="((C,B),(B,A));") tr2Collapsed$tip.label <- c("Patient C","Patient B","Patient B","Patient A") ``` ```{r plot_patient_trees, echo=FALSE} layout(matrix(1:2,1,2)) plot(tr1, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],3),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, cex=0.8, edge.color=c("black",rep(pal[[1]],6),"black",rep(pal[[2]],5),rep(pal[[3]],5))) plot(tr2, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],2),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, cex=0.8, edge.color=c(rep("black",2),rep(pal[[1]],6),pal[[2]],"black",pal[[2]],"black", rep(pal[[3]],4))) ``` We can think of "collapsing" these trees down to one tip per monophyletic clade of tips from the same category, and renaming the new tips with just the category labels. For example, the trees above would collapse into these, respectively: ```{r plot_collapsed_trees, echo=FALSE} layout(matrix(1:2,1,2)) plot(tr1Collapsed, tip.color=c(pal[[1]],pal[[2]],pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset=0.1, font=4, cex=0.8, edge.color=c(pal[[1]],"black",pal[[2]],pal[[3]])) plot(tr2Collapsed, tip.color=c(pal[[1]],rep(pal[[2]],2),pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset=0.1, font=4, cex=0.8, edge.color=c("black",pal[[1]],pal[[2]],"black",pal[[2]],pal[[3]])) ``` We calculate the distance between the original trees using `relatedTreeDist` as follows. The function requires a data frame telling it which individuals belong to which categories: ```{r relatedTreeDist} df <- cbind(c(rep("Patient A",3),rep("Patient B",3),rep("Patient C",4)), sort(tr1$tip.label)) df relatedTreeDist(list(tr1,tr2),df)[[1]] ``` The function `relatedTreeDist` can take a list of many trees as input, and it produces a distance matrix giving the distances between each pair of trees. Since we only supplied two trees, we just returned the first (only) element of this matrix. We now explain how this value is calculated. We find the *mean* height of the most recent common ancestor (MRCA) of each pair of categories in the **collapsed** trees. That is, we effectively find all the pairwise depths of MRCAs: ```{r tipsMRCAdepths} tipsMRCAdepths(tr1Collapsed) tipsMRCAdepths(tr2Collapsed) ``` then reduce these down to an average depth per pair of (different category) tips, per tree: ```{r calculation_of_relatedTreeDist, echo=FALSE} MRCAdepths <- cbind(tipsMRCAdepths(tr1Collapsed),c(0.5,0,0.5)) colnames(MRCAdepths) <- c("tip1","tip2","Tree 1", "Tree 2") MRCAdepths ``` and then we take the Euclidean distances between the final two columns: $$ d(T_1,T_2) = \sqrt{ (1-0.5)^2 + (0-0)^2 + (0-0.5)^2 } = 0.7071068 $$ We now give a larger example with more trees. Along the way we use the function `simulateIndTree` which takes a "category-level" tree and randomly adds individuals to make an example individuals tree. ```{r six_comparable_trees, fig.height=12} set.seed(948) # set colour scheme pal2 <- brewer.pal(8,"Dark2") # create a "base" (category-level) tree baseTree <- rtree(8) baseTree$tip.label <- letters[8:1] tree1 <- simulateIndTree(baseTree, itips=3, permuteTips=FALSE) tree2 <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) tree2$tip.label <- c(paste0("h_",2:5),paste0("g_",3:6),paste0("f_",c(2,3,7,9)), paste0("e_",c(1,2,5,6)),paste0("d_",3:6),paste0("c_",5:8), paste0("b_",c(3,5,6,9)),paste0("a_",c(1,4,8,9))) tree3 <- simulateIndTree(baseTree, itips=4, tipPercent = 20) tree3NotForPlotting <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) # just for setting colours later tree4 <- simulateIndTree(baseTree, itips=6) tree4NotForPlotting <- simulateIndTree(baseTree, itips=6, permuteTips=FALSE) # create another base tree baseTree2 <- rtree(8, tip.label=letters[8:1]) tree5 <- simulateIndTree(baseTree2, itips=6, permuteTips=FALSE) tree6 <- simulateIndTree(baseTree2, itips=6, tipPercent=30) # set up colour palettes tipcolors3 <- c(rep(pal2[[1]],3),rep(pal2[[2]],3),rep(pal2[[3]],3),rep(pal2[[4]],3),rep(pal2[[5]],3),rep(pal2[[6]],3),rep(pal2[[7]],3),rep(pal2[[8]],3)) tipcolors4 <- c(rep(pal2[[1]],4),rep(pal2[[2]],4),rep(pal2[[3]],4),rep(pal2[[4]],4),rep(pal2[[5]],4),rep(pal2[[6]],4),rep(pal2[[7]],4),rep(pal2[[8]],4)) # colours for 4 tips tipcolors6 <- c(rep(pal2[[1]],6),rep(pal2[[2]],6),rep(pal2[[3]],6),rep(pal2[[4]],6),rep(pal2[[5]],6),rep(pal2[[6]],6),rep(pal2[[7]],6),rep(pal2[[8]],6)) # colours for 6 tips # prepare tip colours for plotting tree3TipOrder <- sapply(tree3$tip.label, function(x) which(tree3NotForPlotting$tip.label==x)) tree4TipOrder <- sapply(tree4$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) tree5TipOrder <- sapply(tree5$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) tree6TipOrder <- sapply(tree6$tip.label, function(x) which(tree4NotForPlotting$tip.label==x)) layout(matrix(c(1,4,2,5,3,6), 2,3)) plot(tree1, tip.color=tipcolors3, no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=2) plot(tree2, tip.color=tipcolors4, no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=2) plot(tree3, tip.color=tipcolors4[tree3TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.8) plot(tree4, tip.color=tipcolors6[tree4TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) plot(tree5, tip.color=tipcolors6[tree5TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) plot(tree6, tip.color=tipcolors6[tree6TipOrder], no.margin=TRUE, edge.width = 4, use.edge.length = FALSE, label.offset= 0.5, font=4, cex=1.2) ``` We have created a variety of trees. Trees 1 and 2 have differing numbers of tips, and differing tip labels, but their collapsed forms would be identical. Tree 3 is similar to Trees 1 and 2 but with a few tips permuted, so that the categories are no longer monophyletic. Tree 4 is similar but with more tips permuted. Trees 5 and 6 were created from a different "base" tree so the relationships between the categories are quite different when we compare, for example, Tree 1 and Tree 5. The function `relatedTreeDist` gives a quantitative description of these similarities and differences. First we just put the trees into a list and create a data frame linking individuals to categories: ```{r relatedTreeDist_six_trees} trees <- list(tree1,tree2,tree3,tree4,tree5,tree6) df <- cbind(sort(rep(letters[1:8],9)),sort(paste0(letters[1:8],"_",rep(1:9,8)))) dists <- relatedTreeDist(trees,df) dists ``` We can visualise these distances with a heatmap: ```{r six_heatmap} dists <- as.matrix(dists) colnames(dists) <- rownames(dists) <- c("Tree 1", "Tree 2", "Tree 3", "Tree 4", "Tree 5", "Tree 6") melted_dists <- melt(dists, na.rm=TRUE) ggheatmap <- ggplot(data = melted_dists, aes(Var2, Var1, fill = value))+ geom_tile(color = "darkgrey")+ scale_fill_gradient2(low = "white", high = "firebrick2", name="Tree distance") + theme_minimal() + coord_fixed() ggheatmap + geom_text(aes(Var2, Var1, label = signif(value,2)), color = "black", size = 8) + theme( axis.title.x = element_blank(), axis.title.y = element_blank(), panel.grid.major = element_blank(), panel.border = element_blank(), panel.background = element_blank(), axis.text = element_text(size=12), axis.ticks = element_blank(), legend.position = "none" ) ``` ## Concordance The concordance measure takes a reference tree R whose tips are a set of categories (with no repeats), and a comparable tree T whose tips are individuals from those categories. The measure counts the proportion of tip pairs whose MRCA in T appears at the same place as the MRCA of their categories in R. It takes a value in (0,1]. Full concordance, where the collapsed version of T is identical to R, gives a value of 1. For example, ```{r concordance_basic} catTree <- read.tree(text="(C,(B,A));") indTree1 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,b2),((a3,a2),a1)));") indTree2 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,a2),((a3,b2),a1)));") indTree3 <- read.tree(text="((a3,(a2,a1)),((b1,c2),((c3,b2),(c1,c4))));") plot(catTree, tip.color=pal, edge.width = 4, type="cladogram", label.offset= 0.5, font=4, edge.color=c(pal[[1]],"black",pal[[2]],pal[[3]])) layout(matrix(1:3,1,3)) plot(indTree1, tip.color=c(rep(pal[[1]],4),rep(pal[[2]],2),rep(pal[[3]],3)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[1]],6),"black",rep(pal[[2]],3),rep(pal[[3]],5))) plot(indTree2, tip.color=c(rep(pal[[1]],4),pal[[2]],rep(pal[[3]],2),pal[[2]],pal[[3]]), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[1]],6),rep("black",2),pal[[2]],pal[[3]], rep("black",2),pal[[3]],pal[[2]],pal[[3]])) plot(indTree3, tip.color=c(rep(pal[[3]],3),pal[[2]],rep(pal[[1]],2),pal[[2]],rep(pal[[1]],2)), edge.width = 4, type="cladogram", no.margin=TRUE, label.offset= 0.5, font=4, cex=2, edge.color=c("black",rep(pal[[3]],4),rep("black",2),pal[[2]],pal[[1]], rep("black",2),pal[[1]],pal[[2]],rep(pal[[1]],3))) ``` The first tree has monophyly per category, and the relative positions of those categories are identical to the reference tree. Correspondingly, the concordance is 1: ```{r concordance_T1} df <- cbind(c(rep("A",3),rep("B",2),rep("C",4)),sort(indTree1$tip.label)) treeConcordance(catTree,indTree1,df) ``` The second tree is fairly similar to the reference, with category C monophyletic and basal to the rest. However, the paraphyly of categories A and B gives a concordance less than 1: ```{r concordance_T2} treeConcordance(catTree,indTree2,df) ``` Finally, the third tree has much less in common with the reference and accordingly has lower concordance: ```{r concordance_T3} treeConcordance(catTree,indTree3,df) ``` To give a broader feel for how the concordance measure behaves, we now perform an experiment where we create random trees and compare them to a reference. We permute the tips in the individuals trees by a given percent, and watch the concordance decrease as the permutations increase and the category-level relationships weaken. In the paper we report our findings for `n=10` and `reps=100`, that is, the reference tree has 10 tips, individuals trees have `n*n=100` tips, and we performed 100 repetitions for each level of permutation. To keep the calculations quick we only use `n=5` and `reps=10` here. ```{r concordance_permuations} n <- 5 reps <- 10 reftree <- rtree(n, tip.label=letters[1:n]) indTrees <- lapply(rep(seq(0,100,20),reps), function(x) simulateIndTree(reftree,itips=n,permuteTips=TRUE,tipPercent=x)) df <- cbind(sort(rep(letters[1:n],n)),sort(indTrees[[1]]$tip.label)) concordances <- sapply(indTrees, function(x) treeConcordance(reftree,x,df)) resultsTab <- as.data.frame(cbind(rep(seq(0,100,20),reps),concordances)) colnames(resultsTab) <- c("Percentage","Concordance") resultsTab[,1] <- factor(resultsTab[,1], levels=seq(0,100,20)) plot <- ggplot(resultsTab, aes(x=Percentage, y=Concordance)) plot + geom_boxplot(aes(colour=Percentage)) + theme_bw() + guides(colour=FALSE) + xlab("Percentage of tips permuted") + ylim(c(0,1)) + theme(axis.text = element_text(size=18), axis.title = element_text(size=18)) ```treespace/vignettes/TransmissionTreesVignette.Rmd0000644000176200001440000002012613214177776022123 0ustar liggesusers--- title: "treespace worked example: Transmission trees" author: "Michelle Kendall" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{treespace worked example: Transmission trees} \usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE) options(digits = 4) ``` This vignette demonstrates the use of *treespace* to compare a collection of transmission trees, as proposed in Kendall, Ayabina & Colijn, 2016 [arXiv:1609.09051](http://arxiv.org/abs/1609.09051). First we load the package *treespace*: ```{r load, message=FALSE} library(treespace) ``` *treespace* contains three functions for handling and comparing transmission trees: 1) `findMRCIs()` which takes a "who infected whom matrix" (the information about infectors and infectees; more on this below) and outputs: + `sourceCase`: the number of the node which is the source case, i.e. the common infector of all cases (outputs a warning if there is more than one source case). + `mrcis`: a matrix where, for each pair of individuals i and j, the entry (i,j) is the node number of their MRCI. Note that if i infected j then this entry is i itself. + `mrciDepths`: a matrix where, for each pair of individuals i and j, the entry (i,j) is the *depth* of their MRCI, defined as the number of edges from the source case. The source case has depth zero, its direct infectees have depth 1, and so on. 2) `wiwTreeDist()` which takes a list of `mrciDepths` matrices and computes the distances between them. You have to supply the list of sampled cases in which you are interested, and then it takes the Euclidean distance between each pair of matrices restricted to the sampled cases (and written long-hand, as a vector) 3) `wiwMedTree()` which takes a list of `mrciDepths` matrices, the list of sampled cases, an optional list of weights, and outputs the median transmission tree Examples --------- We define a "who infected whom matrix" as a matrix of two columns, where the first represents the infectors and the second represents their infectees. For example, a simple transmission chain could be represented like this: ```{r tree1} tree1 <- cbind(Infector=1:5,Infectee=2:6) tree1 ``` This can be easily visualised as a transmission chain using graph plotting packages such as *igraph* or *visNetwork*: ```{r igraph_tree1, message=FALSE} library(igraph) # set plotting options: igraph_options(vertex.size=15, vertex.color="cyan", vertex.label.cex=2, edge.color="lightgrey", edge.arrow.size=1) tree1graph <- graph_from_edgelist(tree1) plot(tree1graph) ``` Applying the function `findMRCIs` gives the following: ```{r simple_wiwMRCIs} findMRCIs(tree1) ``` ### Comparing three simple trees Suppose we had other hypotheses for the transmission tree which describes who infected whom amongst these six cases: ```{r trees2_and_3} # a second scenario: tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6) tree2 tree2graph <- graph_from_edgelist(tree2) plot(tree2graph) # and a third scenario: tree3 <- cbind(Infector=c(2,2,2,2,6),Infectee=c(1,3,4,6,5)) tree3 tree3graph <- graph_from_edgelist(tree3) plot(tree3graph) ``` Then we can use *treespace* functions to make the following comparisons: ```{r tree123_comparison} m1 <- findMRCIs(tree1) # find the source case, MRCIs and MRCI depths for tree 1 m2 <- findMRCIs(tree2) m3 <- findMRCIs(tree3) matList <- list(m1$mrciDepths,m2$mrciDepths,m3$mrciDepths) # create a list of the mrciDepths matrices matList wiwTreeDist(matList, sampled=1:6) # find the Euclidean distances between these matrices, where all six cases are sampled ``` If we had only sampled cases 4, 5 and 6, so that "1", "2" and "3" could be regarded as arbitrary names of inferred, unsampled cases, we would compute: ```{r tree123_sampled4:6} wiwTreeDist(matList, sampled=4:6) ``` which substantially changes the measures of similarities and differences between the trees. ### Comparing many trees using an MDS plot Finally, we demonstrate comparing a larger set of transmission trees and finding the median: ```{r trees1000} set.seed(123) num <- 500 # create a list of 500 random transmission trees with 11 cases, where the source case is fixed as case 1: treelistSC1 <- lapply(1:num, function(x) { edges <- rtree(6)$edge # effectively creating a random transmission scenario relabel <- sample(1:11) # create a relabelling so that infections don't all happen in numerical order, but we force the source case to be 1: relabel[[which(relabel==1)]] <- relabel[[7]] relabel[[7]] <- 1 relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]]) relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]]) cbind(relabelledEdges1,relabelledEdges2) }) # create 500 more random transmission trees, but where the source case is fixed as case 2: treelistSC2 <- lapply(1:num, function(x) { edges <- rtree(6)$edge relabel <- sample(1:11) relabel[[which(relabel==2)]] <- relabel[[7]] relabel[[7]] <- 2 relabelledEdges1 <- sapply(edges[,1], function(x) relabel[[x]]) relabelledEdges2 <- sapply(edges[,2], function(x) relabel[[x]]) cbind(relabelledEdges1,relabelledEdges2) }) # combine: combinedLists <- c(treelistSC1,treelistSC2) # get mrciDepths matrices: matList1000 <- lapply(combinedLists, function(x) findMRCIs(x)$mrciDepths ) # find pairwise tree distances, treating all cases as sampled: WiwDists1000 <- wiwTreeDist(matList1000, sampled=1:11) ``` Now that we have a pairwise distance matrix we can use multidimensional scaling (MDS) to view the relative distances between the trees in a 2D projection. We will colour the points in the projection by the "depth" of the corresponding tree, and use symbols to indicate the source case. For "depth" here we simply use the mean of each "mrciDepths" matrix. ```{r wiw_MDS1000, message=FALSE} wiwMDS <- dudi.pco(WiwDists1000, scannf=FALSE, nf=3) library(ggplot2) library(RColorBrewer) wiwPlot <- ggplot(wiwMDS$li, aes(x=wiwMDS$li[,1],y=wiwMDS$li[,2])) # prepare aesthetics depths <- sapply(matList1000, function(x) mean(x)) sourcecase <- c(rep("1",num),rep("2",num)) # prepare colours: colfunc <- colorRampPalette(brewer.pal(10,"Spectral"), space="Lab") wiwPlot + geom_point(size=4, colour="gray60", aes(shape=sourcecase)) + geom_point(size=3, aes(colour=depths, shape=sourcecase)) + scale_colour_gradientn("Mean of v\n", colours=colfunc(7), guide = guide_colourbar(barheight=10)) + scale_shape_discrete("Source case\n", solid=T, guide = guide_legend(keyheight = 3, keywidth=1.5)) + theme_bw(base_size = 12, base_family = "") + theme_bw(base_size = 12, base_family = "") + theme( legend.title = element_text(size=20), legend.text = element_text(size=20), axis.text.x = element_text(size=20), axis.text.y = element_text(size=20)) + xlab("") + ylab("") ``` The symmetry in the plot corresponds to the different source cases, and the trees are also clearly separated by depth. ### Median trees If our transmission trees corresponded to real data it could be meaningful to find a single representative tree. To find the geometric median tree(s) from a collection, we use the function `wiwMedTree`: ```{r wiwMedian} med <- wiwMedTree(matList1000) ``` This returns a list with components: ```{r wiwMedian2} names(med) ``` * `centre` is the mean vector (which may not necessarily correspond to a valid transmission tree with a single source case) * `distances` gives the distance of each tree from the centre, as a vector * `mindist` gives the minimum of these distances * `median` gives the number(s) of the median tree(s) Here the median trees are: ```{r wiwMedTree} med$median ``` The first one looks like this: ```{r wiwMedTreePlot} medgraph <- graph_from_edgelist(combinedLists[[med$median[[1]]]]) plot(medgraph) ```treespace/vignettes/DengueVignette.Rmd0000644000176200001440000003301013231610212017602 0ustar liggesusers--- title: "treespace worked example: Dengue trees" author: "Michelle Kendall, Thibaut Jombart" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{treespace worked example: Dengue trees} \usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE} # set global chunk options: images will be 7x5 inches knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.path="figs/", cache=FALSE, dpi=96) options(digits = 4) library("rgl") knitr::knit_hooks$set(webgl=hook_webgl) ``` This vignette demonstrates the use of *treespace* to compare a collection of trees. For this example we use trees inferred from 17 dengue virus serotype 4 sequences from Lanciotti et al. (1997). We include a sample of trees from BEAST (v1.8), as well as creating neighbour-joining (NJ) and maximum-likelihood (ML) trees. Loading *treespace* and data: ------------- Load the required packages: ```{r load, message=FALSE, warning=FALSE} library("treespace") library("phangorn") library("adegenet") ``` Load BEAST trees: ```{r load_BEAST_trees} data(DengueTrees) ``` We load a random sample of 500 of the trees (from the second half of the posterior) produced using BEAST v1.8 with xml file 4 from Drummond and Rambaut (2007). It uses the standard GTR + Gamma + I substitution model with uncorrelated lognormal-distributed relaxed molecular clock. Each tree has 17 tips. For convenience in our initial analysis we will take a random sample of 200 of these trees; sample sizes can be increased later. ```{r sample_BEAST_trees} set.seed(123) BEASTtrees <- DengueTrees[sample(1:length(DengueTrees),200)] ``` Load nucleotide sequences: ```{r load_seqs} data(DengueSeqs) ``` Creating neighbour-joining and maximum likelihood trees: ------------- Create a neighbour-joining (NJ) tree using the Tamura and Nei (1993) model (see `?dist.dna` for more information) and root it on the outgroup `"D4Thai63"`: ```{r make_NJ} makeTree <- function(x){ tree <- nj(dist.dna(x, model = "TN93")) tree <- root(tree, resolve.root=TRUE, outgroup="D4Thai63") tree } DnjRooted <- makeTree(DengueSeqs) # Note, there is a (small) negative branch length. # We set this to 0 to avoid warnings from the phangorn package later: DnjRooted$edge.length[which(DnjRooted$edge.length < 0)] <- 0 plot(DnjRooted) ``` We use `boot.phylo` to bootstrap the tree: ```{r make_NJ_boots, results="hide"} Dnjboots <- boot.phylo(DnjRooted, DengueSeqs, B=100, makeTree, trees=TRUE, rooted=TRUE) Dnjboots ``` and we can plot the tree again, annotating it with the bootstrap clade support values: ```{r see_NJ_boots} plot(DnjRooted) drawSupportOnEdges(Dnjboots$BP) ``` We create a maximum-likelihood (ML) tree and root it as before: ```{r make_ML, results="hide", message=FALSE} Dfit.ini <- pml(DnjRooted, as.phyDat(DengueSeqs), k=4) Dfit <- optim.pml(Dfit.ini, optNni=TRUE, optBf=TRUE, optQ=TRUE, optGamma=TRUE, model="GTR") # root: DfitTreeRooted <- root(Dfit$tree, resolve.root=TRUE, outgroup="D4Thai63") ``` View the ML tree: ```{r view_ML} plot(DfitTreeRooted) ``` Create bootstrap trees: ```{r make_ML_boots, results="hide"} # bootstrap supports: DMLboots <- bootstrap.pml(Dfit, optNni=TRUE) # root: DMLbootsrooted <- lapply(DMLboots, function(x) root(x, resolve.root=TRUE, outgroup="D4Thai63")) class(DMLbootsrooted) <- "multiPhylo" ``` Plot the ML tree again, with bootstrap support values: ```{r see_ML_boots} plotBS(DfitTreeRooted, DMLboots, type="phylogram") ``` Using *treespace* to compare trees ------------- We now use the function `treespace` to find and plot distances between all these trees: ```{r run_treespace} # collect the trees into a single object of class multiPhylo: DengueTrees <- c(BEASTtrees, Dnjboots$trees, DMLbootsrooted, DnjRooted, DfitTreeRooted) class(DengueTrees) <- "multiPhylo" # add tree names: names(DengueTrees)[1:200] <- paste0("BEAST",1:200) names(DengueTrees)[201:300] <- paste0("NJ_boots",1:100) names(DengueTrees)[301:400] <- paste0("ML_boots",1:100) names(DengueTrees)[[401]] <- "NJ" names(DengueTrees)[[402]] <- "ML" # create vector corresponding to tree inference method: Dtype <- c(rep("BEAST",200),rep("NJboots",100),rep("MLboots",100),"NJ","ML") # use treespace to find and project the distances: Dscape <- treespace(DengueTrees, nf=5) ``` ```{r simple_plot} # simple plot: plotGrovesD3(Dscape$pco, groups=Dtype) ``` The function `plotGrovesD3` produces interactive d3 plots which enable zooming, moving, tooltip text and legend hovering. We now refine the plot with colour-blind friendly colours (selected using [ColorBrewer2](http://colorbrewer2.org/)), bigger points, varying symbols and point opacity to demonstrate the NJ and ML trees, informative legend title and smaller legend width: ```{r make_better_plot} Dcols <- c("#1b9e77","#d95f02","#7570b3") Dmethod <- c(rep("BEAST",200),rep("NJ",100),rep("ML",100),"NJ","ML") Dbootstraps <- c(rep("replicates",400),"NJ","ML") Dhighlight <- c(rep(1,400),2,2) plotGrovesD3(Dscape$pco, groups=Dmethod, colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ``` We can also add tree labels to the plot. Where these overlap, the user can use "drag and drop" to move them around for better visibility. ```{r make_better_plot_with_labels} plotGrovesD3(Dscape$pco, groups=Dmethod, treeNames = names(DengueTrees), # add the tree names as labels colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ``` Alternatively, where labels are too cluttered, it may be preferable not to plot them but to make the tree names available as tooltip text instead: ```{r make_better_plot_with_tooltips} plotGrovesD3(Dscape$pco, groups=Dmethod, tooltip_text = names(DengueTrees), # add the tree names as tooltip text colors=Dcols, col_lab="Tree type", size_var=Dhighlight, size_range = c(100,500), size_lab="", symbol_var=Dbootstraps, symbol_lab="", point_opacity=c(rep(0.4,400),1,1), legend_width=80) ``` The scree plot is available as part of the `treespace` output: ```{r scree_plot} barplot(Dscape$pco$eig, col="navy") ``` We can also view the plot in 3D: ```{r load_rgl} library(rgl) ``` ```{r plot_3D, rgl=TRUE, webgl=TRUE} Dcols3D <- c(rep(Dcols[[1]],200),rep(Dcols[[2]],100),rep(Dcols[[3]],100),Dcols[[2]],Dcols[[3]]) rgl::plot3d(Dscape$pco$li[,1],Dscape$pco$li[,2],Dscape$pco$li[,3], type="s", size=c(rep(1.5,400),3,3), col=Dcols3D, xlab="", ylab="", zlab="") ``` *treespace* analysis ------------- From these plots we can see that *treespace* has identified variation in the trees according to the Kendall Colijn metric ($\lambda=0$, ignoring branch lengths). The NJ and ML bootstrap trees have broadly similar topologies but are different from any of the BEAST trees. We can check whether any bootstrap trees have the same topology as either the NJ or ML tree, as follows: ```{r NJ_and_ML_overlap} # trees with the same topology as the NJ tree: which(as.matrix(Dscape$D)["NJ",]==0) # trees with the same topology as the ML tree: which(as.matrix(Dscape$D)["ML",]==0) ``` This shows that the NJ tree has the same topology as one NJ bootstrap tree and one ML bootstrap tree. The ML tree has the same topology as 15 ML bootstrap trees, but no NJ bootstrap trees. We can compare pairs of trees using the `plotTreeDiff` function to see exactly where their differences arise. Tips with identical ancestry in the two trees are coloured grey, whereas tips with differing ancestry are coloured peach-red, with the colour darkening according to the number of ancestral differences found at each tip. Since we are comparing the trees topologically (ignoring branch lengths, for the moment), we plot with constant branch lengths for clarity. ```{r compare_trees_NJ_v_ML} # comparing NJ and ML: plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE) treeDist(DnjRooted,DfitTreeRooted) ``` We can adjust the plot settings to make the visualisation clearer: ```{r compare_trees_NJ_v_ML_recoloured} # comparing NJ and ML: plotTreeDiff(DnjRooted,DfitTreeRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) ``` For pairwise comparisons it is helpful to find a small number of representative trees. We can find a geometric median tree from the BEAST trees using the `medTree` function: ```{r make_BEAST_median} BEASTmed <- medTree(BEASTtrees) ``` There are two median trees, with identical topology: ```{r compare_BEAST_meds} BEASTmed$trees treeDist(BEASTmed$trees[[1]],BEASTmed$trees[[2]]) ``` so we may select one of them as a BEAST representative tree. Note that for a more thorough analysis it may be appropriate to identify clusters among the BEAST trees and select a summary tree from each cluster: we demonstrate this approach later in the vignette. ```{r save_BEAST_median} BEASTrep <- BEASTmed$trees[[1]] ``` ```{r compare_BEAST_to_other_trees} # comparing BEAST median and NJ: plotTreeDiff(BEASTrep,DnjRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,DnjRooted) # comparing BEAST median and ML: plotTreeDiff(BEASTrep,DfitTreeRooted, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,DfitTreeRooted) # comparing BEAST median to a random BEAST tree: num <- runif(1,1,200) randomBEASTtree <- BEASTtrees[[num]] plotTreeDiff(BEASTrep, randomBEASTtree, use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) treeDist(BEASTrep,randomBEASTtree) ``` Using *treespace* to analyse the BEAST trees in more detail: ------------- We used TreeAnnotator (Drummond and Rambaut, 2007) to create a Maximum Clade Credibility (MCC) tree from amongst the BEAST trees. ```{r BEASTtrees} # load the MCC tree data(DengueBEASTMCC) # concatenate with other BEAST trees BEAST201 <- c(BEASTtrees, DengueBEASTMCC) # compare using treespace: BEASTscape <- treespace(BEAST201, nf=5) # simple plot: plotGrovesD3(BEASTscape$pco) ``` There appear to be clusters of tree topologies within the BEAST trees. We can use the function `findGroves` to identify clusters: ```{r BEASTtrees_clusters} # find clusters or 'groves': BEASTGroves <- findGroves(BEASTscape, nclust=4, clustering = "single") ``` and to find a median tree per cluster: ```{r BEASTtrees_meds} # find median tree(s) per cluster: BEASTMeds <- medTree(BEAST201, groups=BEASTGroves$groups) # for each cluster, select a single median tree to represent it: BEASTMedTrees <- c(BEASTMeds$`1`$trees[[1]], BEASTMeds$`2`$trees[[1]], BEASTMeds$`3`$trees[[1]], BEASTMeds$`4`$trees[[1]]) ``` We can now make the plot again, highlighting the MCC tree and the four median trees: ```{r BEASTtrees_plot, warning=FALSE} # extract the numbers from the tree list 'BEASTtrees' which correspond to the median trees: BEASTMedTreeNums <-c(which(BEASTGroves$groups==1)[[BEASTMeds$`1`$treenumbers[[1]]]], which(BEASTGroves$groups==2)[[BEASTMeds$`2`$treenumbers[[1]]]], which(BEASTGroves$groups==3)[[BEASTMeds$`3`$treenumbers[[1]]]], which(BEASTGroves$groups==4)[[BEASTMeds$`4`$treenumbers[[1]]]]) # prepare a vector to highlight median and MCC trees highlightTrees <- rep(1,201) highlightTrees[[201]] <- 2 highlightTrees[BEASTMedTreeNums] <- 2 # prepare colours: BEASTcols <- c("#66c2a5","#fc8d62","#8da0cb","#e78ac3") # plot: plotGrovesD3(BEASTscape$pco, groups=as.vector(BEASTGroves$groups), colors=BEASTcols, col_lab="Cluster", symbol_var = highlightTrees, size_range = c(60,600), size_var = highlightTrees, legend_width=0) ``` To understand the differences between the representative trees we can use `plotTreeDiff` again, for example: ```{r BEASTtree_diffs} # differences between the MCC tree and the median from the largest cluster: treeDist(DengueBEASTMCC,BEASTMedTrees[[1]]) plotTreeDiff(DengueBEASTMCC,BEASTMedTrees[[1]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) # differences between the median trees from clusters 1 and 2: treeDist(BEASTMedTrees[[1]],BEASTMedTrees[[2]]) plotTreeDiff(BEASTMedTrees[[1]],BEASTMedTrees[[2]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) ``` References -------------- [1] Drummond, A. J., and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7(1), 214. [2] Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. 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Installing *treespace* ------------- To install the development version from github: ```{r install, eval=FALSE} library(devtools) install_github("thibautjombart/treespace") ``` The stable version can be installed from CRAN using: ```{r install2, eval=FALSE} install.packages("treespace") ``` Then, to load the package, use: ```{r load} library("treespace") ``` Content overview ------------- The main functions implemented in *treespace* are: * __`treespace`__: explore landscapes of phylogenetic trees * __`treespaceServer`__: open up an application in a web browser for an interactive exploration of the diversity in a set of trees * __`findGroves`__: identify clusters of similar trees * __`plotGroves`__: scatterplot of groups of trees, and __`plotGrovesD3`__ which enables interactive plotting based on d3.js * __`medTree`__: find geometric median tree(s) to summarise a group of trees Other functions are central to the computations of distances between trees: * __`treeVec`__: characterise a tree by a vector * __`treeDist`__: find the distance between two tree vectors * __`multiDist`__: find the pairwise distances of a list of trees * __`refTreeDist`__: find the distances of a list of trees from a reference tree * __`tipDiff`__: for a pair of trees, list the tips with differing ancestry * __`plotTreeDiff`__: plot a pair of trees, highlighting the tips with differing ancestry Distributed datasets include: * __`woodmiceTrees`__: illustrative set of 201 trees built using the neighbour-joining and bootstrapping example from the woodmice dataset in the ape documentation. * __`DengueTrees`__: 500 trees sampled from a BEAST posterior set of trees from (Drummond and Rambaut, 2007) * __`DengueSeqs`__: 17 dengue virus serotype 4 sequences from (Lanciotti *et al*., 1997), from which the `DengueTrees` were inferred. * __`DengueBEASTMCC`__: the maximum clade credibility (MCC) tree from the `DengueTrees`. Exploring trees with *treespace* -------------- We first load *treespace*, and the packages required for graphics: ```{r load_packages, message=FALSE, warning=FALSE} library("treespace") library("adegenet") library("adegraphics") library("rgl") ``` The function `treespace` defines typologies of phylogenetic trees using a two-step approach: 1. perform pairwise comparisons of trees using various (Euclidean) metrics; by default, the comparison uses the Kendall and Colijn metric (Kendall and Colijn, 2016) which is described in more detail below; other metrics rely on tip distances implemented in *adephylo* (Jombart *et al.*, 2010) and *phangorn* (Schliep 2011). 2. use Metric Multidimensional Scaling (MDS, aka Principal Coordinates Analysis, PCoA) to summarise pairwise distances between the trees as well as possible into a few dimensions; the output of the MDS is typically visualised using scatterplots of the first few Principal Components (PCs); this step relies on the PCoA implemented in *ade4* (Dray and Dufour, 2007). The function `treespace` performs both tasks, returning both the matrix of pairwise tree comparisons (`$D`), and the PCoA (`$pco`). This can be illustrated using randomly generated trees: ```{r treespace} # generate list of trees set.seed(1) x <- rmtree(10, 20) names(x) <- paste("tree", 1:10, sep = "") # use treespace res <- treespace(x, nf=3) names(res) res ``` Pairwise tree distances can be visualised using *adegraphics*: ```{r distances} # table.image table.image(res$D, nclass=30) # table.value with some customization table.value(res$D, nclass=5, method="color", symbol="circle", col=redpal(5)) ``` The best representation of these distances in a 2-dimensional space is given by the first 2 PCs of the MDS. These can be visualised using any scatter plotting tool; here we use the *treespace* function `plotGroves`, based on the *adegraphics* function `scatter`: ```{r plotgroves} plotGroves(res$pco, lab.show=TRUE, lab.cex=1.5) ``` Alternatively, `plotGrovesD3` creates interactive plots based on d3.js: ```{r plotgrovesD3} plotGrovesD3(res$pco, treeNames=1:10) ``` Tree labels can be dragged into new positions to avoid problems such as overlapping. The functionality of `treespace` can be further illustrated using *ape*'s dataset *woodmouse*, from which we built the 201 trees supplied in `woodmiceTrees` using the neighbour-joining and bootstrapping example from the *ape* documentation. ```{r woodmicePlots} data(woodmiceTrees) wm.res <- treespace(woodmiceTrees,nf=3) # PCs are stored in: head(wm.res$pco$li) # plot results plotGrovesD3(wm.res$pco) ``` Packages such as *adegraphics* and *ggplot2* can be used to make alternative plots, for example visualising the density of points within the space. The *treespace* function `multiDist` simply performs the pairwise comparison of trees and outputs a distance matrix. This function may be preferable for large datasets, and when principal co-ordinate analysis is not required. It includes an option to save memory at the expense of computation time. Identifying clusters of trees -------------- Once a typology of trees has been derived using the approach described above, one may want to formally identify clusters of similar trees. One simple approach is: 1. select a few first PCs of the MDS (retaining signal but getting rid of random noise) 2. derive pairwise Euclidean distances between trees based on these PCs 3. use hierarchical clustering to obtain a dendrogram of these trees 4. cut the dendrogram to obtain clusters In *treespace*, the function `findGroves` implements this approach, offering various clustering options (see `?findGroves`). Here we supply the function with our `treespace` output `wm.res` since we have already calculated it, but it is also possible to skip the steps above and directly supply `findGroves` with a multiPhylo list of trees. ```{r findgroves, cache=FALSE} wm.groves <- findGroves(wm.res, nclust=6) names(wm.groves) ``` Note that when the number of clusters (`nclust`) is not provided, the function will display a dendrogram and ask for a cut-off height. The results can be plotted directly using `plotGrovesD3` (see `?plotGrovesD3` for options): ```{r plotgroves2} # basic plot plotGrovesD3(wm.groves) # alternative with improved legend and tooltip text, giving the tree numbers: plotGrovesD3(wm.groves, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster") # plot axes 2 and 3. This helps to show why, for example, clusters 2 and 4 have been identified as separate, despite them appearing to overlap when viewing axes 1 and 2. plotGrovesD3(wm.groves, xax=2, yax=3, tooltip_text=paste0("Tree ",1:201), legend_width=50, col_lab="Cluster") ``` We can also plot in 3D: ```{r plotgroves_3D, rgl=TRUE, webgl=TRUE} # prepare a colour palette: colours <- fac2col(wm.groves$groups, col.pal=funky) plot3d(wm.groves$treespace$pco$li[,1], wm.groves$treespace$pco$li[,2], wm.groves$treespace$pco$li[,3], col=colours, type="s", size=1.5, xlab="", ylab="", zlab="") ``` `treespaceServer`: a web application for *treespace* -------------- The functionalities of `treespace` are also available via a user-friendly web interface, running locally on the default web browser. It can be started by simply typing `treespaceServer()`. The interface allows you to import trees and run `treespace` to view and explore the tree space in 2 or 3 dimensions. It is then straightforward to analyse the tree space by varying $\lambda$, looking for clusters using `findGroves` and saving results in various formats. Individual trees can be easily viewed, including median trees per cluster (see below). Pairs of trees can be viewed together with their tip-differences highlighted using the function `plotTreeDiff`, and collections of trees can be seen together using `densiTree` from the package *phangorn*. It is fully documented in the *help* tab. ```{r shiny_figures, echo=FALSE, fig.retina = NULL} knitr::include_graphics("figs/treespace3d.png", dpi=72) knitr::include_graphics("figs/treespaceTree.png", dpi=72) knitr::include_graphics("figs/treespaceDensiTree.png", dpi=72) ``` Finding median trees -------------- When a set of trees have very similar structures, it makes sense to summarize them into a single 'consensus' tree. In `treespace`, this is achieved by finding the *median tree* for a set of trees according to the Kendall and Colijn metric. That is, we find the tree which is closest to the centre of the set of trees in the tree landscape defined in `treespace`. This procedure is implemented by the function `medTree`: ```{r woodmiceMedian} # get first median tree tre <- medTree(woodmiceTrees)$trees[[1]] # plot tree plot(tre,type="cladogram",edge.width=3, cex=0.8) ``` However, a more complete and accurate summary of the data can be given by finding a summary tree from each cluster. This is achieved using the `groups` argument of `medTree`: ```{r woodmiceCluster1, out.width="600px"} # find median trees for the 6 clusters identified earlier: res <- medTree(woodmiceTrees, wm.groves$groups) # there is one output per cluster names(res) # get the first median of each med.trees <- lapply(res, function(e) ladderize(e$trees[[1]])) # plot trees par(mfrow=c(2,3)) for(i in 1:length(med.trees)) plot(med.trees[[i]], main=paste("cluster",i),cex=1.5) ``` These trees exhibit a number of topological differences, e.g. in the placement of the **(1007S,1208S,0909S)** clade. To examine the differences between the trees in a pairwise manner, we can use the function `plotTreeDiff`, for example: ```{r woodmice_plotTreeDiff} # Compare median trees from clusters 1 and 2: plotTreeDiff(med.trees[[1]],med.trees[[2]], use.edge.length=FALSE, treesFacing = TRUE, colourMethod = "palette", palette = funky) # Compare median trees from clusters 1 and 4, and change aesthetics: plotTreeDiff(med.trees[[1]],med.trees[[4]], type="cladogram", use.edge.length=FALSE, treesFacing = TRUE, edge.width=2, colourMethod="palette", palette=spectral) ``` Performing this analysis enables the detection of distinct representative trees supported by data. Note that in this example we supplied the function `medTree` with the multiPhylo list of trees. A more computationally efficient process (at the expense of using more memory) is to use the option `return.tree.vectors` in the initial `treespace` call, and then supply these vectors directly to `medTree`. In this case, the tree indices are returned by `medTree` but the trees are not (since they were not supplied). Emphasising the placement of certain tips or clades -------------- In some analyses it may be informative to emphasise the placement of particular tips or clades within a set of trees. This can be particularly useful in large trees where the study is focused on a smaller clade. Priority can be given to a list of tips using the argument `emphasise.tips`, whose corresponding values in the vector comparison will be given a weight of `emphasise.weight` times the others (the default is 2, i.e. twice the weight). For example, if we wanted to emphasise where the woodmice trees agree and disagree on the placement of the **(1007S,1208S,0909S)** clade, we can simply emphasise that clade as follows: ```{r woodmice-tip-emphasis} wm3.res <- treespace(woodmiceTrees,nf=2,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3) # plot results plotGrovesD3(wm3.res$pco) ``` It can be seen from the scale of the plot and the density of clustering that the trees are now separated into more distinct clusters. ```{r findgroves-with-emphasis} wm3.groves <- findGroves(woodmiceTrees,nf=3,nclust=6,emphasise.tips=c("No1007S","No1208S","No0909S"),emphasise.weight=3) plotGrovesD3(wm3.groves) ``` Conversely, where the structure of a particular clade is not of interest (for example, lineages within an outgroup which was only included for rooting purposes), those tips can be given a weight less than 1 so as to give them less emphasis in the comparison. We note that although it is possible to give tips a weighting of 0, we advise caution with this as the underlying function will no longer be guaranteed to be a metric. That is, a distance of 0 between two trees will no longer necessarily imply that the trees are identical. In most cases it would be wiser to assign a very small weighting to tips which are not of interest. Method: characterising a tree by a vector -------------- Kendall and Colijn proposed a [metric](http://dx.doi.org/10.1093/molbev/msw124) for comparing rooted phylogenetic trees (Kendall and COlijn, 2016). Each tree is characterised by a vector which notes the placement of the most recent common ancestor (MRCA) of each pair of tips, as demonstrated in this example: ```{r figure_construction, echo=FALSE, fig.retina = NULL} knitr::include_graphics("figs/construction.png", dpi=72) ``` Specifically, it records the distance between the MRCA of a pair of tips $(i,j)$ and the root in two ways: the number of edges $m_{i,j}$, and the path length $M_{i,j}$. It also records the length $p_i$ of each 'pendant' edge between a tip $i$ and its immediate ancestor. This procedure results in two vectors for a tree $T$: $$ m(T) = (m_{1,2}, m_{1,3},...,m_{k-1,k},1,...,1) $$ and $$ M(T) = (M_{1,2}, M_{1,3},...,M_{k-1,k},p_1,...,p_k). $$ In $m(T)$ we record the pendant lengths as 1, as each tip is 1 step from its immediate ancestor. We combine $m$ and $M$ with a parameter $\lambda$ between zero and one to weight the contribution of branch lengths, characterising each tree with a vector $$ v_\lambda(T) = (1-\lambda)m(T) + \lambda M(T). $$ This is implemented as the function __`treeVec`__. For example, ```{r treevec} # generate a random tree: tree <- rtree(6) # topological vector of mrca distances from root: treeVec(tree) # vector of mrca distances from root when lambda=0.5: treeVec(tree,0.5) # vector of mrca distances as a function of lambda: vecAsFunction <- treeVec(tree,return.lambda.function=TRUE) # evaluate the vector at lambda=0.5: vecAsFunction(0.5) ``` The metric -- the distance between two trees -- is the Euclidean distance between these vectors: $$ d_\lambda(T_a, T_b) = || v_\lambda(T_a) - v_\lambda(T_b) ||. $$ This can be found using __`treeDist`__: ```{r treedist} # generate random trees tree_a <- rtree(6) tree_b <- rtree(6) # topological (lambda=0) distance: treeDist(tree_a,tree_b) # branch-length focused (lambda=1) distance: treeDist(tree_a,tree_b,1) ``` References -------------- * Dray, S. and Dufour, A. B. (2007) The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software 22(4): 1-20. * Drummond, A. J. and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7(1), 214. * Jombart, T., Balloux, F. and Dray, S. (2010) adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics 26: 1907-1909. DOI: 10.1093/bioinformatics/btq292 * Kendall, M. and Colijn, C. (2016) Mapping phylogenetic trees to reveal distinct patterns of evolution. Molecular Biology and Evolution, first published online: June 24, 2016. DOI: 10.1093/molbev/msw124 * Lanciotti, R. S., Gubler, D. J. and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. Journal of General Virology, 78(9), 2279-2286. * Schliep, K. P. (2011) phangorn: phylogenetic analysis in R. Bioinformatics 27(4): 592-593. Authors / Contributors -------------- Authors: * [Thibaut Jombart](https://sites.google.com/site/thibautjombart/) * [Michelle Kendall](http://www.imperial.ac.uk/people/m.kendall) Contributors: * [Jacob Almagro-Garcia](http://www.well.ox.ac.uk/jacob-almagro-garcia) * [Caroline Colijn](http://www.imperial.ac.uk/people/c.colijn) Maintainer of the CRAN version: * [Michelle Kendall](http://www.imperial.ac.uk/people/m.kendall) treespace/README.md0000644000176200001440000001257013231612756013517 0ustar liggesusers[![Travis-CI Build Status](https://travis-ci.org/thibautjombart/treespace.png?branch=master)](https://travis-ci.org/thibautjombart/treespace) [![Build status](https://ci.appveyor.com/api/projects/status/klr8khh1ieb26rh4/branch/master?svg=true)](https://ci.appveyor.com/project/thibautjombart/treespace/branch/master) [![CRAN Status Badge](http://www.r-pkg.org/badges/version/treespace)](https://cran.r-project.org/package=treespace) [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/treespace)](https://cran.r-project.org/package=treespace) *treespace*: exploration of landscapes of phylogenetic trees ============================================================ *treespace* implements new methods for the exploration and analysis of distributions of phylogenetic trees for a given set of taxa. Installing *treespace* ------------- To install the development version from github: ```r library(devtools) install_github("thibautjombart/treespace") ``` The stable version can be installed from CRAN using: ```r install.packages("treespace") ``` Then, to load the package, use: ```r library("treespace") ``` ``` ## Loading required package: ape ``` ``` ## Loading required package: ade4 ``` ``` ## Creating a generic function for 'toJSON' from package 'jsonlite' in package 'googleVis' ``` Content overview ---------------- The main functions implemented in *treespace* are: * __`treespace`__: explore landscapes of phylogenetic trees * __`treespaceServer`__: open up an application in a web browser for an interactive exploration of the diversity in a set of trees * __`findGroves`__: identify clusters of similar trees * __`plotGroves`__: scatterplot of groups of trees, and __`plotGrovesD3`__ which enables interactive plotting based on d3.js * __`medTree`__: find geometric median tree(s) to summarise a group of trees * __`wiwTreeDist`__: find the distance between transmission trees by comparing their MRCI depth matrices * __`wiwMedTree`__: find the median of a list of transmission scenarios * __`relatedTreeDist`__: calculate the distances between trees whose tips belong to the same categories but are not necessarily identically labelled * __`treeConcordance`__: calculate the concordance between a category tree and an individuals tree Other functions are central to the computations of distances between trees: * __`treeVec`__: characterise a tree by a vector * __`treeDist`__: find the distance between two tree vectors * __`multiDist`__: find the pairwise distances of a list of trees * __`refTreeDist`__: find the distances of a list of trees from a reference tree * __`tipDiff`__: for a pair of trees, list the tips with differing ancestry * __`plotTreeDiff`__: plot a pair of trees, highlighting the tips with differing ancestry * __`findMRCIs`__: find the most recent common infector (MRCI) matrix from "who infected whom" information * __`tipsMRCAdepths`__: similar to `treeVec` but the output is a matrix where columns 1 and 2 correspond to tip labels and column 3 gives the depth of the MRCA of that pair of tips Distributed datasets include: * __`woodmiceTrees`__: illustrative set of 201 trees built using the neighbour-joining and bootstrapping example from the *woodmice* dataset in the *ape* documentation. * __`DengueTrees`__: 500 trees sampled from a BEAST posterior set of trees from (Drummond and Rambaut, 2007) * __`DengueSeqs`__: 17 dengue virus serotype 4 sequences from (Lanciotti *et al.*, 1997), from which the `DengueTrees` were inferred. * __`DengueBEASTMCC`__: the maximum clade credibility (MCC) tree from the `DengueTrees`.
Documentation ------------- *treespace* is fully documented on a [dedicated website](https://thibautjombart.github.io/treespace/). It also comes with the following vignettes: - [*introduction*](https://thibautjombart.github.io/treespace/articles/introduction.html): general introduction using a worked example. - [*Dengue example*](https://thibautjombart.github.io/treespace/articles/DengueVignette.html): worked example using a Dengue dataset, used in the *treespace* publication. - [*transmission trees*](https://thibautjombart.github.io/treespace/articles/TransmissionTreesVignette.html): worked example using transmission trees. - *tip categories*: introduction to the measures for comparing trees with shared tip label "categories" Contributing / asking a question -------------------------------- Contributions are welcome via **pull requests**. Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms. Questions, feature requests and bugs can be reported using the package's [issue system](https://github.com/thibautjombart/treespace/issues). Authors ------- Authors: * [Thibaut Jombart](https://sites.google.com/site/thibautjombart/) * [Michelle Kendall](http://www.imperial.ac.uk/people/m.kendall) Contributors: * [Jacob Almagro-Garcia](http://www.well.ox.ac.uk/jacob-almagro-garcia) * [Caroline Colijn](http://www.imperial.ac.uk/people/c.colijn) Maintainer of the CRAN version: * [Michelle Kendall](http://www.imperial.ac.uk/people/m.kendall) See details of contributions on:
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Title: Statistical Exploration of Landscapes of Phylogenetic Trees Date: 2018-01-23 Version: 1.1.1 Authors@R: c(TJ = person("Thibaut", "Jombart", email="thibautjombart@gmail.com", role = "aut"), MK = person("Michelle", "Kendall", email="m.kendall@imperial.ac.uk", role = c("aut", "cre")), JAG = person("Jacob", "Almagro-Garcia", role = "aut"), CC = person("Caroline", "Colijn", role = "aut")) Maintainer: Michelle Kendall Description: Tools for the exploration of distributions of phylogenetic trees. This package includes a 'shiny' interface which can be started from R using treespaceServer(). Depends: R (>= 3.1.2), ape, ade4 Imports: adegenet, adegraphics, adephylo, combinat, compiler, distory, fields, htmlwidgets, MASS, parallel, phangorn, phytools, Rcpp, RLumShiny, scatterD3, shiny, shinyBS, utils LinkingTo: Rcpp Suggests: ggplot2, igraph, knitr, pander, RColorBrewer, reshape2, rgl, rmarkdown, testthat License: MIT + file LICENSE LazyData: true Collate: RcppExports.R metrics.R medTree.R treespace.R findGroves.R plotGroves.R plotTreeDiff.R servers.R transmissionTrees.R data.R makeCollapsedTree.R relatedTreeDist.R simulateIndTree.R tipsMRCAdepths.R treeConcordance.R RoxygenNote: 6.0.1 VignetteBuilder: knitr NeedsCompilation: yes Packaged: 2018-01-23 11:26:37 UTC; Michelle Author: Thibaut Jombart [aut], Michelle Kendall [aut, cre], Jacob Almagro-Garcia [aut], Caroline Colijn [aut] Repository: CRAN Date/Publication: 2018-01-23 14:02:57 UTC treespace/man/0000755000176200001440000000000013225441426013003 5ustar liggesuserstreespace/man/plotGrovesD3.Rd0000644000176200001440000000435013164413033015622 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotGroves.R \name{plotGrovesD3} \alias{plotGrovesD3} \title{Scatterplot of groups of trees using \code{scatterD3}} \usage{ plotGrovesD3(x, groups = NULL, xax = 1, yax = 2, treeNames = NULL, symbol_var = NULL, xlab = paste0("Axis ", xax), ylab = paste0("Axis ", yax), ...) } \arguments{ \item{x}{a list returned by \code{\link{findGroves}} or a MDS with class \code{dudi}} \item{groups}{a factor defining groups of trees. If x is a list returned by \code{\link{findGroves}} these will be detected automatically.} \item{xax}{a number indicating which principal component to be used as 'x' axis} \item{yax}{a number indicating which principal component to be used as 'y' axis} \item{treeNames}{if a list of tree names or labels are given, these will be plotted alongside the points. Their size can be altered using \code{labels_size} - see \code{?scatterD3} for more information.} \item{symbol_var}{a factor by which to vary the symbols in the plot} \item{xlab}{the label for the 'x' axis. Defaults to use the value of 'xax'} \item{ylab}{the label for the 'y' axis. Defaults to use the value of 'yax'} \item{...}{further arguments passed to \code{\link{scatterD3}}} } \value{ A \code{scatterD3} plot } \description{ This function displays the scatterplot of the Multidimensional Scaling (MDS) output by treespace, superimposing group information (derived by \code{\link{findGroves}}) using colors. \code{scatterD3} enables interactive plotting based on d3.js, including zooming, panning and fading effects in the legend. } \examples{ \dontrun{ if(require("adegenet") && require("scatterD3")){ ## load data data(woodmiceTrees) ## run findGroves: treespace+clustering res <- findGroves(woodmiceTrees, nf=5, nclust=6) ## basic plot plotGrovesD3(res) ## adding tree labels plotGrovesD3(res, treeNames=1:201) ## customizing: vary the colour and the symbol by group plotGrovesD3(res, symbol_var=res$groups) ## example with no group information plotGrovesD3(res$treespace$pco) } } } \seealso{ \code{\link{findGroves}} to find any clusters in the tree landscape } \author{ Thibaut Jombart \email{thibautjombart@gmail.com} } treespace/man/refTreeDist.Rd0000644000176200001440000000374213164413033015513 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{refTreeDist} \alias{refTreeDist} \title{Metric function for comparing a reference \code{phylo} to \code{multiPhylo} input} \usage{ refTreeDist(refTree, trees, lambda = 0, return.lambda.function = FALSE, emphasise.tips = NULL, emphasise.weight = 2) } \arguments{ \item{refTree}{a tree of class \code{phylo}, the "reference tree".} \item{trees}{an object of the class \code{multiPhylo} containing the trees to be compared to the reference tree} \item{lambda}{a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}.} \item{return.lambda.function}{If true, a function that can be invoked with different lambda values is returned. This function returns the vector of metric values for the given lambda.} \item{emphasise.tips}{an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}.} \item{emphasise.weight}{applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised.} } \value{ The vector of distances, where entry i corresponds to the distance between the i'th tree and the reference tree, or a function that produces the vector of distances given a value for lambda. } \description{ Comparison of a single reference tree to a list of trees using the Kendall Colijn metric. Output is given as a vector of distances from the reference tree. } \examples{ ## generate a single reference tree with 6 tips refTree <- rtree(6) ## generate 10 random trees, each with 6 tips trees <- rmtree(10,6) ## find the distances from each of the 10 random trees to the single reference tree refTreeDist(refTree,trees) } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/treeConcordance.Rd0000644000176200001440000000273013227077753016404 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/treeConcordance.R \name{treeConcordance} \alias{treeConcordance} \title{Tree concordance} \usage{ treeConcordance(catTree, indTree, df) } \arguments{ \item{catTree}{object of class phylo} \item{indTree}{object of class phylo} \item{df}{data frame specifying to which category each individual belongs. Each row gives an individual (column 2) and its corresponding category (column 1)} } \description{ This function calculates the concordance between a category tree and an individuals tree } \examples{ # create an example category tree catTree <- read.tree(text="(C:1,(B:1,A:1):1);") plot(catTree) # make individuals tree with complete concordance: indTree1 <- read.tree(text="(((c4,c3),(c2,c1)),((b1,b2),((a3,a2),a1)));") plot(indTree1) #' # create data frame linking categories with individuals df <- cbind(c(rep("A",3),rep("B",2),rep("C",4)),sort(indTree1$tip.label)) treeConcordance(catTree,indTree1,df) # make a less concordant tree: indTree2 <- read.tree(text="((((c4,c3),(c2,c1)),b2),(b1,((a3,a2),a1)));") plot(indTree2) treeConcordance(catTree,indTree2,df) # simulate larger example: catTree <- rtree(10) indTree3 <- simulateIndTree(catTree, tipPercent=10) df <- cbind(sort(rep(catTree$tip.label,5)),sort(indTree3$tip.label)) plot(indTree3) treeConcordance(catTree,indTree3,df) } \seealso{ \code{\link{simulateIndTree}} \code{\link{makeCollapsedTree}} } treespace/man/plotTreeDiff.Rd0000644000176200001440000001252413207511745015667 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotTreeDiff.R \name{plotTreeDiff} \alias{plotTreeDiff} \title{Plot tree differences} \usage{ plotTreeDiff(tr1, tr2, tipDiff = NULL, vec1 = NULL, vec2 = NULL, sizeOfDifferences = FALSE, tipMatch = TRUE, treesFacing = FALSE, baseCol = "grey", col1 = "peachpuff", col2 = "red2", colourMethod = "ramp", palette = lightseasun, ...) } \arguments{ \item{tr1}{an object of the class \code{phylo}: the first tree to plot.} \item{tr2}{an object of the class \code{phylo}: the second tree to plot.} \item{tipDiff}{an optional input, the result of \code{\link{tipDiff}}. Supplying this will save time if calling \code{plotTreeDiff} repeatedly, for example with different aesthetics.} \item{vec1}{an optional input, the result of \code{treeVec(tr1, lambda=0)}. This argument is ignored if \code{tipDiff} is supplied; otherwise supplying this will save time if calling \code{plotTreeDiff} repeatedly, for example with different aesthetics.} \item{vec2}{an optional input, the result of \code{treeVec(tr2, lambda=0)}. This argument is ignored if \code{tipDiff} is supplied; otherwise supplying this will save time if calling \code{plotTreeDiff} repeatedly, for example with different aesthetics.} \item{sizeOfDifferences}{a logical (default FALSE) specifying whether the size of the tip differences should be used, or just a count of the number of differences (see \code{tipDiff})} \item{tipMatch}{a logical (default TRUE) specifying whether the second tree should be rotated so that, as far as possible, each of its tips lies opposite its equivalent in the first tree} \item{treesFacing}{a logical (default FALSE) specifying whether the trees should be plotted facing each other - that is, with the second tree plotted "leftwards".} \item{baseCol}{the colour used for tips with identical ancestry in the two trees. Defaults to "grey".} \item{col1}{the first colour used to define the colour spectrum for tips with differences. This colour will be used for tips with minor differences. Defaults to "peachpuff". Ignored if \code{colourMethod="palette"}.} \item{col2}{the second colour used to define the colour spectrum for tips with differences. This colour will be used for tips with major differences. Defaults to "red2". Ignored if \code{colourMethod="palette"}.} \item{colourMethod}{the method to use for colouring. Default is "ramp", corresponding to the original implementation, where the function \code{colorRampPalette} is used to create a palette which ranges from \code{col1} to \code{col2}. For large trees this can be hard to interpret, and method \code{palette} may be preferred, which permits the selection of a palette to use in \code{adegenet}'s function \code{num2col}.} \item{palette}{the colour palette to be used if \code{colourMethod="palette"}. For a list of available palettes see \code{?num2col}.} \item{...}{further arguments passed to \code{\link{plot.phylo}}} } \value{ A plot of the two trees side by side. Tips are coloured in the following way: \itemize{ \item if each ancestor of a tip in tree 1 occurs in tree 2 with the same partition of tip descendants, then the tip is coloured grey (or supplied "baseCol") \item if not, the tip gets coloured pale orange to red on a scale according to how many differences there are amongst its most recent common ancestors with other tips. The colour spectrum can be changed according to preference. } } \description{ Highlight the topological differences between two trees, plotted side by side. This function is useful for comparing representative "median" trees - see \code{\link{medTree}}. It relies on the function \code{\link{tipDiff}}. } \examples{ ## simple example on trees with five tips: tr1 <- read.tree(text="((A:1,B:1):1,((C:1,D:1):1,E:1):1):1;") tr2 <- read.tree(text="((A:1,B:1):1,(C:1,(D:1,E:1):1):1):1;") plotTreeDiff(tr1,tr2) ## example on larger woodmice trees data(woodmiceTrees) tr1 <- woodmiceTrees[[1]] tr2 <- woodmiceTrees[[57]] # for example # find the tip differences in advance, to avoid recalculating with each plot wmTipDiff <- tipDiff(tr1,tr2, sizeOfDifferences=TRUE) plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, tipMatch=TRUE) ## change aesthetics: # trees facing each other: plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, treesFacing=TRUE) # radial plots, and change colours: plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, baseCol="grey2", col1="cyan", col2="navy", edge.width=2, type="radial", cex=0.5, font=2) # cladogram plots, and use colour palette from adegenet to see differences more clearly: plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, treesFacing=TRUE, baseCol="black", colourMethod="palette", edge.width=2, type="cladogram", cex=0.5, font=2) # including the size of the differences highlights tip "No0906s" a little more: # (this is typically a more informative plot in cases where many tips have the # same difference count, for example when a whole clade has been shifted "up" # or "down" the tree but its internal topology remains the same.) plotTreeDiff(tr1,tr2, tipDiff=wmTipDiff, sizeOfDifferences=TRUE, treesFacing=TRUE, baseCol="black", colourMethod="palette", edge.width=2, type="cladogram", cex=0.5, font=2) } \seealso{ \code{\link{medTree}}, \code{\link{tipDiff}} } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/wiwTreeDist.Rd0000644000176200001440000000267613164413033015552 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/transmissionTrees.R \name{wiwTreeDist} \alias{wiwTreeDist} \title{Transmission tree distance} \usage{ wiwTreeDist(matList, sampled = NULL) } \arguments{ \item{matList}{a list of matrices, each of which is the output of \code{findMRCIs$mrciDepths}} \item{sampled}{a vector of node IDs which corresponds to those nodes which are sampled cases. Default is to treat all nodes as sampled cases.} } \value{ Returns a distance matrix, where entry (i,j) is the transmission tree distance between matrices i and j in \code{matList} } \description{ Function to find the distance between transmission trees by comparing their MRCI depth matrices; to be precise, by finding the Euclidean distance between the tree vectors, restricted to their sampled node entries. } \examples{ # create some simple "who infected whom" scenarios: tree1 <- cbind(Infector=1:5,Infectee=2:6) tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6) tree3 <- cbind(Infector=c(2,2,3,4,5),Infectee=c(1,3,4,5,6)) # create list of the MRCI depth matrices: matList <- lapply(list(tree1,tree2,tree3), function(x) findMRCIs(x)$mrciDepths) # transmission tree distance, assuming all cases are sampled: wiwTreeDist(matList) # transmission tree distance when cases 1, 2 and 4 are sampled: wiwTreeDist(matList, sampled=c(1,2,4)) } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/relatedTreeDist.Rd0000644000176200001440000000357613225441426016371 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/relatedTreeDist.R \name{relatedTreeDist} \alias{relatedTreeDist} \title{Tree distance when trees have "related" tips} \usage{ relatedTreeDist(trees, df, checkTrees = TRUE) } \arguments{ \item{trees}{an object of class multiphylo} \item{df}{a data frame specifying to which category each individual (from all the trees) belongs. Each row gives: an individual (column 2) and its corresponding category (column 1)} \item{checkTrees}{a logical (default TRUE) specifying whether the trees should be checked. When TRUE, error messages will be helpful in locating problematic trees, that is, any trees with repeated tip labels, or any trees with missing categories.} } \description{ This function calculates the distances between trees whose tips belong to the same categories but are not necessarily identically labelled } \examples{ # we will simulate some trees as an example, each "based" on the same tree: baseTree <- rtree(5) baseTree$tip.label <- letters[5:1] plot(baseTree) tree1 <- simulateIndTree(baseTree, itips=3, permuteTips=FALSE) tree2 <- simulateIndTree(baseTree, itips=4, permuteTips=FALSE) tree3 <- simulateIndTree(baseTree, itips=4, permuteTips=TRUE, tipPercent=20) tree4 <- simulateIndTree(baseTree, itips=4, permuteTips=TRUE, tipPercent=60) tree5 <- simulateIndTree(baseTree, itips=4, permuteTips=TRUE, tipPercent=100) # combine: trees <- list(tree1,tree2,tree3,tree4,tree5) df <- cbind(sort(rep(letters[1:5],4)),sort(paste0(letters[1:5],"_",rep(1:4,5)))) head(df) # Find distances: relatedTreeDist(trees,df) # Note that trees 1 and 2 have different numbers of tips but the relationships between those tips # are identical at the category level, hence the related tree distance is 0. # We can see that the distances between trees increase the more the trees are permuted. } treespace/man/simulateIndTree.Rd0000644000176200001440000000226313225441426016373 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/simulateIndTree.R \name{simulateIndTree} \alias{simulateIndTree} \title{Simulate randomised "individuals" tree} \usage{ simulateIndTree(catTree, itips = 5, permuteCat = FALSE, permuteTips = TRUE, tipPercent = 100) } \arguments{ \item{catTree}{object of class phylo, the category-level tree} \item{itips}{number of individual tips to assign per category} \item{permuteCat}{logical specifying whether to permute the category labels on the category tree before grafting on individual tips. Defaults to FALSE.} \item{permuteTips}{logical specifying whether to permute the individual tip labels after building the individual level tree based on the category tree. Defaults to TRUE.} \item{tipPercent}{number specifying the percentage of tips to be permuted. Defaults to 100, ignored if permuteTips=FALSE.} } \description{ This function takes in a "category" tree and outputs a simulated corresponding "individuals" tree, for testing the concordance measure. } \examples{ tree <- simulateIndTree(rtree(3)) plot(tree) } \seealso{ \code{\link{treeConcordance}} \code{\link{makeCollapsedTree}} } treespace/man/tipsMRCAdepths.Rd0000644000176200001440000000117613225442325016130 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tipsMRCAdepths.R \name{tipsMRCAdepths} \alias{tipsMRCAdepths} \title{Tip-tip MRCA depths} \usage{ tipsMRCAdepths(tree) } \arguments{ \item{tree}{An object of class phylo} } \description{ This function creates a matrix where columns 1 and 2 correspond to tip labels and column 3 gives the depth of the MRCA of that pair of tips. It is strongly based on \code{treeVec} and is used by \code{relatedTreeDist} and \code{treeConcordance} where tip labels belong to "categories". } \examples{ tree <- rtree(10) plot(tree) tipsMRCAdepths(tree) } treespace/man/multiDist.Rd0000644000176200001440000000454513164413033015253 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{multiDist} \alias{multiDist} \title{Metric function for \code{multiPhylo} input} \usage{ multiDist(trees, lambda = 0, return.lambda.function = FALSE, save.memory = FALSE, emphasise.tips = NULL, emphasise.weight = 2) } \arguments{ \item{trees}{an object of the class \code{multiPhylo} containing the trees to be compared} \item{lambda}{a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}.} \item{return.lambda.function}{If true, a function that can be invoked with different lambda values is returned. This function returns the matrix of metric values for the given lambda.} \item{save.memory}{A flag that saves a lot of memory but increases the execution time (not compatible with return.lambda.function=TRUE).} \item{emphasise.tips}{an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}.} \item{emphasise.weight}{applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised.} } \value{ The pairwise tree distance matrix or a function that produces the distance matrix given a value for lambda. } \description{ Comparison of a list of trees using the Kendall Colijn metric. Output is given as a pairwise distance matrix. This is equivalent to the \code{$D} output from \code{treespace} but may be preferable for large datasets, and when principal co-ordinate analysis is not required. It includes an option to save memory at the expense of computation time. } \examples{ ## generate 10 random trees, each with 6 tips trees <- rmtree(10,6) ## pairwise distance matrix when lambda=0 multiDist(trees) ## pairwise distance matrix as a function of lambda: m <- multiDist(trees, return.lambda.function=TRUE) ## evaluate at lambda=0. Equivalent to multiDist(trees). m0 <- m(0) ## save memory by recomputing each tree vector for each pairwise tree comparison (for fixed lambda): m0.5 <- multiDist(trees,0.5,save.memory=TRUE) } \author{ Jacob Almagro-Garcia \email{nativecoder@gmail.com} Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/makeCollapsedTree.Rd0000644000176200001440000000377513225504364016673 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/makeCollapsedTree.R \name{makeCollapsedTree} \alias{makeCollapsedTree} \title{Collapse a tree into a single tip per category} \usage{ makeCollapsedTree(tree, df, warnings = TRUE) } \arguments{ \item{tree}{an object of the class \code{phylo}: the tree to collapse.} \item{df}{a two-column data frame linking tip labels (column 2) with their corresponding categories (column 1).} \item{warnings}{a logical determining whether a warning should be given if there are paraphyletic categories (default TRUE)} } \value{ A tree (class \code{phylo}) whose tip labels are exactly the set of unique categories from \code{df}. } \description{ Reduce a tree with many tips into a tree with a single tip per category. Where a category's tips form a monophyletic clade, the clade is replaced by a single tip labelled by that category. Where a category's tips are paraphyletic, the largest clade for that category is treated as above, and all other tips pruned. } \examples{ # simulate a tree which is monophyletic per category tree <- simulateIndTree(rtree(5), permuteTips=FALSE) df <- cbind(sort(rep(rtree(5)$tip.label,5)),sort(tree$tip.label)) palette <- c("red","blue","black","green","purple")#' tipCols <- palette[as.factor(sapply(tree$tip.label, function(l) df[which(df[,2]==l),1]))] plot(tree, tip.color=tipCols) collapsedTree <- makeCollapsedTree(tree,df) plot(collapsedTree, tip.color=palette[as.factor(collapsedTree$tip.label)]) # simulate a tree which is paraphyletic per category tree <- simulateIndTree(rtree(5), tipPercent=20) tipCols <- palette[as.factor(sapply(tree$tip.label, function(l) df[which(df[,2]==l),1]))] plot(tree, tip.color=tipCols) collapsedTree <- makeCollapsedTree(tree,df) plot(collapsedTree, tip.color=palette[as.factor(collapsedTree$tip.label)]) } \seealso{ \code{\link{treeConcordance}} \code{\link{simulateIndTree}} } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/plotGroves.Rd0000644000176200001440000000537713164413033015445 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotGroves.R \name{plotGroves} \alias{plotGroves} \title{Scatterplot of groups of trees} \usage{ plotGroves(x, groups = NULL, xax = 1, yax = 2, type = c("chull", "ellipse"), col.pal = funky, bg = "white", lab.show = FALSE, lab.col = "black", lab.cex = 1, lab.optim = TRUE, point.cex = 1, scree.pal = NULL, scree.size = 0.2, scree.posi = c(0.02, 0.02), ...) } \arguments{ \item{x}{a list returned by \code{\link{findGroves}} or a MDS with class \code{dudi}} \item{groups}{a factor defining groups of trees} \item{xax}{a number indicating which principal component to be used as 'x' axis} \item{yax}{a number indicating which principal component to be used as 'y' axis} \item{type}{a character string indicating which type of graph to use} \item{col.pal}{a color palette to be used for the groups} \item{bg}{the background color} \item{lab.show}{a logical indicating whether labels should be displayed} \item{lab.col}{a color for the labels} \item{lab.cex}{the size of the labels} \item{lab.optim}{a logical indicating whether label positions should be optimized to avoid overlap; better display but time-consuming for large datasets} \item{point.cex}{the size of the points} \item{scree.pal}{a color palette for the screeplot} \item{scree.size}{a size factor for the screeplot, between 0 and 1} \item{scree.posi}{either a character string or xy coordinates indicating the position of the screeplot.} \item{...}{further arguments passed to \code{\link{s.class}}} } \value{ An \code{adegraphics} object (class: \code{ADEgS}) } \description{ This function displays the scatterplot of the Multidimensional Scaling (MDS) output by treespace, superimposing group information (derived by \code{\link{findGroves}}) using colors. } \details{ This function relies on \code{\link[adegraphics]{s.class}} from the \code{adegraphics} package. } \examples{ \dontrun{ if(require("adegenet") && require("adegraphics")){ ## load data data(woodmiceTrees) ## run findGroves: treespace+clustering res <- findGroves(woodmiceTrees, nf=5, nclust=6) ## basic plot plotGroves(res) ## adding labels plotGroves(res, lab.show=TRUE) ## customizing plotGroves(res, lab.show=TRUE, bg="black", lab.col="white", scree.size=.35) ## customizing plotGroves(res, type="ellipse", lab.show=TRUE, lab.optim=FALSE, scree.size=.35) ## example with no group information plotGroves(res$treespace$pco) ## adding labels plotGroves(res$treespace$pco, lab.show=TRUE, lab.cex=2) } } } \seealso{ \code{\link{findGroves}} to find any clusters in the tree landscape \code{\link[adegraphics]{s.class}} } \author{ Thibaut Jombart \email{thibautjombart@gmail.com} } treespace/man/treespaceServer.Rd0000644000176200001440000000143313164413033016430 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/servers.R \name{treespaceServer} \alias{treespaceServer} \title{Web-based tree explorer} \usage{ treespaceServer() } \description{ This function opens up an application in a web browser for an interactive exploration of the diversity in a set of trees. For further details please see the "help" tab within the application. } \seealso{ For convenience, \code{treespaceServer} is also available as a separate web app which can be used from any browser (it is not necessary to have \R installed): {\url{http://shiny.imperial-stats-experimental.co.uk/users/mlkendal/treespace/}} } \author{ Thibaut Jombart \email{thibautjombart@gmail.com} Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/DengueSeqs.Rd0000644000176200001440000000127213164413033015332 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{DengueSeqs} \alias{DengueSeqs} \title{Dengue fever sequences} \format{A DNAbin object containing 17 DNA sequences, each of length 1485.} \source{ http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-7-214 } \description{ 17 dengue virus serotype 4 sequences from Lanciotti et al. (1997) } \references{ Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. \emph{Journal of General Virology}, 78(9), 2279-2286. } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } \keyword{datasets} treespace/man/woodmiceTrees.Rd0000644000176200001440000000174513164413033016105 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{woodmiceTrees} \alias{woodmiceTrees} \title{Bootstrap trees from woodmouse dataset} \format{A multiPhylo object containing 201 trees, each with 15 tips} \source{ A set of 15 sequences of the mitochondrial gene cytochrome b of the woodmouse (Apodemus sylvaticus) which is a subset of the data analysed by Michaux et al. (2003). The full data set is available through GenBank (accession numbers AJ511877 to AJ511987) } \description{ These trees were created using the neighbour-joining and bootstrapping example from the ape documentation. } \references{ 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. \emph{Molecular Ecology}, 12, 685-697 } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } \keyword{datasets} treespace/man/treespace.Rd0000644000176200001440000000707713231615030015247 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/treespace.R \name{treespace} \alias{treespace} \title{Phylogenetic tree exploration} \usage{ treespace(x, method = "treeVec", nf = NULL, lambda = 0, return.tree.vectors = FALSE, processors = 1, ...) } \arguments{ \item{x}{an object of the class multiPhylo} \item{method}{the method for summarising the tree as a vector. Choose from: \code{treeVec} (default) the Kendall Colijn metric vector \code{BHV} the Billera, Holmes Vogtmann metric using \code{dist.multiPhylo} from package \code{distory} \code{KF} the Kuhner Felsenstein metric (branch score distance) using \code{KF.dist} from package \code{phangorn} (Note: this considers the trees as unrooted) \code{RF} the Robinson Foulds metric using \code{RF.dist} from package \code{phangorn} (Note: this considers the trees as unrooted and issues a corresponding warning) \code{wRF} the weighted Robinson Foulds metric using \code{wRF.dist} from package \code{phangorn} (Note: this considers the trees as unrooted and issues a corresponding warning) \code{nNodes} the Steel & Penny tip-tip path difference metric, (topological, ignoring branch lengths), using \code{path.dist} from package \code{phangorn} (Note: this considers the trees as unrooted) \code{patristic} the Steel & Penny tip-tip path difference metric, using branch lengths, calling \code{path.dist} from package \code{phangorn} (Note: this considers the trees as unrooted) others inherited from \code{distTips} in \code{adephylo}: \itemize{ \item \code{Abouheif}: performs Abouheif's test. See Pavoine et al. (2008) and \code{adephylo}. \item \code{sumDD}: sum of direct descendants of all nodes on the path, related to Abouheif's test. See \code{adephylo}. }} \item{nf}{the number of principal components to retain} \item{lambda}{a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised in the Kendall Colijn metric.} \item{return.tree.vectors}{option to also return the tree vectors. Note that this can use a lot of memory so defaults to \code{FALSE}.} \item{processors}{value (default 1) to be passed to mcmapply specifying the number of cores to use. Must be 1 on Windows (see \code{mcmapply} for more details).} \item{...}{further arguments to be passed to \code{method}.} } \description{ Compares phylogenetic trees using a choice of metrics / measures, and maps their pairwise distances into a small number of dimensions for easy visualisation and identification of clusters. } \examples{ ## generate list of trees x <- rmtree(10, 20) names(x) <- paste("tree", 1:10, sep = "") ## use treespace res <- treespace(x, nf=3) table.paint(as.matrix(res$D)) scatter(res$pco) data(woodmiceTrees) woodmiceDists <- treespace(woodmiceTrees,nf=3) plot(woodmiceDists$pco$li[,1],woodmiceDists$pco$li[,2]) woodmicedf <- woodmiceDists$pco$li if(require(ggplot2)){ woodmiceplot <- ggplot(woodmicedf, aes(x=A1, y=A2)) # create plot woodmiceplot + geom_density2d(colour="gray80") + # contour lines geom_point(size=6, shape=1, colour="gray50") + # grey edges geom_point(size=6, alpha=0.2, colour="navy") + # transparent blue points xlab("") + ylab("") + theme_bw(base_family="") # remove axis labels and grey background } \dontrun{ if(require(rgl)){ plot3d(woodmicedf[,1], woodmicedf[,2], woodmicedf[,3], type="s", size=1.5, col="navy", alpha=0.5, xlab="", ylab="", zlab="") } } } \author{ Thibaut Jombart \email{thibautjombart@gmail.com} Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/medTree.Rd0000644000176200001440000000733613164413033014663 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/medTree.R \name{medTree} \alias{medTree} \title{Geometric median tree function} \usage{ medTree(x, groups = NULL, lambda = 0, weights = NULL, emphasise.tips = NULL, emphasise.weight = 2, return.lambda.function = FALSE, save.memory = FALSE) } \arguments{ \item{x}{A list of trees of the class multiPhylo, for which the median tree will be computed, \cr OR a matrix of tree vectors as given by \code{treespace$vectors}.} \item{groups}{an optional factor defining groups of trees; if provided, one median tree will be found for each group.} \item{lambda}{a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE} or if the vectors are already supplied as the object \code{x}.} \item{weights}{A vector of weights for the trees. Defaults to a vector of 1's so that all trees are equally weighted, but can be used to encode likelihood, posterior probabilities or other characteristics.} \item{emphasise.tips}{an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}.} \item{emphasise.weight}{applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised.} \item{return.lambda.function}{If true, a function that can be invoked with different lambda values is returned. This function returns the vector of metric values for the given lambda. Ignored if the tree vectors are already supplied as the object \code{x}.} \item{save.memory}{A flag that saves a lot of memory but increases the execution time (not compatible with return.lambda.function=TRUE). Ignored if the tree vectors are already supplied as the object \code{x}.} } \value{ A list of five objects: \itemize{ \item $centre is the "central vector", that is, the (weighted) mean of the tree vectors (which typically does not correspond to a tree itself); \item $distances gives the distance of each tree from the central vector; \item $mindist is the minimum of these distances; \item $treenumbers gives the numbers (and, if supplied, names) of the "median tree(s)", that is, the tree(s) which achieve this minimum distance to the centre; \item $trees if trees were supplied then this returns the median trees as a multiPhylo object. } If groups are provided, then one list is returned for each group. If \code{return.lambda.function=TRUE} then a function is returned that produces this list for a given value of lambda. } \description{ Finds the geometric median of a set of trees according to the Kendall Colijn metric. } \examples{ ## EXAMPLE WITH WOODMICE DATA data(woodmiceTrees) ## LOOKING FOR A SINGLE MEDIAN ## get median tree(s) res <- medTree(woodmiceTrees) res ## plot first tree med.tree <- res$trees[[1]] plot(med.tree) ## LOOKING FOR MEDIANS IN SEVERAL CLUSTERS ## identify 6 clusters groves <- findGroves(woodmiceTrees, nf=3, nclust=6) ## find median trees res.with.grp <- medTree(woodmiceTrees, groves$groups) ## there is one output per cluster names(res.with.grp) ## get the first median of each med.trees <- lapply(res.with.grp, function(e) ladderize(e$trees[[1]])) ## plot trees par(mfrow=c(2,3)) for(i in 1:length(med.trees)) plot(med.trees[[i]], main=paste("cluster",i)) ## highlight the differences between a pair of median trees plotTreeDiff(med.trees[[1]],med.trees[[5]]) } \author{ Jacob Almagro-Garcia \email{nativecoder@gmail.com} Michelle Kendall \email{michelle.louise.kendall@gmail.com} Thibaut Jombart \email{thibautjombart@gmail.com} } treespace/man/findGroves.Rd0000644000176200001440000000365713164413033015406 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/findGroves.R \name{findGroves} \alias{findGroves} \title{Identify clusters of similar trees} \usage{ findGroves(x, method = "treeVec", nf = NULL, clustering = "ward.D2", nclust = NULL, ...) } \arguments{ \item{x}{an object of the class multiPhylo or the output of the function \code{treespace}} \item{method}{(ignored if x is from \code{treespace}) this specifies a function which outputs the summary of a tree in the form of a vector. Defaults to \code{treeVec}.} \item{nf}{(ignored if x is from \code{treespace}) the number of principal components to retain} \item{clustering}{a character string indicating the clustering method to be used; defaults to Ward's method; see argument \code{method} in \code{?hclust} for more details.} \item{nclust}{an integer indicating the number of clusters to find; if not provided, an interactive process based on cutoff threshold selection is used.} \item{...}{further arguments to be passed to \code{treespace}} } \value{ A list containing: \itemize{ \item groups: a factor defining groups of trees \item treespace: the output of treespace } } \description{ This function uses hierarchical clustering on principal components output by \code{\link{treespace}} to identify groups of similar trees. Clustering relies on \code{\link{hclust}}, using Ward's method by default. } \examples{ if(require("adegenet") && require("adegraphics")){ ## load data data(woodmiceTrees) ## run findGroves: treespace+clustering res <- findGroves(woodmiceTrees, nf=5, nclust=6) ## plot results on first 2 axes PCs <- res$treespace$pco$li s.class(PCs, fac=res$groups, col=funky(6)) ## using plotGroves plotGroves(res) } } \seealso{ \code{\link{plotGroves}} to display results } \author{ Thibaut Jombart \email{thibautjombart@gmail.com} Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/linearMrca.Rd0000644000176200001440000000145413164413033015346 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{linearMrca} \alias{linearMrca} \title{Linear MRCA function} \usage{ linearMrca(tree, k = 0) } \arguments{ \item{tree}{an object of the class \code{phylo} which should be rooted.} \item{k}{(optional) number of tips in tree, for faster computation} } \description{ Function to make the most recent common ancestor (MRCA) matrix of a tree, where entry (i,j) gives the MRCA of tips i and j. The function is linear, exploiting the fact that the tree is rooted. } \examples{ ## generate a random tree x <- rtree(6) ## create matrix of MRCAs: entry (i,j) is the node number of the MRCA of tips i and j linearMrca(x,6) } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/fluTrees.Rd0000644000176200001440000000144613164413033015063 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{fluTrees} \alias{fluTrees} \title{BEAST analysis of seasonal influenza (A/H3N2)} \format{A multiPhylo object containing 200 trees, each with 165 tips} \source{ http://beast.bio.ed.ac.uk/tutorials } \description{ These trees were created using BEAST on hemagglutinin (HA) segments of seasonal influenza A/H3N2 samples collected in New-York city (US) between 2000 and 2003. This data comes from the influenza BEAST tutorial distributed at: http://beast.bio.ed.ac.uk/tutorials } \details{ Only the first 200 trees (out of 10,000) were retained. } \references{ http://beast.bio.ed.ac.uk/tutorials } \author{ Thibaut Jombart \email{thibautjombart@gmail.com} } \keyword{datasets} treespace/man/tipDiff.Rd0000644000176200001440000000343213207311126014652 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotTreeDiff.R \name{tipDiff} \alias{tipDiff} \title{Find tip position differences} \usage{ tipDiff(tr1, tr2, vec1 = NULL, vec2 = NULL, sizeOfDifferences = FALSE) } \arguments{ \item{tr1}{an object of the class \code{phylo}: the first tree to compare.} \item{tr2}{an object of the class \code{phylo}: the second tree to compare.} \item{vec1}{an optional input, the result of \code{treeVec(tr1, lambda=0)}, to speed up the computation.} \item{vec2}{an optional input, the result of \code{treeVec(tr2, lambda=0)}, to speed up the computation.} \item{sizeOfDifferences}{a logical (default FALSE) specifying whether the size of the differences in the vectors per tip is also computed} } \value{ A data frame of the tree tips and the number of ancestral differences between them in the two trees, in order of increasing difference. A tip is said to have zero difference if each of its ancestral nodes admits the same tip partition in the two trees. } \description{ Find the topologicial differences between two trees with the same tip labels. The function returns a data frame of the tips and the number of differences in their ancestry between the two trees. Called by \code{\link{plotTreeDiff}}, which highlights the differing tips in a plot of the two trees. } \examples{ ## simple example on trees with five tips: tr1 <- read.tree(text="((A:1,B:1):1,((C:1,D:1):1,E:1):1):1;") tr2 <- read.tree(text="((A:1,B:1):1,(C:1,(D:1,E:1):1):1):1;") tipDiff(tr1,tr2) ## example on larger woodmice trees data(woodmiceTrees) tipDiff(woodmiceTrees[[1]],woodmiceTrees[[2]]) } \seealso{ \code{\link{medTree}} \code{\link{plotTreeDiff}} } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/DengueBEASTMCC.Rd0000644000176200001440000000145513164413033015603 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{DengueBEASTMCC} \alias{DengueBEASTMCC} \title{Dengue fever BEAST MCC tree} \format{A phylo object} \source{ http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-7-214 } \description{ The maximum clade credibility (MCC) tree from \code{\link{DengueTrees}} } \references{ Drummond, A. J., and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. \emph{BMC Evolutionary Biology}, 7(1), 214. Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. \emph{Journal of General Virology}, 78(9), 2279-2286. } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } \keyword{datasets} treespace/man/treeVec.Rd0000644000176200001440000000364313164413033014670 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{treeVec} \alias{treeVec} \title{Tree vector function} \usage{ treeVec(tree, lambda = 0, return.lambda.function = FALSE, emphasise.tips = NULL, emphasise.weight = 2) } \arguments{ \item{tree}{an object of the class \code{phylo}} \item{lambda}{a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}.} \item{return.lambda.function}{If true, a function that can be invoked with different lambda values is returned. This function returns the vector of metric values for the given lambda.} \item{emphasise.tips}{an optional list of tips whose entries in the tree vector should be emphasised. Defaults to \code{NULL}.} \item{emphasise.weight}{applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised.} } \value{ The vector of values according to the metric, or a function that produces the vector given a value of lambda. } \description{ Function which takes an object of class phylo and outputs the vector for the Kendall Colijn metric. The elements of the vector are numeric if \code{return.lambda.function=FALSE} (default), and otherwise they are functions of lambda. } \examples{ ## generate a random tree tree <- rtree(6) ## topological vector of mrca distances from root: treeVec(tree) ## vector of mrca distances from root when lambda=0.5: treeVec(tree,0.5) ## vector of mrca distances as a function of lambda: vecAsFunction <- treeVec(tree,return.lambda.function=TRUE) ## evaluate the vector at lambda=0.5: vecAsFunction(0.5) } \author{ Jacob Almagro-Garcia \email{nativecoder@gmail.com} Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/wiwMedTree.Rd0000644000176200001440000000311713164413033015343 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/transmissionTrees.R \name{wiwMedTree} \alias{wiwMedTree} \title{Median transmission tree} \usage{ wiwMedTree(matList, sampled = NULL, weights = NULL) } \arguments{ \item{matList}{a list of matrices, each of which is the output of \code{findMRCIs$mrciDepths}} \item{sampled}{a vector of node IDs which corresponds to those nodes which are sampled cases} \item{weights}{optional vector of weights to correspond to the entries of matList} } \value{ Returns three objects: \itemize{ \item \code{centre}: the mean of the matList entries, restricted to the sampled cases \item \code{distances}: for each entry of matList, its distance from \code{centre} \item \code{mindist}: the minimum of \code{distances} \item \code{median}: the number of the median entry of matList, i.e. the one(s) which achieve the \code{mindist} from the \code{centre}. } } \description{ Function to find the median of a list of transmission scenarios } \examples{ # create some simple "who infected whom" scenarios: tree1 <- cbind(Infector=1:5,Infectee=2:6) tree2 <- cbind(Infector=c(1,5,2,2,3),Infectee=2:6) tree3 <- cbind(Infector=c(2,2,3,4,5),Infectee=c(1,3,4,5,6)) # create list of the MRCI depth matrices: matList <- lapply(list(tree1,tree2,tree3), function(x) findMRCIs(x)$mrciDepths) # median tree, assuming all cases are sampled: wiwMedTree(matList) # median tree when cases 1, 2 and 4 are sampled: wiwMedTree(matList, sampled=c(1,2,4)) } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/DengueTrees.Rd0000644000176200001440000000252013164413033015476 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{DengueTrees} \alias{DengueTrees} \title{BEAST analysis of Dengue fever} \format{A multiPhylo object containing 500 trees, each with 17 tips} \source{ http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-7-214 } \description{ These trees were created using one of the \code{xml} files provided with the original BEAST paper by Drummond and Rambaut (2007). They provide an example of 17 dengue virus serotype 4 sequences from Lanciotti et al. (1997) (available as \code{\link{DengueSeqs}}) and \code{xml} files with varying priors for model and clock rate. Here we include a random sample of 500 of the trees (from the second half of the posterior) produced using BEAST v1.8 with the standard GTR + Gamma + I substitution model with uncorrelated lognormal-distributed relaxed molecular clock (file 4). } \references{ Drummond, A. J., and Rambaut, A. (2007) BEAST: Bayesian evolutionary analysis by sampling trees. \emph{BMC Evolutionary Biology}, 7(1), 214. Lanciotti, R. S., Gubler, D. J., and Trent, D. W. (1997) Molecular evolution and phylogeny of dengue-4 viruses. \emph{Journal of General Virology}, 78(9), 2279-2286. } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } \keyword{datasets} treespace/man/findMRCIs.Rd0000644000176200001440000000253313205033211015036 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/transmissionTrees.R \name{findMRCIs} \alias{findMRCIs} \title{Find MRCIs} \usage{ findMRCIs(wiw) } \arguments{ \item{wiw}{a two-column matrix where the first column gives the infectors and the second column gives the infectees; each row corresponds to a transmission event from an infector to an infectee.} } \value{ Returns three objects: \itemize{ \item \code{sourceCase}: the number of the node which is the source case, i.e. the common infector of all cases (outputs a warning if there is more than one source case). \item \code{mrcis}: a matrix where, for each pair of individuals i and j, the entry (i,j) is the node number of their MRCI. Note that if i infected j then this entry is i itself. \item \code{mrciDepths}: a matrix where, for each pair of individuals i and j, the entry (i,j) is the depth of their MRCI, defined as the number of edges from the source case. The source case has depth zero, its direct infectees have depth 1, and so on. } } \description{ Function to find the most recent common infector (MRCI) matrix from "who infected whom" information. } \examples{ ## a simple who infected whom matrix: tree1 <- cbind(Infector=1:5,Infectee=2:6) findMRCIs(tree1) } \author{ Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/man/treeDist.Rd0000644000176200001440000000442613164413033015056 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{treeDist} \alias{treeDist} \title{Metric function} \usage{ treeDist(tree.a, tree.b, lambda = 0, return.lambda.function = FALSE, emphasise.tips = NULL, emphasise.weight = 2) } \arguments{ \item{tree.a}{an object of the class \code{phylo}} \item{tree.b}{an object of the class \code{phylo} (with the same tip labels as tree.a)} \item{lambda}{a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE}.} \item{return.lambda.function}{If true, a function that can be invoked with different lambda values is returned. This function returns the vector of metric values for the given lambda.} \item{emphasise.tips}{an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}.} \item{emphasise.weight}{applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised.} } \value{ The distance between the two trees according to the metric for the given value of lambda, or a function that produces the distance given a value of lambda. } \description{ Comparison of two trees using the Kendall Colijn metric } \examples{ ## generate random trees tree.a <- rtree(6) tree.b <- rtree(6) treeDist(tree.a,tree.b) # lambda=0 treeDist(tree.a,tree.b,1) # lambda=1 dist.func <- treeDist(tree.a,tree.b,return.lambda.function=TRUE) # distance as a function of lambda dist.func(0) # evaluate at lambda=0. Equivalent to treeDist(tree.a,tree.b). ## We can see how the distance changes when moving from focusing on topology to length: plot(sapply(seq(0,1,length.out=100), function(x) dist.func(x)), type="l",ylab="",xlab="") ## The distance may also change if we emphasise the position of certain tips: plot(sapply(tree.a$tip.label, function(x) treeDist(tree.a,tree.b,emphasise.tips=x)), xlab="Tip number",ylab="Distance when vector entries corresponding to tip are doubled") } \author{ Jacob Almagro-Garcia \email{nativecoder@gmail.com} Michelle Kendall \email{michelle.louise.kendall@gmail.com} } treespace/LICENSE0000644000176200001440000000010213164413031013217 0ustar liggesusersYEAR: 2016 COPYRIGHT HOLDER: Thibaut Jombart and Michelle Kendall