├── DESCRIPTION
├── NAMESPACE
├── R
    ├── allChecks.R
    ├── formatTree.R
    ├── phylo_species-data.R
    ├── phylo_tree-data.R
    ├── spdata-data.R
    └── treeAndLeaf.R
├── README.md
├── build
    └── vignette.rds
├── data
    ├── phylo_species.RData
    ├── phylo_tree.RData
    └── spdata.RData
├── inst
    ├── NEWS.Rd
    ├── doc
    │   ├── TreeAndLeaf.R
    │   ├── TreeAndLeaf.Rmd
    │   └── TreeAndLeaf.html
    └── unitTests
    │   └── test_tal.R
├── man
    ├── TreeAndLeaf-package.Rd
    ├── formatTree.Rd
    ├── phylo_species.Rd
    ├── phylo_tree.Rd
    ├── spdata.Rd
    └── treeAndLeaf.Rd
├── tests
    └── runTests.R
└── vignettes
    ├── PhyloPurple.png
    ├── QuakesTree.png
    ├── TreeAndLeaf.Rmd
    ├── USAReds.png
    ├── custom.css
    ├── ggtree_tal.png
    ├── nBinPhylo.png
    └── overview.png


/DESCRIPTION:
--------------------------------------------------------------------------------
 1 | Package: TreeAndLeaf
 2 | Type: Package
 3 | Title: Displaying binary trees with focus on dendrogram leaves
 4 | Version: 1.5.3
 5 | Description: The TreeAndLeaf package combines unrooted and force-directed graph algorithms in order to layout binary trees, aiming to represent multiple layers of information onto dendrogram leaves.
 6 | Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro A. A. Castro
 7 | Maintainer: Milena A. Cardoso <milenandreuzo@gmail.com>
 8 | Depends: R(>= 4.0)
 9 | Imports: RedeR(>= 1.40.4), igraph, ape
10 | Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr,
11 |         geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer
12 | License: Artistic-2.0
13 | Encoding: UTF-8
14 | RoxygenNote: 7.1.1
15 | VignetteBuilder: knitr
16 | biocViews: Infrastructure, GraphAndNetwork, Software, Network,
17 |         Visualization, DataRepresentation
18 | NeedsCompilation: no
19 | Packaged: 2021-08-28 15:56:06 UTC; maac
20 | 


--------------------------------------------------------------------------------
/NAMESPACE:
--------------------------------------------------------------------------------
 1 | # Generated by roxygen2: do not edit by hand
 2 | 
 3 | export(formatTree)
 4 | export(treeAndLeaf)
 5 | importFrom(RedeR,RedPort)
 6 | importFrom(ape,as.igraph.phylo)
 7 | importFrom(ape,as.phylo)
 8 | importFrom(igraph,'E<-')
 9 | importFrom(igraph,'V<-')
10 | importFrom(igraph,E)
11 | importFrom(igraph,V)
12 | importFrom(igraph,edge_betweenness)
13 | importFrom(igraph,is.igraph)
14 | 


--------------------------------------------------------------------------------
/R/allChecks.R:
--------------------------------------------------------------------------------
 1 | ##This function is used for argument checking
 2 | tal.checks <- function(name, para){
 3 |     if(identical(name, "tal")) {
 4 |         if(!is.tal(para)){
 5 |             stop("'tal' must be an 'igraph' object created by the 
 6 |                  TreeAndLeaf() function!", call.=FALSE)
 7 |         }
 8 |     } else if(identical(name, "theme")){
 9 |         if(!is.singleInteger(para) || para > 5 || para < 1){
10 |             stop("'theme' must be an integer ranging from 1 to 5!", 
11 |                     call.=FALSE)
12 |         }
13 |     } else if(identical(name, "obj")){
14 |         if (!any(class(para)%in%c("hclust","phylo","ggtree")))
15 |             stop("'obj' must be a 'hclust', 'phylo', or 'ggtree' objects!", 
16 |                  call.=FALSE)
17 |     } else {
18 |         stop("'name' option not available!", call.=FALSE)
19 |     }
20 | }
21 | 
22 | ##------------------------------------------------------------------------------
23 | is.tal <- function(para){
24 |     "tal" %in% class(para)
25 | }
26 | is.singleInteger <- function(para){
27 |     lg <- (is.integer(para) || is.numeric(para)) && length(para)==1L && 
28 |         !is.na(para)
29 |     if(lg) lg <- (para / ceiling(para)) == 1
30 |     return(lg)
31 | }
32 | is.singleString <- function(para){
33 |     is.character(para) && length(para) == 1L && !is.na(para)
34 | }
35 | is.singleLogical <- function(para){
36 |     is.logical(para) && length(para) == 1L && !is.na(para)
37 | }
38 | 


--------------------------------------------------------------------------------
/R/formatTree.R:
--------------------------------------------------------------------------------
 1 | #' A theme function for tree-and-leaf igraph objects.
 2 | #'
 3 | #' This function sets attributes of a tree-and-leaf igraph object for
 4 | #' plotting in the RedeR app interface.
 5 | #' 
 6 | #' @param tal An igraph object generated by the \code{\link{TreeAndLeaf}} function.
 7 | #' @param theme An integer ranging from 1 to 5 with desired theme. \cr
 8 | #' Options: \cr
 9 | #' 1- A clean black and blue theme, for additional customizations. \cr
10 | #' 2- Green palette theme. \cr
11 | #' 3- Blue palette theme. \cr
12 | #' 4- Purple palette theme. \cr
13 | #' 5- Red palette theme. \cr
14 | #' For detailed customization, see attributes in the 
15 | #' \code{\link[RedeR]{addGraph}} method. \cr
16 | #' 
17 | #' @return An igraph object with attributes for RedeR application.
18 | #'
19 | #' @seealso \code{\link[RedeR]{addGraph}}
20 | #' @seealso \code{\link{treeAndLeaf}}
21 | #'
22 | #' @examples
23 | #' library(RedeR)
24 | #' hc <- hclust(dist(USArrests), "ave")
25 | #' tal <- treeAndLeaf(hc)
26 | #' tal <- formatTree(tal, theme = 5)
27 | #'
28 | #' @importFrom igraph V E  
29 | #' @export
30 | 
31 | formatTree <- function(tal, theme = 1){
32 |     tal.checks(name = "tal", para = tal)
33 |     tal.checks(name = "theme", para = theme)
34 |     tal <- .setSizeTheme(tal)
35 |     tal <- .setColorTheme(tal, theme)
36 |     return(tal)
37 | }
38 | #-------------------------------------------------------------------------------
39 | .setSizeTheme <- function(tal){
40 |     sz <- sum(V(tal)$isLeaf)
41 |     sz <- sqrt(sz)
42 |     igraph::V(tal)$nodeFontSize <- ceiling(min(max(15,sz),75))
43 |     igraph::V(tal)$nodeFontSize[!V(tal)$isLeaf] <- 1
44 |     igraph::V(tal)$nodeSize <- ceiling(min(max(15,sz),100))
45 |     igraph::V(tal)$nodeSize[!V(tal)$isLeaf] <- ceiling(min(max(5,sz/6),15))
46 |     igraph::E(tal)$edgeWidth <- ceiling(min(max(15,sz/2),50))
47 |     igraph::V(tal)$nodeLineWidth  <- ceiling(min(max(5,sz/2),20))
48 |     return(tal)
49 | }
50 | #-------------------------------------------------------------------------------
51 | .setColorTheme <- function(tal, theme){
52 |     #Set node line color
53 |     switch (theme,
54 |             igraph::V(tal)$nodeLineColor <- "#000000",
55 |             igraph::V(tal)$nodeLineColor <- "#379683",
56 |             igraph::V(tal)$nodeLineColor <- "#55CFD1",
57 |             igraph::V(tal)$nodeLineColor <- "#7F61A5",
58 |             igraph::V(tal)$nodeLineColor <- "#D4A59A")
59 |     #Set node font color
60 |     switch (theme,
61 |             igraph::V(tal)$nodeFontColor <- "#000000",
62 |             igraph::V(tal)$nodeFontColor <- "#05386B",
63 |             igraph::V(tal)$nodeFontColor <- "#1F2833",
64 |             igraph::V(tal)$nodeFontColor <- "#5C2018",
65 |             igraph::V(tal)$nodeFontColor <- "#5C2018")
66 |     #Set the edge color
67 |     switch (theme,
68 |             igraph::E(tal)$edgeColor <- "#000000",
69 |             igraph::E(tal)$edgeColor <- "#379683",
70 |             igraph::E(tal)$edgeColor <- "#55CFD1",
71 |             igraph::E(tal)$edgeColor <- "#7F61A5",
72 |             igraph::E(tal)$edgeColor <- "#D4A59A")
73 |     #Set the node colors
74 |     switch (theme,
75 |             igraph::V(tal)$nodeColor <- "#190061",
76 |             igraph::V(tal)$nodeColor <- "#5CDB95",
77 |             igraph::V(tal)$nodeColor <- "#45A29E",
78 |             igraph::V(tal)$nodeColor <- "#44318D",
79 |             igraph::V(tal)$nodeColor <- "#BC4639")
80 |     return(tal)
81 | }
82 | 


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/R/phylo_species-data.R:
--------------------------------------------------------------------------------
 1 | #' Species metadata from STRING-db v11
 2 | #'
 3 | #' Species metadata used in a phylo object.
 4 | #' 
 5 | #' @docType data
 6 | #' 
 7 | #' @name phylo_species
 8 | #'
 9 | #' @usage data(phylo_species)
10 | #' 
11 | #' @format An object of class \code{"data.frame"}
12 | #'
13 | #' @keywords datasets
14 | #'
15 | #' @references 
16 | #' Szklarczyk D. et al. STRING v11: protein-protein association networks with 
17 | #' increased coverage, supporting functional discovery in genome-wide 
18 | #' experimental datasets. Nucleic Acids Res. 47:D607-613, 2019.
19 | #' 
20 | NULL


--------------------------------------------------------------------------------
/R/phylo_tree-data.R:
--------------------------------------------------------------------------------
 1 | #' Species tree from STRING-db v11
 2 | #'
 3 | #' A phylo object for the species tree available from the STRING-db v11.
 4 | #' 
 5 | #' @docType data
 6 | #' 
 7 | #' @name phylo_tree
 8 | #'
 9 | #' @usage data(phylo_tree)
10 | #' 
11 | #' @format An object of class \code{"phylo"}
12 | #'
13 | #' @keywords phylotree
14 | #'
15 | #' @references 
16 | #' Szklarczyk D. et al. STRING v11: protein-protein association networks with 
17 | #' increased coverage, supporting functional discovery in genome-wide 
18 | #' experimental datasets. Nucleic Acids Res. 47:D607-613, 2019.
19 | 
20 | #' 
21 | NULL


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/R/spdata-data.R:
--------------------------------------------------------------------------------
 1 | #' Genome statistics for eukaryotes with complete genome sequence
 2 | #'
 3 | #' Data from the NCBI Genome Database for eukaryotes with complete genome sequence.
 4 | #' The list of organisms were obtained from the KEGG Organisms Database, and
 5 | #' taxonomy IDs were obtained from the NCBI Taxonomy Database.
 6 | #' 
 7 | #' @docType data
 8 | #'
 9 | #' @name spdata
10 | #'
11 | #' @usage data(spdata)
12 | #'
13 | #' @format An object of class \code{"data.frame"}
14 | #'
15 | #' @keywords datasets
16 | #'
17 | #' @references
18 | #' NCBI Genome Database
19 | #' https://www.ncbi.nlm.nih.gov/genome
20 | #' Accessed: August 15, 2019.
21 | #' 
22 | #' NCBI Taxonomy Database
23 | #' https://www.ncbi.nlm.nih.gov/taxonomy
24 | #' Accessed: August 15, 2019.
25 | #' 
26 | #' KEGG Organisms Database
27 | #' https://www.genome.jp/kegg/catalog/org_list.html
28 | #' Accessed: August 15, 2019.
29 | #'
30 | #' @examples
31 | #' data(spdata)
32 | NULL


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/R/treeAndLeaf.R:
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  1 | #' Layout a TreeAndLeaf diagram.
  2 | #'
  3 | #' This function tranforms hclust and phylo objects into tree-and-leaf 
  4 | #' igraph objects.
  5 | #'
  6 | #' @param obj An object of class 'hclust' or 'phylo'.
  7 | #' 
  8 | #' @return A tree-and-leaf igraph object.
  9 | #'
 10 | #' @seealso \code{\link{formatTree}}
 11 | #' @seealso \code{\link[stats:hclust]{hclust}}
 12 | #' @seealso \code{\link[ape:as.phylo]{as.phylo}}
 13 | #' @seealso \code{\link[RedeR:addGraph]{addGraph}}
 14 | #' @seealso \code{\link[RedeR:relax]{relax}}
 15 | #'
 16 | #' @examples
 17 | #' library(RedeR)
 18 | #' rdp <- RedPort()
 19 | #' hc <- hclust(dist(USArrests), "ave")
 20 | #' tal <- treeAndLeaf(hc)
 21 | #' 
 22 | #' \dontrun{
 23 | #' calld(rdp)
 24 | #' addGraph(obj=rdp, tal)
 25 | #' }
 26 | #'
 27 | #' @importFrom igraph edge_betweenness is.igraph V E 'V<-' 'E<-' 
 28 | #' @importFrom ape as.phylo as.igraph.phylo
 29 | #' @importFrom RedeR RedPort
 30 | #' @export
 31 | 
 32 | treeAndLeaf <- function(obj){
 33 |     tal.checks(name="obj", para=obj)
 34 |     tal <- .setTaL(obj)
 35 |     return(tal)
 36 | }
 37 | # resetd(rdp)
 38 | # addGraph(rdp, tal)
 39 | #-------------------------------------------------------------------------------
 40 | .setTaL <- function(obj){
 41 |     if ("hclust" %in% class(obj)){
 42 |         phylo <- ape::as.phylo(obj)
 43 |         gg <- ape::as.igraph.phylo(phylo, directed=FALSE)
 44 |         coords <- .get.tree.coords(phylo, use.edge.length=FALSE)
 45 |     } else if("phylo" %in% class(obj)){
 46 |         gg <- ape::as.igraph.phylo(obj, directed=FALSE)
 47 |         coords <- .get.tree.coords(obj, use.edge.length=FALSE)
 48 |     } else if("ggtree" %in% class(obj)){
 49 |         lpar <- obj$layers[[1]]$stat_params$layout
 50 |         vpar <- c("daylight","ape","fan","equal_angle")
 51 |         if(!lpar%in%vpar)
 52 |             stop("Please, use one of the 'ggtree' layouts: ",
 53 |                  paste0(vpar, collapse = ", "),"!", call.=FALSE)
 54 |         # gdata <- ggplot_build(obj)$data[[1]]
 55 |         phylo <- ape::as.phylo(obj$data)
 56 |         gg <- ape::as.igraph.phylo(phylo, directed=FALSE)
 57 |         coords <- .get.tree.coords(phylo, use.edge.length=FALSE)
 58 |         coords$layout[,] <- as.matrix(obj$data[,c("x","y")])
 59 |     }
 60 |     if(!all(V(gg)$name%in%rownames(coords$layout)))
 61 |         stop("An unexpected error occurred while transforming the graph object!")
 62 |     #--- set layout
 63 |     layout <- coords$layout[V(gg)$name,]
 64 |     V(gg)$coordX <- layout[,"x"]
 65 |     V(gg)$coordY <- layout[,"y"]
 66 |     V(gg)$isLeaf <- V(gg)$name%in%coords$phylo$tip.label
 67 |     gg$centralVertex <- coords$centralVertex
 68 |     #--- set edge weights
 69 |     E(gg)$weight <- 0.01
 70 |     bt <- igraph::edge_betweenness(gg, directed = T)
 71 |     bt <- (1 - bt/max(bt))
 72 |     E(gg)$weight <- bt
 73 |     #--- set node and font sizes
 74 |     V(gg)$nodeAlias <- V(gg)$name
 75 |     V(gg)$nodeSize <- 5
 76 |     V(gg)$nodeSize[V(gg)$isLeaf] <- 30
 77 |     V(gg)$nodeFontSize <- 15
 78 |     V(gg)$nodeFontSize[!V(gg)$isLeaf] <- 1
 79 |     V(gg)$nodeColor <- "#ffcccc"
 80 |     V(gg)$nodeLineColor <- "#9999ff"
 81 |     V(gg)$nodeLineWidth <- 2
 82 |     E(gg)$edgeColor <- "#9999ff"
 83 |     E(gg)$edgeWidth <- 2
 84 |     #--- set zoom
 85 |     sz <- sum(V(gg)$isLeaf)
 86 |     sz <- sqrt(sz)
 87 |     # gg$zoom <- ceiling(max(0,100-sz))
 88 |     gg$gtype <- "TreeAndLeaf"
 89 |     class(gg) <- c("tal","igraph")
 90 |     return(gg)
 91 | }
 92 | 
 93 | #-------------------------------------------------------------------------------
 94 | # This function gets coordinates from an unrooted 'phylo' objects,
 95 | # derived from a currectly not exported function from the ape package
 96 | .get.tree.coords <- function(phy, use.edge.length = FALSE){
 97 |     if (is.null(phy$edge.length)) use.edge.length <- FALSE
 98 |     phy <- ape::reorder.phylo(phy)
 99 |     Ntip <- length(phy$tip.label)
100 |     Nedge <- dim(phy$edge)[1]
101 |     Nnode <- phy$Nnode
102 |     nb.sp <- ape::node.depth(phy)
103 |     XY <- if(use.edge.length){
104 |         .unrooted.xy(Ntip, Nnode, phy$edge, phy$edge.length, nb.sp)
105 |     } else {
106 |         .unrooted.xy(Ntip, Nnode, phy$edge, rep(1, Nedge), nb.sp)
107 |     }
108 |     xx <- XY$M[, 1] - min(XY$M[, 1])
109 |     yy <- XY$M[, 2] - min(XY$M[, 2])
110 |     layout <- cbind(x=xx,y=yy)
111 |     if(is.null(phy$node.label)) phy <- ape::makeNodeLabel(phy)
112 |     if(anyDuplicated(c(phy$tip.label, phy$node.label))) 
113 |         stop("Duplicated labels!")
114 |     rownames(layout) <- c(phy$tip.label,phy$node.label)
115 |     centralVertex <- phy$node.label[1]
116 |     return(list(phylo=phy, layout=layout,centralVertex=centralVertex))
117 | }
118 | 
119 | #-------------------------------------------------------------------------------
120 | # This function gets coordinates from an unrooted 'phylo' objects,
121 | # derived from a currectly not exported function from the ape package
122 | .unrooted.xy <- function(Ntip, Nnode, edge, edge.length, nb.sp){
123 |     foo <- function(node, ANGLE, AXIS) {
124 |         ind <- which(edge[, 1] == node)
125 |         sons <- edge[ind, 2]
126 |         start <- AXIS - ANGLE/2
127 |         for (i in 1:length(sons)) {
128 |             h <- edge.length[ind[i]]
129 |             angle[sons[i]] <<- alpha <- ANGLE*nb.sp[sons[i]]/nb.sp[node]
130 |             axis[sons[i]] <<- beta <- start + alpha/2
131 |             start <- start + alpha
132 |             xx[sons[i]] <<- h*cos(beta) + xx[node]
133 |             yy[sons[i]] <<- h*sin(beta) + yy[node]
134 |         }
135 |         for (i in sons)
136 |             if (i > Ntip) foo(i, angle[i], axis[i])
137 |     }
138 |     Nedge <- dim(edge)[1]
139 |     yy <- xx <- numeric(Ntip + Nnode)
140 |     axis <- angle <- numeric(Ntip + Nnode)
141 |     foo(Ntip + 1L, 2*pi, 0)
142 |     M <- cbind(xx, yy)
143 |     axe <- axis[1:Ntip]
144 |     axeGTpi <- axe > pi
145 |     axe[axeGTpi] <- axe[axeGTpi] - 2*pi
146 |     list(M = M, axe = axe)
147 | }
148 | 


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/README.md:
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 1 | # TreeAndLeaf 
 2 | An R package for displaying binary trees, aiming to represent multiple layers of information on dendrogram leaves.
 3 | 
 4 | ## Installation
 5 | Install TreeAndLeaf from github:
 6 | 
 7 | ```{r}
 8 | if (! requireNamespace("devtools", quietly = TRUE))
 9 | install.packages("devtools")
10 | devtools::install_github("sysbiolab/TreeAndLeaf", force = TRUE, build_vignettes = TRUE)
11 | ```
12 | 
13 | For more info on how to use the package:
14 | 
15 | ```{r}
16 | vignette("TreeAndLeaf")
17 | ```
18 | 


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/build/vignette.rds:
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https://raw.githubusercontent.com/sysbiolab/TreeAndLeaf/7ea90400b10199518cc22ec6ad1075acefdd1550/build/vignette.rds


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/data/phylo_species.RData:
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https://raw.githubusercontent.com/sysbiolab/TreeAndLeaf/7ea90400b10199518cc22ec6ad1075acefdd1550/data/phylo_species.RData


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/data/phylo_tree.RData:
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https://raw.githubusercontent.com/sysbiolab/TreeAndLeaf/7ea90400b10199518cc22ec6ad1075acefdd1550/data/phylo_tree.RData


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/data/spdata.RData:
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https://raw.githubusercontent.com/sysbiolab/TreeAndLeaf/7ea90400b10199518cc22ec6ad1075acefdd1550/data/spdata.RData


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/inst/NEWS.Rd:
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 1 | \name{TreeAndLeaf-News}
 2 | \title{TreeAndLeaf News}
 3 | \encoding{UTF-8}
 4 | 
 5 | \section{Version 1.5.1}
 6 |   \itemize{
 7 |     \item Object transformation functions condensed into the `treeAndLeaf` function, to be made automatically [2020-08-24].
 8 |     \item TreeAndLeaf function became more automated.
 9 |     \item The igraph object manipulation was upgraded, through the use of RedeR functions.
10 |     }
11 | }
12 | 
13 | \section{Version 0.99.0}{
14 |   \itemize{
15 |     \item Submitted to Bioconductor [2019-12-16].
16 |   }
17 | }
18 | 
19 | 
20 | 


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/inst/doc/TreeAndLeaf.R:
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  1 | ## ---- include = FALSE---------------------------------------------------------
  2 | knitr::opts_chunk$set(
  3 |     collapse = TRUE,
  4 |     comment = "#>"
  5 | )
  6 | 
  7 | ## ---- eval=TRUE, message=FALSE------------------------------------------------
  8 | #-- Libraries required in this section:
  9 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
 10 | # BiocManager::install(c("TreeAndLeaf","RedeR"))
 11 | # install.packages(c("igraph","RColorBrewer"))
 12 | 
 13 | #-- Load packages
 14 | library("TreeAndLeaf")
 15 | library("RedeR")
 16 | library("igraph")
 17 | library("RColorBrewer")
 18 | 
 19 | ## ---- eval=TRUE, message=FALSE------------------------------------------------
 20 | #-- Check data
 21 | dim(USArrests)
 22 | head(USArrests)
 23 | 
 24 | ## ---- eval=TRUE, message=FALSE------------------------------------------------
 25 | hc <- hclust(dist(USArrests), "ave")
 26 | plot(hc, main="Dendrogram for the 'USArrests' dataset",
 27 |      xlab="", sub="")
 28 | 
 29 | ## ---- eval=FALSE--------------------------------------------------------------
 30 | #  #-- Convert the 'hclust' object into a 'tree-and-leaf' object
 31 | #  tal <- treeAndLeaf(hc)
 32 | 
 33 | ## ---- eval=FALSE--------------------------------------------------------------
 34 | #  #--- Map attributes to the tree-and-leaf
 35 | #  #Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
 36 | #  tal <- att.mapv(g = tal, dat = USArrests, refcol = 0)
 37 | 
 38 | ## ---- eval=FALSE--------------------------------------------------------------
 39 | #  #--- Set graph attributes using the 'att.setv' wrapper function
 40 | #  pal <- brewer.pal(9, "Reds")
 41 | #  tal <- att.setv(g = tal, from = "Murder", to = "nodeColor",
 42 | #                  cols = pal, nquant = 5)
 43 | #  tal <- att.setv(g = tal, from = "UrbanPop", to = "nodeSize",
 44 | #                  xlim = c(10, 50, 5), nquant = 5)
 45 | #  
 46 | #  #--- Set graph attributes using 'att.addv' and 'att.adde' functions
 47 | #  tal <- att.addv(tal, "nodeFontSize", value = 15, index = V(tal)$isLeaf)
 48 | #  tal <- att.adde(tal, "edgeWidth", value = 3)
 49 | 
 50 | ## ---- eval=FALSE--------------------------------------------------------------
 51 | #  #--- Call RedeR application
 52 | #  rdp <- RedPort()
 53 | #  calld(rdp)
 54 | #  resetd(rdp)
 55 | 
 56 | ## ---- eval=FALSE--------------------------------------------------------------
 57 | #  #--- Send the tree-and-leaf to the interactive R/Java interface
 58 | #  addGraph(obj = rdp, g = tal, gzoom=75)
 59 | #  
 60 | #  #--- Call 'relax' to fine-tune the leaf nodes
 61 | #  relax(rdp, p1=25, p2=200, p3=5, p5=5, ps=TRUE)
 62 | 
 63 | ## ---- eval=FALSE--------------------------------------------------------------
 64 | #  #--- Add legends
 65 | #  addLegend.color(obj = rdp, tal, title = "Murder Rate",
 66 | #                  position = "topright")
 67 | #  addLegend.size(obj = rdp, tal, title = "Urban Population Size",
 68 | #                 position = "bottomright")
 69 | 
 70 | ## ---- eval=FALSE, message=FALSE-----------------------------------------------
 71 | #  #-- Libraries required in this section:
 72 | #  #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
 73 | #  # BiocManager::install(c("TreeAndLeaf","RedeR","ggtree))
 74 | #  # install.packages(c("igraph","ape", "dendextend", "dplyr",
 75 | #  #                    "ggplot2", "RColorBrewer"))
 76 | #  
 77 | #  #-- Load packages
 78 | #  library("TreeAndLeaf")
 79 | #  library("RedeR")
 80 | #  library("igraph")
 81 | #  library("ape")
 82 | #  library("ggtree")
 83 | #  library("dendextend")
 84 | #  library("dplyr")
 85 | #  library("ggplot2")
 86 | #  library("RColorBrewer")
 87 | 
 88 | ## ---- eval=FALSE--------------------------------------------------------------
 89 | #  #--- Generate a random phylo tree
 90 | #  phylo_tree <- rcoal(300)
 91 | #  
 92 | #  #--- Set groups and node sizes
 93 | #  group <- size <- dendextend::cutree(phylo_tree, 10)
 94 | #  group[] <- LETTERS[1:10][group]
 95 | #  size[] <- sample(size)
 96 | #  group.df <- data.frame(label=names(group), group=group, size=size)
 97 | #  phylo_tree <- dplyr::full_join(phylo_tree, group.df, by='label')
 98 | #  
 99 | #  #--- Generate a ggtree with 'daylight' layout
100 | #  pal <- brewer.pal(10, "Set3")
101 | #  ggt <- ggtree(phylo_tree, layout = 'daylight', branch.length='none')
102 | #  
103 | #  #--- Plot the ggtree
104 | #  ggt + geom_tippoint(aes(color=group, size=size)) +
105 | #    scale_color_manual(values=pal) + scale_y_reverse()
106 | 
107 | ## ---- eval=FALSE--------------------------------------------------------------
108 | #  #-- Convert the 'ggtree' object into a 'tree-and-leaf' object
109 | #  tal <- treeAndLeaf(ggt)
110 | #  
111 | #  #--- Map attributes to the tree-and-leaf
112 | #  #Note: 'refcol = 1' indicates that 'dat' col 1 will be used as mapping IDs
113 | #  tal <- att.mapv(g = tal, dat = group.df, refcol = 1)
114 | #  
115 | #  #--- Set graph attributes using the 'att.setv' wrapper function
116 | #  tal <- att.setv(g = tal, from = "group", to = "nodeColor",
117 | #                  cols = pal)
118 | #  tal <- att.setv(g = tal, from = "size", to = "nodeSize",
119 | #                  xlim = c(10, 50, 5))
120 | #  
121 | #  #--- Set graph attributes using 'att.addv' and 'att.adde' functions
122 | #  tal <- att.addv(tal, "nodeFontSize", value = 1)
123 | #  tal <- att.addv(tal, "nodeLineWidth", value = 0)
124 | #  tal <- att.addv(tal, "nodeColor", value = "black", index=!V(tal)$isLeaf)
125 | #  tal <- att.adde(tal, "edgeWidth", value = 3)
126 | #  tal <- att.adde(tal, "edgeColor", value = "black")
127 | 
128 | ## ---- eval=FALSE--------------------------------------------------------------
129 | #  #--- Call RedeR application
130 | #  rdp <- RedPort()
131 | #  calld(rdp)
132 | #  resetd(rdp)
133 | 
134 | ## ---- eval=FALSE--------------------------------------------------------------
135 | #  #--- Send the tree-and-leaf to the interactive R/Java interface
136 | #  addGraph(obj = rdp, g = tal, gzoom=50)
137 | #  
138 | #  #--- Select inner nodes, preventing them from relaxing
139 | #  selectNodes(rdp, V(tal)$name[!V(tal)$isLeaf], anchor=TRUE)
140 | #  
141 | #  #--- Call 'relax' to fine-tune the leaf nodes
142 | #  relax(rdp, p1=25, p2=100, p3=5, p5=1, p8=5, ps=TRUE)
143 | #  
144 | #  #--- Add legends
145 | #  addLegend.color(obj = rdp, tal, title = "Group",
146 | #                  position = "topright",vertical=T)
147 | #  addLegend.size(obj = rdp, tal, title = "Size",
148 | #                 position = "topleft",
149 | #                 vertical=T, dxtitle=10)
150 | 
151 | ## ---- eval=FALSE, message=FALSE-----------------------------------------------
152 | #  #-- Libraries required in this section:
153 | #  #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
154 | #  # BiocManager::install(c("TreeAndLeaf","RedeR"))
155 | #  # install.packages(c("igraph", "RColorBrewer"))
156 | #  
157 | #  #-- Load packages
158 | #  library(TreeAndLeaf)
159 | #  library(RedeR)
160 | #  library(igraph)
161 | #  library(RColorBrewer)
162 | 
163 | ## ----echo=TRUE----------------------------------------------------------------
164 | #-- Check data
165 | dim(quakes)
166 | head(quakes)
167 | 
168 | ## ---- eval=TRUE, message=FALSE------------------------------------------------
169 | #-- Building a large dendrogram
170 | hc <- hclust(dist(quakes), "ave")
171 | plot(hc, main="Dendrogram for the 'quakes' dataset",
172 |      xlab="", sub="")
173 | 
174 | ## ---- eval=FALSE--------------------------------------------------------------
175 | #  #-- Convert the 'hclust' object into a 'tree-and-leaf' object
176 | #  tal <- treeAndLeaf(hc)
177 | 
178 | ## ---- eval=FALSE--------------------------------------------------------------
179 | #  #--- Map attributes to the tree-and-leaf
180 | #  #Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
181 | #  tal <- att.mapv(tal, quakes, refcol = 0)
182 | #  
183 | #  #--- Set graph attributes using the 'att.setv' wrapper function
184 | #  pal <- brewer.pal(9, "Greens")
185 | #  tal <- att.setv(g = tal, from = "mag", to = "nodeColor",
186 | #                  cols = pal, nquant = 10)
187 | #  tal <- att.setv(g = tal, from = "depth", to = "nodeSize",
188 | #                  xlim = c(40, 120, 20), nquant = 5)
189 | #  
190 | #  #--- Set graph attributes using 'att.addv' and 'att.adde' functions
191 | #  tal <- att.addv(tal, "nodeFontSize", value = 1)
192 | #  tal <- att.adde(tal, "edgeWidth", value = 10)
193 | 
194 | ## ---- eval=FALSE--------------------------------------------------------------
195 | #  #--- Call RedeR application
196 | #  rdp <- RedPort()
197 | #  calld(rdp)
198 | #  resetd(rdp)
199 | 
200 | ## ---- eval=FALSE--------------------------------------------------------------
201 | #  #--- Send the tree-and-leaf to the interactive R/Java interface
202 | #  addGraph(obj = rdp, g = tal, gzoom=10)
203 | #  
204 | #  #--- Call 'relax' to fine-tune the leaf nodes
205 | #  relax(rdp, p1=25, p2=200, p3=10, p4=100, p5=10, ps=TRUE)
206 | 
207 | ## ---- eval=FALSE--------------------------------------------------------------
208 | #  #--- Add legends
209 | #  addLegend.color(obj = rdp, tal, title = "Richter Magnitude",
210 | #                  position = "bottomright")
211 | #  addLegend.size(obj = rdp, tal, title = "Depth (km)")
212 | 
213 | ## ---- eval=TRUE, message=FALSE------------------------------------------------
214 | #-- Libraries required in this section:
215 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
216 | # BiocManager::install(c("TreeAndLeaf","RedeR","geneplast))
217 | # install.packages(c("igraph","ape", "RColorBrewer"))
218 | 
219 | #-- Load packages
220 | library(TreeAndLeaf)
221 | library(RedeR)
222 | library(igraph)
223 | library(ape)
224 | library(geneplast)
225 | library(RColorBrewer)
226 | 
227 | ## ---- eval=TRUE, message=FALSE------------------------------------------------
228 | #-- Load data and plot the phylogenetic tree
229 | data("spdata")
230 | data("gpdata.gs")
231 | plot(phyloTree)
232 | 
233 | ## ---- eval=FALSE--------------------------------------------------------------
234 | #  #--- Drop organisms not listed in the 'spdata' annotation
235 | #  phyloTree$tip.label <- as.character(phyloTree$tip.label)
236 | #  tokeep <- phyloTree$tip.label %in% spdata$tax_id
237 | #  pruned.phylo <- drop.tip(phyloTree, phyloTree$tip.label[!tokeep])
238 | 
239 | ## ---- eval=FALSE--------------------------------------------------------------
240 | #  #-- Convert the phylogenetic tree into a 'tree-and-leaf' object
241 | #  tal <- treeAndLeaf(pruned.phylo)
242 | #  
243 | #  #--- Map attributes to the tree-and-leaf
244 | #  #Note: 'refcol = 1' indicates that 'dat' col 1 will be used as mapping IDs
245 | #  tal <- att.mapv(g = tal, dat = spdata, refcol = 1)
246 | #  
247 | #  #--- Set graph attributes using the 'att.setv' wrapper function
248 | #  pal <- brewer.pal(9, "Purples")
249 | #  tal <- att.setv(g = tal, from = "genome_size_Mb",
250 | #                  to = "nodeSize", xlim = c(120, 250, 1), nquant = 5)
251 | #  tal <- att.setv (g = tal, from = "proteins",
252 | #                   to = "nodeColor", nquant = 5,
253 | #                   cols = pal, na.col = "black")
254 | 
255 | ## ---- eval=FALSE--------------------------------------------------------------
256 | #  #--- Add graph attributes using 'att.adde' and 'att.addv' functions
257 | #  tal <- att.addv(tal, "nodeFontSize", value = 10)
258 | #  tal <- att.adde(tal, "edgeWidth", value = 20)
259 | #  
260 | #  # Set species names to 'nodeAlias' attribute
261 | #  tal <- att.setv(tal, from = "sp_name", to = "nodeAlias")
262 | #  
263 | #  # Select a few names to highlight in the graph
264 | #  tal <- att.addv(tal, "nodeFontSize", value = 100,
265 | #         filter=list('name'=sample(pruned.phylo$tip.label,30)))
266 | #  tal <- att.addv(tal, "nodeFontSize", value = 100,
267 | #                  filter=list('name'="9606")) #Homo sapiens
268 | 
269 | ## ---- eval=FALSE--------------------------------------------------------------
270 | #  # Call RedeR
271 | #  rdp <- RedPort()
272 | #  calld(rdp)
273 | #  resetd(rdp)
274 | #  
275 | #  #--- Send the tree-and-leaf to the interactive R/Java interface
276 | #  addGraph(obj = rdp, g = tal, gzoom=10)
277 | #  
278 | #  #--- Call 'relax' to fine-tune the leaf nodes
279 | #  relax(rdp, ps=TRUE)
280 | 
281 | ## ---- eval=FALSE--------------------------------------------------------------
282 | #  #--- Add legends
283 | #  addLegend.color(rdp, tal, title = "Proteome Size (n)")
284 | #  addLegend.size(rdp, tal, title = "Genome Size (Mb)")
285 | 
286 | ## ---- eval=FALSE--------------------------------------------------------------
287 | #  #-- Libraries required in this section:
288 | #  #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
289 | #  # BiocManager::install(c("TreeAndLeaf","RedeR","geneplast))
290 | #  # install.packages(c("igraph","ape", "RColorBrewer"))
291 | #  
292 | #  #-- Load packages
293 | #  library(TreeAndLeaf)
294 | #  library(RedeR)
295 | #  library(igraph)
296 | #  library(ape)
297 | #  library(geneplast)
298 | #  library(RColorBrewer)
299 | 
300 | ## ---- eval=FALSE, message=FALSE-----------------------------------------------
301 | #  #-- Load data
302 | #  data("spdata")
303 | #  data("phylo_tree")
304 | 
305 | ## ---- eval=FALSE, message=FALSE-----------------------------------------------
306 | #  #--- Drop organisms not listed in the 'spdata' annotation
307 | #  tokeep <- phylo_tree$tip.label %in% spdata$tax_id
308 | #  pruned.phylo <- drop.tip(phylo_tree, phylo_tree$tip.label[!tokeep])
309 | 
310 | ## ---- eval=FALSE--------------------------------------------------------------
311 | #  #-- Convert the phylogenetic tree into a 'tree-and-leaf' object
312 | #  tal <- treeAndLeaf(pruned.phylo)
313 | 
314 | ## ---- eval=FALSE--------------------------------------------------------------
315 | #  #--- Map attributes to the tree-and-leaf using "%>%" operator
316 | #  tal <- tal %>%
317 | #    att.mapv(dat = spdata, refcol = 1) %>%
318 | #    att.setv(from = "genome_size_Mb", to = "nodeSize",
319 | #             xlim = c(120, 250, 1), nquant = 5) %>%
320 | #    att.setv(from = "proteins", to = "nodeColor", nquant = 5,
321 | #             cols = brewer.pal(9, "Blues"), na.col = "black") %>%
322 | #    att.setv(from = "sp_name", to = "nodeAlias") %>%
323 | #    att.adde(to = "edgeWidth", value = 20) %>%
324 | #    att.addv(to = "nodeFontSize", value = 10) %>%
325 | #    att.addv(to = "nodeFontSize", value = 100,
326 | #        filter = list("name" = sample(pruned.phylo$tip.label, 30))) %>%
327 | #    att.addv(to = "nodeFontSize", value = 100,
328 | #             filter = list("name" = "9606"))
329 | 
330 | ## ---- eval=FALSE--------------------------------------------------------------
331 | #  # Call RedeR
332 | #  rdp <- RedPort()
333 | #  calld(rdp)
334 | #  resetd(rdp)
335 | #  
336 | #  #--- Send the tree-and-leaf to the interactive R/Java interface
337 | #  addGraph(obj = rdp, g = tal, gzoom=5)
338 | #  
339 | #  #--- Call 'relax' to fine-tune the leaf nodes
340 | #  relax(rdp, ps=TRUE)
341 | 
342 | ## ---- eval=FALSE--------------------------------------------------------------
343 | #  #--- Add legends
344 | #  addLegend.color(rdp, tal, title = "Proteome Size (n)")
345 | #  addLegend.size(rdp, tal, title = "Genome size (Mb)")
346 | 
347 | ## ----label='Session information', eval=TRUE, echo=FALSE-----------------------
348 | sessionInfo()
349 | 
350 | 


--------------------------------------------------------------------------------
/inst/doc/TreeAndLeaf.Rmd:
--------------------------------------------------------------------------------
  1 | ---
  2 | title: '*TreeAndLeaf*: A graph layout strategy for binary trees with focus on the leaves'
  3 | author: 'Milena A Cardoso, Luis E A Rizzardi, Leonardo W Kume, Sheyla Trefflich, Clarice Groeneveld, Mauro A A Castro'
  4 | date: "`r BiocStyle::doc_date()`"
  5 | abstract: "The **TreeAndLeaf** package combines tree and force-directed layout algorithms for drawing binary trees, aiming to represent multiple layers of information onto the leaves."
  6 | package: "`r BiocStyle::pkg_ver('TreeAndLeaf')`"
  7 | output:
  8 |     BiocStyle::html_document:
  9 |         css: custom.css
 10 | vignette: >
 11 |   %\VignetteIndexEntry{TreeAndLeaf: an graph layout to dendrograms.}
 12 |   %\VignetteEngine{knitr::rmarkdown}
 13 |   %\VignetteEncoding{UTF-8}
 14 | ---
 15 | 
 16 | ```{r, include = FALSE}
 17 | knitr::opts_chunk$set(
 18 |     collapse = TRUE,
 19 |     comment = "#>"
 20 | )
 21 | ```
 22 | 
 23 | # Overview
 24 | 
 25 | A dendrogram diagram displays binary trees focused on representing 
 26 | hierarchical relations between the tree elements. A dendrogram contains 
 27 | nodes, branches (edges), a root, and leaves (**Figure 1A**). The root is where 
 28 | the branches and nodes come from, indicating the direction to the leaves, 
 29 | *i.e.*, the terminal nodes.
 30 | 
 31 | Most of the space of a dendrogram layout is used to arrange branches and inner 
 32 | nodes, with limited space to the leaves. For large dendrograms, the leaf 
 33 | labels are often squeezed to fit into small slots. Therefore, a dendrogram 
 34 | may not provide the best layout when the information to be displayed should 
 35 | highlight the leaves.
 36 | 
 37 | The **TreeAndLeaf** package aims to improve the visualization of the dendrogram 
 38 | leaves by combining tree and force-directed layout algorithms, shifting the 
 39 | focus of analysis to the leaves (**Figure 1B**). The package's workflow is 
 40 | summarized in **Figure 1C**.
 41 | 
 42 | <p align="center">
 43 | <img width="1000" src="overview.png">
 44 | </p>
 45 | **Figure 1**. **TreeAndLeaf** workflow summary. **(A,B)** The dendrogram in A 
 46 | is used to construct the graph in B. **(C)** The main input for the **TreeAndLeaf** 
 47 | package consists of a dendrogram, and then the package transforms the dendrogram 
 48 | into a graph representation. The finest graph layout is achieved by a two-steps 
 49 | process, starting with an unrooted tree diagram, which is subsequently relaxed 
 50 | by a force-directed algorithm applied to the terminal nodes of the tree. The 
 51 | final *tree-and-leaf* layout varies depending on the initial state, which can be 
 52 | obtained by other tree layout algorithms (see *section 3* for examples using 
 53 | *ggtree* layouts to setup the initial state).
 54 | 
 55 | # Quick Start
 56 | 
 57 | This section provides a basic example using the R built-in `USArrests` dataset. 
 58 | The `USArrests` is a dataframe available in the user's workspace. To know more 
 59 | about this dataframe, please query `?USArrests` in the R console. We will build 
 60 | a dendrogram from the `USArrests` dataset, then transform the dendrogram into 
 61 | a *tree-and-leaf* diagram, and the result will be visualized in the **RedeR** 
 62 | application.
 63 | 
 64 | ## Package and data requirements
 65 | 
 66 | ```{r, eval=TRUE, message=FALSE}
 67 | #-- Libraries required in this section:
 68 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
 69 | # BiocManager::install(c("TreeAndLeaf","RedeR"))
 70 | # install.packages(c("igraph","RColorBrewer"))
 71 | 
 72 | #-- Load packages
 73 | library("TreeAndLeaf")
 74 | library("RedeR")
 75 | library("igraph")
 76 | library("RColorBrewer")
 77 | ```
 78 | 
 79 | ```{r, eval=TRUE, message=FALSE}
 80 | #-- Check data
 81 | dim(USArrests)
 82 | head(USArrests)
 83 | ```
 84 | 
 85 | ## Building a dendrogram example
 86 | 
 87 | In order to build a dendrogram from the `USArrests` dataset, we need a distance 
 88 | matrix. We will use the default "euclidean distance" method from the `dist()` 
 89 | function, and then the "average" method from `hclust()` function to create the 
 90 | dendrogram.
 91 | 
 92 | ```{r, eval=TRUE, message=FALSE}
 93 | hc <- hclust(dist(USArrests), "ave")
 94 | plot(hc, main="Dendrogram for the 'USArrests' dataset",
 95 |      xlab="", sub="")
 96 | ```
 97 | 
 98 | ## Converting the *hclust* object into a *tree-and-leaf* object
 99 | 
100 | The `treeAndLeaf` function will transform the *hclust* into an *igraph* object,
101 | including some basic graph attributes to display in the **RedeR** application.
102 | 
103 | ```{r, eval=FALSE}
104 | #-- Convert the 'hclust' object into a 'tree-and-leaf' object
105 | tal <- treeAndLeaf(hc)
106 | ```
107 | 
108 | ## Setting graph attributes
109 | 
110 | The `att.mapv()` function can be used to add external annotations to an *igraph* 
111 | object, for example, mapping new variables to the graph vertices. We will add 
112 | all `USArrests` variables to the `tal` object. To map one object to another 
113 | it is essential to use the same mapping IDs, set by the `refcol` parameter, 
114 | which points to a column in the input annotation dataset. In this example, 
115 | `refcol = 0` indicates that the `USArrests` rownames will be used as 
116 | mapping IDs. To check the IDs in the *igraph* vertices, please type 
117 | `V(tal)$name` in the R console.
118 | 
119 | ```{r, eval=FALSE}
120 | #--- Map attributes to the tree-and-leaf
121 | #Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
122 | tal <- att.mapv(g = tal, dat = USArrests, refcol = 0)
123 | ```
124 | 
125 | Now we use the `att.setv()` wrapper function to set attributes in the 
126 | *tree-and-leaf* diagram. To see all attributes available to display in the 
127 | **RedeR** application, please type `?addGraph` in the R console. The graph
128 | attributes can also be customized following **igraph** syntax rules.
129 | 
130 | ```{r, eval=FALSE}
131 | #--- Set graph attributes using the 'att.setv' wrapper function
132 | pal <- brewer.pal(9, "Reds")
133 | tal <- att.setv(g = tal, from = "Murder", to = "nodeColor", 
134 |                 cols = pal, nquant = 5)
135 | tal <- att.setv(g = tal, from = "UrbanPop", to = "nodeSize",
136 |                 xlim = c(10, 50, 5), nquant = 5)
137 | 
138 | #--- Set graph attributes using 'att.addv' and 'att.adde' functions
139 | tal <- att.addv(tal, "nodeFontSize", value = 15, index = V(tal)$isLeaf)
140 | tal <- att.adde(tal, "edgeWidth", value = 3)
141 | ```
142 | 
143 | ## Plotting a *tree-and-leaf* diagram
144 | 
145 | The next steps will call the **RedeR** application, and then display the 
146 | *tree-and-leaf* diagram in an interactive R/Java interface. The initial layout
147 | will show an unrooted tree diagram, which will be subsequently relaxed by a 
148 | force-directed algorithm applied to the terminal nodes of the tree.
149 | 
150 | ```{r, eval=FALSE}
151 | #--- Call RedeR application
152 | rdp <- RedPort()
153 | calld(rdp)
154 | resetd(rdp)
155 | ```
156 | 
157 | ```{r, eval=FALSE}
158 | #--- Send the tree-and-leaf to the interactive R/Java interface
159 | addGraph(obj = rdp, g = tal, gzoom=75)
160 | 
161 | #--- Call 'relax' to fine-tune the leaf nodes
162 | relax(rdp, p1=25, p2=200, p3=5, p5=5, ps=TRUE)
163 | ```
164 | 
165 | At this point, the user can interact with the layout process to achieve the 
166 | best or desired layout; we suggest fine-tuning the force-directed algorithm 
167 | parameters, either through the R/Java interface or the command line relaxation 
168 | function. Note that the unroot tree diagram represents the initial state; then 
169 | a relaxing process should start until the finest graph layout is achieved. 
170 | The final layout varies depending on the initial state, which can also be 
171 | adjusted by providing more or less room for the spatial configuration 
172 | (*e.g.* via `gzoom` parameter).
173 | 
174 | ```{r, eval=FALSE}
175 | #--- Add legends
176 | addLegend.color(obj = rdp, tal, title = "Murder Rate", 
177 |                 position = "topright")
178 | addLegend.size(obj = rdp, tal, title = "Urban Population Size",
179 |                position = "bottomright")
180 | ```
181 | 
182 | <p align="center">
183 | <img height="300" src="USAReds.png">
184 | </p>
185 | 
186 | # Setting the initial *tree-and-leaf* state with *ggtree* layouts
187 | 
188 | The tree diagram represents the initial state of a *tree-and-leaf*, which is then 
189 | relaxed by a force-directed algorithm applied to the terminal nodes. Therefore,
190 | the final *tree-and-leaf* layout varies depending on the initial state. The
191 | **treeAndLeaf** package also accepts `ggtree` layouts to setup the initial state. 
192 | For example, next we show a tree diagram generated by the **ggtree** package,
193 | and then we apply the *tree-and-leaf* transformation.
194 | 
195 | ## Package and data requirements
196 | 
197 | ```{r, eval=FALSE, message=FALSE}
198 | #-- Libraries required in this section:
199 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
200 | # BiocManager::install(c("TreeAndLeaf","RedeR","ggtree))
201 | # install.packages(c("igraph","ape", "dendextend", "dplyr", 
202 | #                    "ggplot2", "RColorBrewer"))
203 | 
204 | #-- Load packages
205 | library("TreeAndLeaf")
206 | library("RedeR")
207 | library("igraph")
208 | library("ape")
209 | library("ggtree")
210 | library("dendextend")
211 | library("dplyr")
212 | library("ggplot2")
213 | library("RColorBrewer")
214 | ```
215 | 
216 | ## Building and plotting a *phylo* tree with *ggtree* layouts
217 | 
218 | ```{r, eval=FALSE}
219 | #--- Generate a random phylo tree
220 | phylo_tree <- rcoal(300)
221 | 
222 | #--- Set groups and node sizes
223 | group <- size <- dendextend::cutree(phylo_tree, 10)
224 | group[] <- LETTERS[1:10][group]
225 | size[] <- sample(size)
226 | group.df <- data.frame(label=names(group), group=group, size=size)
227 | phylo_tree <- dplyr::full_join(phylo_tree, group.df, by='label')
228 | 
229 | #--- Generate a ggtree with 'daylight' layout
230 | pal <- brewer.pal(10, "Set3")
231 | ggt <- ggtree(phylo_tree, layout = 'daylight', branch.length='none')
232 | 
233 | #--- Plot the ggtree
234 | ggt + geom_tippoint(aes(color=group, size=size)) +
235 |   scale_color_manual(values=pal) + scale_y_reverse()
236 | ```
237 | 
238 | ## Applying *tree-and-leaf* transformation to *ggtree* layouts
239 | 
240 | ```{r, eval=FALSE}
241 | #-- Convert the 'ggtree' object into a 'tree-and-leaf' object
242 | tal <- treeAndLeaf(ggt)
243 | 
244 | #--- Map attributes to the tree-and-leaf
245 | #Note: 'refcol = 1' indicates that 'dat' col 1 will be used as mapping IDs
246 | tal <- att.mapv(g = tal, dat = group.df, refcol = 1)
247 | 
248 | #--- Set graph attributes using the 'att.setv' wrapper function
249 | tal <- att.setv(g = tal, from = "group", to = "nodeColor",
250 |                 cols = pal)
251 | tal <- att.setv(g = tal, from = "size", to = "nodeSize",
252 |                 xlim = c(10, 50, 5))
253 | 
254 | #--- Set graph attributes using 'att.addv' and 'att.adde' functions
255 | tal <- att.addv(tal, "nodeFontSize", value = 1)
256 | tal <- att.addv(tal, "nodeLineWidth", value = 0)
257 | tal <- att.addv(tal, "nodeColor", value = "black", index=!V(tal)$isLeaf)
258 | tal <- att.adde(tal, "edgeWidth", value = 3)
259 | tal <- att.adde(tal, "edgeColor", value = "black")
260 | ```
261 | 
262 | ```{r, eval=FALSE}
263 | #--- Call RedeR application
264 | rdp <- RedPort()
265 | calld(rdp)
266 | resetd(rdp)
267 | ```
268 | 
269 | ```{r, eval=FALSE}
270 | #--- Send the tree-and-leaf to the interactive R/Java interface
271 | addGraph(obj = rdp, g = tal, gzoom=50)
272 | 
273 | #--- Select inner nodes, preventing them from relaxing
274 | selectNodes(rdp, V(tal)$name[!V(tal)$isLeaf], anchor=TRUE)
275 | 
276 | #--- Call 'relax' to fine-tune the leaf nodes
277 | relax(rdp, p1=25, p2=100, p3=5, p5=1, p8=5, ps=TRUE)
278 | 
279 | #--- Add legends
280 | addLegend.color(obj = rdp, tal, title = "Group", 
281 |                 position = "topright",vertical=T)
282 | addLegend.size(obj = rdp, tal, title = "Size", 
283 |                position = "topleft", 
284 |                vertical=T, dxtitle=10)
285 | ```
286 | 
287 | <p align="center">
288 | <img height="350" src="ggtree_tal.png">
289 | </p>
290 | 
291 | # Case Study 1: visualizing a large dendrogram
292 | 
293 | ## Context
294 | 
295 | This section follows the same steps described in the *Quick Start*, but 
296 | using a larger dendrogram derived from the R built-in `quakes` dataset. 
297 | The `quakes` is a dataframe available in the user's workspace. To know more 
298 | about this dataframe, please query `?quakes` in the R console. 
299 | We will build a dendrogram from the `quakes` dataset, then transform the 
300 | dendrogram into a *tree-and-leaf* diagram, and the result will be visualized 
301 | in the **RedeR** application.
302 | 
303 | ## Package and data requirements
304 | 
305 | ```{r, eval=FALSE, message=FALSE}
306 | #-- Libraries required in this section:
307 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
308 | # BiocManager::install(c("TreeAndLeaf","RedeR"))
309 | # install.packages(c("igraph", "RColorBrewer"))
310 | 
311 | #-- Load packages
312 | library(TreeAndLeaf)
313 | library(RedeR)
314 | library(igraph)
315 | library(RColorBrewer)
316 | ```
317 | 
318 | ```{r echo=TRUE}
319 | #-- Check data
320 | dim(quakes)
321 | head(quakes)
322 | ```
323 | 
324 | ```{r, eval=TRUE, message=FALSE}
325 | #-- Building a large dendrogram
326 | hc <- hclust(dist(quakes), "ave")
327 | plot(hc, main="Dendrogram for the 'quakes' dataset",
328 |      xlab="", sub="")
329 | ```
330 | 
331 | ## Building and plotting a large *tree-and-leaf* diagram
332 | 
333 | ```{r, eval=FALSE}
334 | #-- Convert the 'hclust' object into a 'tree-and-leaf' object
335 | tal <- treeAndLeaf(hc)
336 | ```
337 | 
338 | ```{r, eval=FALSE}
339 | #--- Map attributes to the tree-and-leaf
340 | #Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
341 | tal <- att.mapv(tal, quakes, refcol = 0)
342 | 
343 | #--- Set graph attributes using the 'att.setv' wrapper function
344 | pal <- brewer.pal(9, "Greens")
345 | tal <- att.setv(g = tal, from = "mag", to = "nodeColor",
346 |                 cols = pal, nquant = 10)
347 | tal <- att.setv(g = tal, from = "depth", to = "nodeSize",
348 |                 xlim = c(40, 120, 20), nquant = 5)
349 | 
350 | #--- Set graph attributes using 'att.addv' and 'att.adde' functions
351 | tal <- att.addv(tal, "nodeFontSize", value = 1)
352 | tal <- att.adde(tal, "edgeWidth", value = 10)
353 | ```
354 | 
355 | The next steps will call the **RedeR** application, and then display the 
356 | *tree-and-leaf* diagram in an interactive R/Java interface. The initial layout
357 | will show an unrooted tree diagram, which will be subsequently relaxed by a 
358 | force-directed algorithm applied to the terminal nodes of the tree.
359 | 
360 | ```{r, eval=FALSE}
361 | #--- Call RedeR application
362 | rdp <- RedPort()
363 | calld(rdp)
364 | resetd(rdp)
365 | ```
366 | 
367 | ```{r, eval=FALSE}
368 | #--- Send the tree-and-leaf to the interactive R/Java interface
369 | addGraph(obj = rdp, g = tal, gzoom=10)
370 | 
371 | #--- Call 'relax' to fine-tune the leaf nodes
372 | relax(rdp, p1=25, p2=200, p3=10, p4=100, p5=10, ps=TRUE)
373 | ```
374 | 
375 | ```{r, eval=FALSE}
376 | #--- Add legends
377 | addLegend.color(obj = rdp, tal, title = "Richter Magnitude",
378 |                 position = "bottomright")
379 | addLegend.size(obj = rdp, tal, title = "Depth (km)")
380 | ```
381 | 
382 | <p align="center">
383 | <img height="400" src="QuakesTree.png">
384 | </p>
385 | 
386 | # Case Study 2: visualizing a phylogenetic tree
387 | 
388 | ## Context
389 | 
390 | This section generates a *tree-and-leaf* diagram from a pre-computed `phylo` 
391 | tree object. We will use a phylogenetic tree listing 121 eukaryotes, available 
392 | from the **geneplast** package.
393 | 
394 | ## Package and data requirements
395 | 
396 | ```{r, eval=TRUE, message=FALSE}
397 | #-- Libraries required in this section:
398 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
399 | # BiocManager::install(c("TreeAndLeaf","RedeR","geneplast))
400 | # install.packages(c("igraph","ape", "RColorBrewer"))
401 | 
402 | #-- Load packages
403 | library(TreeAndLeaf)
404 | library(RedeR)
405 | library(igraph)
406 | library(ape)
407 | library(geneplast)
408 | library(RColorBrewer)
409 | ```
410 | 
411 | ```{r, eval=TRUE, message=FALSE}
412 | #-- Load data and plot the phylogenetic tree
413 | data("spdata")
414 | data("gpdata.gs")
415 | plot(phyloTree)
416 | ```
417 | 
418 | ## Building and plotting a *tree-and-leaf* from a phylogenetic tree
419 | 
420 | ```{r, eval=FALSE}
421 | #--- Drop organisms not listed in the 'spdata' annotation
422 | phyloTree$tip.label <- as.character(phyloTree$tip.label)
423 | tokeep <- phyloTree$tip.label %in% spdata$tax_id
424 | pruned.phylo <- drop.tip(phyloTree, phyloTree$tip.label[!tokeep])
425 | ```
426 | 
427 | ```{r, eval=FALSE}
428 | #-- Convert the phylogenetic tree into a 'tree-and-leaf' object
429 | tal <- treeAndLeaf(pruned.phylo)
430 | 
431 | #--- Map attributes to the tree-and-leaf
432 | #Note: 'refcol = 1' indicates that 'dat' col 1 will be used as mapping IDs
433 | tal <- att.mapv(g = tal, dat = spdata, refcol = 1)
434 | 
435 | #--- Set graph attributes using the 'att.setv' wrapper function
436 | pal <- brewer.pal(9, "Purples")
437 | tal <- att.setv(g = tal, from = "genome_size_Mb",
438 |                 to = "nodeSize", xlim = c(120, 250, 1), nquant = 5)
439 | tal <- att.setv (g = tal, from = "proteins",
440 |                  to = "nodeColor", nquant = 5,
441 |                  cols = pal, na.col = "black")
442 | ```
443 | 
444 | ```{r, eval=FALSE}
445 | #--- Add graph attributes using 'att.adde' and 'att.addv' functions
446 | tal <- att.addv(tal, "nodeFontSize", value = 10)
447 | tal <- att.adde(tal, "edgeWidth", value = 20)
448 | 
449 | # Set species names to 'nodeAlias' attribute
450 | tal <- att.setv(tal, from = "sp_name", to = "nodeAlias")
451 | 
452 | # Select a few names to highlight in the graph
453 | tal <- att.addv(tal, "nodeFontSize", value = 100, 
454 |        filter=list('name'=sample(pruned.phylo$tip.label,30)))
455 | tal <- att.addv(tal, "nodeFontSize", value = 100, 
456 |                 filter=list('name'="9606")) #Homo sapiens
457 | ```
458 | 
459 | ```{r, eval=FALSE}
460 | # Call RedeR
461 | rdp <- RedPort()
462 | calld(rdp)
463 | resetd(rdp)
464 | 
465 | #--- Send the tree-and-leaf to the interactive R/Java interface
466 | addGraph(obj = rdp, g = tal, gzoom=10)
467 | 
468 | #--- Call 'relax' to fine-tune the leaf nodes
469 | relax(rdp, ps=TRUE)
470 | ```
471 | 
472 | ```{r, eval=FALSE}
473 | #--- Add legends
474 | addLegend.color(rdp, tal, title = "Proteome Size (n)")
475 | addLegend.size(rdp, tal, title = "Genome Size (Mb)")
476 | ```
477 | 
478 | <p align="center">
479 | <img height="400" src="PhyloPurple.png">
480 | </p>
481 | 
482 | # Case Study 3: visualizing a non-binary tree
483 | 
484 | ## Context
485 | 
486 | The **TreeAndLeaf** package is designed to layout binary trees, but it can also
487 | layout other graph configurations. To exemplify this case, we will use a 
488 | larger phylogenetic tree available from the **geneplast** package, and for 
489 | which some inner nodes have more than two children, or non-binary nodes.
490 | 
491 | ## Package and data requirements
492 | 
493 | ```{r, eval=FALSE}
494 | #-- Libraries required in this section:
495 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
496 | # BiocManager::install(c("TreeAndLeaf","RedeR","geneplast))
497 | # install.packages(c("igraph","ape", "RColorBrewer"))
498 | 
499 | #-- Load packages
500 | library(TreeAndLeaf)
501 | library(RedeR)
502 | library(igraph)
503 | library(ape)
504 | library(geneplast)
505 | library(RColorBrewer)
506 | ```
507 | 
508 | ```{r, eval=FALSE, message=FALSE}
509 | #-- Load data 
510 | data("spdata")
511 | data("phylo_tree")
512 | ```
513 | 
514 | ```{r, eval=FALSE, message=FALSE}
515 | #--- Drop organisms not listed in the 'spdata' annotation
516 | tokeep <- phylo_tree$tip.label %in% spdata$tax_id
517 | pruned.phylo <- drop.tip(phylo_tree, phylo_tree$tip.label[!tokeep])
518 | ```
519 | 
520 | ## Building and plotting a *tree-and-leaf* for a non-binary tree
521 | 
522 | ```{r, eval=FALSE}
523 | #-- Convert the phylogenetic tree into a 'tree-and-leaf' object
524 | tal <- treeAndLeaf(pruned.phylo)
525 | ```
526 | 
527 | ```{r, eval=FALSE}
528 | #--- Map attributes to the tree-and-leaf using "%>%" operator
529 | tal <- tal %>%
530 |   att.mapv(dat = spdata, refcol = 1) %>%
531 |   att.setv(from = "genome_size_Mb", to = "nodeSize",
532 |            xlim = c(120, 250, 1), nquant = 5) %>%
533 |   att.setv(from = "proteins", to = "nodeColor", nquant = 5,
534 |            cols = brewer.pal(9, "Blues"), na.col = "black") %>%
535 |   att.setv(from = "sp_name", to = "nodeAlias") %>%
536 |   att.adde(to = "edgeWidth", value = 20) %>%
537 |   att.addv(to = "nodeFontSize", value = 10) %>%
538 |   att.addv(to = "nodeFontSize", value = 100,
539 |       filter = list("name" = sample(pruned.phylo$tip.label, 30))) %>%
540 |   att.addv(to = "nodeFontSize", value = 100, 
541 |            filter = list("name" = "9606"))
542 | ```
543 | 
544 | ```{r, eval=FALSE}
545 | # Call RedeR
546 | rdp <- RedPort()
547 | calld(rdp)
548 | resetd(rdp)
549 | 
550 | #--- Send the tree-and-leaf to the interactive R/Java interface
551 | addGraph(obj = rdp, g = tal, gzoom=5)
552 | 
553 | #--- Call 'relax' to fine-tune the leaf nodes
554 | relax(rdp, ps=TRUE)
555 | ```
556 | 
557 | ```{r, eval=FALSE}
558 | #--- Add legends
559 | addLegend.color(rdp, tal, title = "Proteome Size (n)")
560 | addLegend.size(rdp, tal, title = "Genome size (Mb)")
561 | ```
562 | 
563 | <p align="center">
564 | <img width="700" src="nBinPhylo.png">
565 | </p>
566 | 
567 | # Session information
568 | 
569 | ```{r label='Session information', eval=TRUE, echo=FALSE}
570 | sessionInfo()
571 | ```
572 | 


--------------------------------------------------------------------------------
/inst/unitTests/test_tal.R:
--------------------------------------------------------------------------------
 1 | test_tal <- function(){
 2 |     hc <- hclust(dist(USArrests), "average")
 3 |     tal <- treeAndLeaf(hc)
 4 |     RUnit::checkTrue(is(tal, "igraph"))
 5 |     RUnit::checkTrue(!is.null(tal$gtype))
 6 |     RUnit::checkTrue(tal$gtype=="TreeAndLeaf")
 7 | }
 8 | test_theme<- function(){
 9 |     hc <- hclust(dist(USArrests), "average")
10 |     tal <- treeAndLeaf(hc)
11 |     tal <- formatTree(tal, theme = 5)
12 |     RUnit::checkTrue(is(tal, "igraph"))
13 |     RUnit::checkTrue(!is.null(tal$gtype))
14 |     RUnit::checkTrue(tal$gtype=="TreeAndLeaf")
15 | }
16 | 
17 | 
18 | 


--------------------------------------------------------------------------------
/man/TreeAndLeaf-package.Rd:
--------------------------------------------------------------------------------
 1 | \name{TreeAndLeaf-package}
 2 | \alias{TreeAndLeaf-package}
 3 | \alias{TreeAndLeaf}
 4 | \docType{package}
 5 | 
 6 | \title{
 7 | TreeAndLeaf: A graph layout for binary trees with focus on dendrogram leaves.
 8 | }
 9 | 
10 | \description{
11 | The TreeAndLeaf package combines unrooted and force-directed graph algorithms 
12 | in order to layout binary trees, aiming to represent multiple layers of 
13 | information onto dendrogram leaves.
14 | }
15 | 
16 | \details{
17 | \tabular{ll}{
18 | Package: \tab TreeAndLeaf\cr
19 | Type: \tab Package\cr
20 | Depends: \tab R (>= 4.0)\cr 
21 | Imports: \tab RedeR, igraph, ape \cr
22 | Suggests: \tab knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr,
23 | RColorBrewer\cr 
24 | License: \tab Artistic-2.0\cr
25 | biocViews: \tab NetworkEnrichment, GraphAndNetwork\cr
26 | }
27 | }
28 | 
29 | \section{Index}{
30 | 
31 | \tabular{ll}{
32 | \link{formatTree}: \tab A theme function for tree-and-leaf igraph objects.\cr
33 | \link{treeAndLeaf}: \tab Layout a TreeAndLeaf diagram.\cr
34 | }
35 | 
36 | Further information is available from the vignettes 
37 | \code{vignette("TreeAndLeaf")}.
38 | }
39 | 
40 | \author{
41 | Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro A. A. Castro
42 | }
43 | 
44 | \references{
45 | CASTRO, M. A. et al. RedeR: R/Bioconductor package for representing modular 
46 | structures, nested networks and multiple levels of hierarchical associations.
47 | \strong{Genome Biology}, 13(4):R29, 2012.
48 | 
49 | CASTRO, M. A. A. et al. Regulators of genetic risk of breast cancer identified 
50 | by integrative network analysis. \strong{Nature Genetics}, 48(1):12–21, 2016.
51 | 
52 | RUSU, A.; SANTIAGO, C. Grid Drawings of Binary Trees: An Experimental 
53 | Study. \strong{Journal of Graph Algorithms and Applications}, 
54 | 12(2):131–195, 2008.
55 | }
56 | 
57 | \keyword{package}
58 | 
59 | 


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/man/formatTree.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/formatTree.R
 3 | \name{formatTree}
 4 | \alias{formatTree}
 5 | \title{A theme function for tree-and-leaf igraph objects.}
 6 | \usage{
 7 | formatTree(tal, theme = 1)
 8 | }
 9 | \arguments{
10 | \item{tal}{An igraph object generated by the \code{\link{TreeAndLeaf}} function.}
11 | 
12 | \item{theme}{An integer ranging from 1 to 5 with desired theme. \cr
13 | Options: \cr
14 | 1- A clean black and blue theme, for additional customizations. \cr
15 | 2- Green palette theme. \cr
16 | 3- Blue palette theme. \cr
17 | 4- Purple palette theme. \cr
18 | 5- Red palette theme. \cr
19 | For detailed customization, see attributes in the 
20 | \code{\link[RedeR]{addGraph}} method. \cr}
21 | }
22 | \value{
23 | An igraph object with attributes for RedeR application.
24 | }
25 | \description{
26 | This function sets attributes of a tree-and-leaf igraph object for
27 | plotting in the RedeR app interface.
28 | }
29 | \examples{
30 | library(RedeR)
31 | hc <- hclust(dist(USArrests), "ave")
32 | tal <- treeAndLeaf(hc)
33 | tal <- formatTree(tal, theme = 5)
34 | 
35 | }
36 | \seealso{
37 | \code{\link[RedeR]{addGraph}}
38 | 
39 | \code{\link{treeAndLeaf}}
40 | }
41 | 


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/man/phylo_species.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/phylo_species-data.R
 3 | \docType{data}
 4 | \name{phylo_species}
 5 | \alias{phylo_species}
 6 | \title{Species metadata from STRING-db v11}
 7 | \format{
 8 | An object of class \code{"data.frame"}
 9 | }
10 | \usage{
11 | data(phylo_species)
12 | }
13 | \description{
14 | Species metadata used in a phylo object.
15 | }
16 | \references{
17 | Szklarczyk D. et al. STRING v11: protein-protein association networks with 
18 | increased coverage, supporting functional discovery in genome-wide 
19 | experimental datasets. Nucleic Acids Res. 47:D607-613, 2019.
20 | }
21 | \keyword{datasets}
22 | 


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/man/phylo_tree.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/phylo_tree-data.R
 3 | \docType{data}
 4 | \name{phylo_tree}
 5 | \alias{phylo_tree}
 6 | \title{Species tree from STRING-db v11}
 7 | \format{
 8 | An object of class \code{"phylo"}
 9 | }
10 | \usage{
11 | data(phylo_tree)
12 | }
13 | \description{
14 | A phylo object for the species tree available from the STRING-db v11.
15 | }
16 | \references{
17 | Szklarczyk D. et al. STRING v11: protein-protein association networks with 
18 | increased coverage, supporting functional discovery in genome-wide 
19 | experimental datasets. Nucleic Acids Res. 47:D607-613, 2019.
20 | }
21 | \keyword{phylotree}
22 | 


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/man/spdata.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/spdata-data.R
 3 | \docType{data}
 4 | \name{spdata}
 5 | \alias{spdata}
 6 | \title{Genome statistics for eukaryotes with complete genome sequence}
 7 | \format{
 8 | An object of class \code{"data.frame"}
 9 | }
10 | \usage{
11 | data(spdata)
12 | }
13 | \description{
14 | Data from the NCBI Genome Database for eukaryotes with complete genome sequence.
15 | The list of organisms were obtained from the KEGG Organisms Database, and
16 | taxonomy IDs were obtained from the NCBI Taxonomy Database.
17 | }
18 | \examples{
19 | data(spdata)
20 | }
21 | \references{
22 | NCBI Genome Database
23 | https://www.ncbi.nlm.nih.gov/genome
24 | Accessed: August 15, 2019.
25 | 
26 | NCBI Taxonomy Database
27 | https://www.ncbi.nlm.nih.gov/taxonomy
28 | Accessed: August 15, 2019.
29 | 
30 | KEGG Organisms Database
31 | https://www.genome.jp/kegg/catalog/org_list.html
32 | Accessed: August 15, 2019.
33 | }
34 | \keyword{datasets}
35 | 


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/man/treeAndLeaf.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/treeAndLeaf.R
 3 | \name{treeAndLeaf}
 4 | \alias{treeAndLeaf}
 5 | \title{Layout a TreeAndLeaf diagram.}
 6 | \usage{
 7 | treeAndLeaf(obj)
 8 | }
 9 | \arguments{
10 | \item{obj}{An object of class 'hclust' or 'phylo'.}
11 | }
12 | \value{
13 | A tree-and-leaf igraph object.
14 | }
15 | \description{
16 | This function tranforms hclust and phylo objects into tree-and-leaf 
17 | igraph objects.
18 | }
19 | \examples{
20 | library(RedeR)
21 | rdp <- RedPort()
22 | hc <- hclust(dist(USArrests), "ave")
23 | tal <- treeAndLeaf(hc)
24 | 
25 | \dontrun{
26 | calld(rdp)
27 | addGraph(obj=rdp, tal)
28 | }
29 | 
30 | }
31 | \seealso{
32 | \code{\link{formatTree}}
33 | 
34 | \code{\link[stats:hclust]{hclust}}
35 | 
36 | \code{\link[ape:as.phylo]{as.phylo}}
37 | 
38 | \code{\link[RedeR:addGraph]{addGraph}}
39 | 
40 | \code{\link[RedeR:relax]{relax}}
41 | }
42 | 


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/tests/runTests.R:
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1 | BiocGenerics:::testPackage("TreeAndLeaf")
2 | 


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/vignettes/PhyloPurple.png:
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https://raw.githubusercontent.com/sysbiolab/TreeAndLeaf/7ea90400b10199518cc22ec6ad1075acefdd1550/vignettes/PhyloPurple.png


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/vignettes/QuakesTree.png:
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https://raw.githubusercontent.com/sysbiolab/TreeAndLeaf/7ea90400b10199518cc22ec6ad1075acefdd1550/vignettes/QuakesTree.png


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/vignettes/TreeAndLeaf.Rmd:
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  1 | ---
  2 | title: '*TreeAndLeaf*: A graph layout strategy for binary trees with focus on the leaves'
  3 | author: 'Milena A Cardoso, Luis E A Rizzardi, Leonardo W Kume, Sheyla Trefflich, Clarice Groeneveld, Mauro A A Castro'
  4 | date: "`r BiocStyle::doc_date()`"
  5 | abstract: "The **TreeAndLeaf** package combines tree and force-directed layout algorithms for drawing binary trees, aiming to represent multiple layers of information onto the leaves."
  6 | package: "`r BiocStyle::pkg_ver('TreeAndLeaf')`"
  7 | output:
  8 |     BiocStyle::html_document:
  9 |         css: custom.css
 10 | vignette: >
 11 |   %\VignetteIndexEntry{TreeAndLeaf: an graph layout to dendrograms.}
 12 |   %\VignetteEngine{knitr::rmarkdown}
 13 |   %\VignetteEncoding{UTF-8}
 14 | ---
 15 | 
 16 | ```{r, include = FALSE}
 17 | knitr::opts_chunk$set(
 18 |     collapse = TRUE,
 19 |     comment = "#>"
 20 | )
 21 | ```
 22 | 
 23 | # Overview
 24 | 
 25 | A dendrogram diagram displays binary trees focused on representing 
 26 | hierarchical relations between the tree elements. A dendrogram contains 
 27 | nodes, branches (edges), a root, and leaves (**Figure 1A**). The root is where 
 28 | the branches and nodes come from, indicating the direction to the leaves, 
 29 | *i.e.*, the terminal nodes.
 30 | 
 31 | Most of the space of a dendrogram layout is used to arrange branches and inner 
 32 | nodes, with limited space to the leaves. For large dendrograms, the leaf 
 33 | labels are often squeezed to fit into small slots. Therefore, a dendrogram 
 34 | may not provide the best layout when the information to be displayed should 
 35 | highlight the leaves.
 36 | 
 37 | The **TreeAndLeaf** package aims to improve the visualization of the dendrogram 
 38 | leaves by combining tree and force-directed layout algorithms, shifting the 
 39 | focus of analysis to the leaves (**Figure 1B**). The package's workflow is 
 40 | summarized in **Figure 1C**.
 41 | 
 42 | <p align="center">
 43 | <img width="1000" src="overview.png">
 44 | </p>
 45 | **Figure 1**. **TreeAndLeaf** workflow summary. **(A,B)** The dendrogram in A 
 46 | is used to construct the graph in B. **(C)** The main input for the **TreeAndLeaf** 
 47 | package consists of a dendrogram, and then the package transforms the dendrogram 
 48 | into a graph representation. The finest graph layout is achieved by a two-steps 
 49 | process, starting with an unrooted tree diagram, which is subsequently relaxed 
 50 | by a force-directed algorithm applied to the terminal nodes of the tree. The 
 51 | final *tree-and-leaf* layout varies depending on the initial state, which can be 
 52 | obtained by other tree layout algorithms (see *section 3* for examples using 
 53 | *ggtree* layouts to setup the initial state).
 54 | 
 55 | # Quick Start
 56 | 
 57 | This section provides a basic example using the R built-in `USArrests` dataset. 
 58 | The `USArrests` is a dataframe available in the user's workspace. To know more 
 59 | about this dataframe, please query `?USArrests` in the R console. We will build 
 60 | a dendrogram from the `USArrests` dataset, then transform the dendrogram into 
 61 | a *tree-and-leaf* diagram, and the result will be visualized in the **RedeR** 
 62 | application.
 63 | 
 64 | ## Package and data requirements
 65 | 
 66 | ```{r, eval=TRUE, message=FALSE}
 67 | #-- Libraries required in this section:
 68 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
 69 | # BiocManager::install(c("TreeAndLeaf","RedeR"))
 70 | # install.packages(c("igraph","RColorBrewer"))
 71 | 
 72 | #-- Load packages
 73 | library("TreeAndLeaf")
 74 | library("RedeR")
 75 | library("igraph")
 76 | library("RColorBrewer")
 77 | ```
 78 | 
 79 | ```{r, eval=TRUE, message=FALSE}
 80 | #-- Check data
 81 | dim(USArrests)
 82 | head(USArrests)
 83 | ```
 84 | 
 85 | ## Building a dendrogram example
 86 | 
 87 | In order to build a dendrogram from the `USArrests` dataset, we need a distance 
 88 | matrix. We will use the default "euclidean distance" method from the `dist()` 
 89 | function, and then the "average" method from `hclust()` function to create the 
 90 | dendrogram.
 91 | 
 92 | ```{r, eval=TRUE, message=FALSE}
 93 | hc <- hclust(dist(USArrests), "ave")
 94 | plot(hc, main="Dendrogram for the 'USArrests' dataset",
 95 |      xlab="", sub="")
 96 | ```
 97 | 
 98 | ## Converting the *hclust* object into a *tree-and-leaf* object
 99 | 
100 | The `treeAndLeaf` function will transform the *hclust* into an *igraph* object,
101 | including some basic graph attributes to display in the **RedeR** application.
102 | 
103 | ```{r, eval=FALSE}
104 | #-- Convert the 'hclust' object into a 'tree-and-leaf' object
105 | tal <- treeAndLeaf(hc)
106 | ```
107 | 
108 | ## Setting graph attributes
109 | 
110 | The `att.mapv()` function can be used to add external annotations to an *igraph* 
111 | object, for example, mapping new variables to the graph vertices. We will add 
112 | all `USArrests` variables to the `tal` object. To map one object to another 
113 | it is essential to use the same mapping IDs, set by the `refcol` parameter, 
114 | which points to a column in the input annotation dataset. In this example, 
115 | `refcol = 0` indicates that the `USArrests` rownames will be used as 
116 | mapping IDs. To check the IDs in the *igraph* vertices, please type 
117 | `V(tal)$name` in the R console.
118 | 
119 | ```{r, eval=FALSE}
120 | #--- Map attributes to the tree-and-leaf
121 | #Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
122 | tal <- att.mapv(g = tal, dat = USArrests, refcol = 0)
123 | ```
124 | 
125 | Now we use the `att.setv()` wrapper function to set attributes in the 
126 | *tree-and-leaf* diagram. To see all attributes available to display in the 
127 | **RedeR** application, please type `?addGraph` in the R console. The graph
128 | attributes can also be customized following **igraph** syntax rules.
129 | 
130 | ```{r, eval=FALSE}
131 | #--- Set graph attributes using the 'att.setv' wrapper function
132 | pal <- brewer.pal(9, "Reds")
133 | tal <- att.setv(g = tal, from = "Murder", to = "nodeColor", 
134 |                 cols = pal, nquant = 5)
135 | tal <- att.setv(g = tal, from = "UrbanPop", to = "nodeSize",
136 |                 xlim = c(10, 50, 5), nquant = 5)
137 | 
138 | #--- Set graph attributes using 'att.addv' and 'att.adde' functions
139 | tal <- att.addv(tal, "nodeFontSize", value = 15, index = V(tal)$isLeaf)
140 | tal <- att.adde(tal, "edgeWidth", value = 3)
141 | ```
142 | 
143 | ## Plotting a *tree-and-leaf* diagram
144 | 
145 | The next steps will call the **RedeR** application, and then display the 
146 | *tree-and-leaf* diagram in an interactive R/Java interface. The initial layout
147 | will show an unrooted tree diagram, which will be subsequently relaxed by a 
148 | force-directed algorithm applied to the terminal nodes of the tree.
149 | 
150 | ```{r, eval=FALSE}
151 | #--- Call RedeR application
152 | rdp <- RedPort()
153 | calld(rdp)
154 | resetd(rdp)
155 | ```
156 | 
157 | ```{r, eval=FALSE}
158 | #--- Send the tree-and-leaf to the interactive R/Java interface
159 | addGraph(obj = rdp, g = tal, gzoom=75)
160 | 
161 | #--- Call 'relax' to fine-tune the leaf nodes
162 | relax(rdp, p1=25, p2=200, p3=5, p5=5, ps=TRUE)
163 | ```
164 | 
165 | At this point, the user can interact with the layout process to achieve the 
166 | best or desired layout; we suggest fine-tuning the force-directed algorithm 
167 | parameters, either through the R/Java interface or the command line relaxation 
168 | function. Note that the unroot tree diagram represents the initial state; then 
169 | a relaxing process should start until the finest graph layout is achieved. 
170 | The final layout varies depending on the initial state, which can also be 
171 | adjusted by providing more or less room for the spatial configuration 
172 | (*e.g.* via `gzoom` parameter).
173 | 
174 | ```{r, eval=FALSE}
175 | #--- Add legends
176 | addLegend.color(obj = rdp, tal, title = "Murder Rate", 
177 |                 position = "topright")
178 | addLegend.size(obj = rdp, tal, title = "Urban Population Size",
179 |                position = "bottomright")
180 | ```
181 | 
182 | <p align="center">
183 | <img height="300" src="USAReds.png">
184 | </p>
185 | 
186 | # Setting the initial *tree-and-leaf* state with *ggtree* layouts
187 | 
188 | The tree diagram represents the initial state of a *tree-and-leaf*, which is then 
189 | relaxed by a force-directed algorithm applied to the terminal nodes. Therefore,
190 | the final *tree-and-leaf* layout varies depending on the initial state. The
191 | **treeAndLeaf** package also accepts `ggtree` layouts to setup the initial state. 
192 | For example, next we show a tree diagram generated by the **ggtree** package,
193 | and then we apply the *tree-and-leaf* transformation.
194 | 
195 | ## Package and data requirements
196 | 
197 | ```{r, eval=FALSE, message=FALSE}
198 | #-- Libraries required in this section:
199 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
200 | # BiocManager::install(c("TreeAndLeaf","RedeR","ggtree))
201 | # install.packages(c("igraph","ape", "dendextend", "dplyr", 
202 | #                    "ggplot2", "RColorBrewer"))
203 | 
204 | #-- Load packages
205 | library("TreeAndLeaf")
206 | library("RedeR")
207 | library("igraph")
208 | library("ape")
209 | library("ggtree")
210 | library("dendextend")
211 | library("dplyr")
212 | library("ggplot2")
213 | library("RColorBrewer")
214 | ```
215 | 
216 | ## Building and plotting a *phylo* tree with *ggtree* layouts
217 | 
218 | ```{r, eval=FALSE}
219 | #--- Generate a random phylo tree
220 | phylo_tree <- rcoal(300)
221 | 
222 | #--- Set groups and node sizes
223 | group <- size <- dendextend::cutree(phylo_tree, 10)
224 | group[] <- LETTERS[1:10][group]
225 | size[] <- sample(size)
226 | group.df <- data.frame(label=names(group), group=group, size=size)
227 | phylo_tree <- dplyr::full_join(phylo_tree, group.df, by='label')
228 | 
229 | #--- Generate a ggtree with 'daylight' layout
230 | pal <- brewer.pal(10, "Set3")
231 | ggt <- ggtree(phylo_tree, layout = 'daylight', branch.length='none')
232 | 
233 | #--- Plot the ggtree
234 | ggt + geom_tippoint(aes(color=group, size=size)) +
235 |   scale_color_manual(values=pal) + scale_y_reverse()
236 | ```
237 | 
238 | ## Applying *tree-and-leaf* transformation to *ggtree* layouts
239 | 
240 | ```{r, eval=FALSE}
241 | #-- Convert the 'ggtree' object into a 'tree-and-leaf' object
242 | tal <- treeAndLeaf(ggt)
243 | 
244 | #--- Map attributes to the tree-and-leaf
245 | #Note: 'refcol = 1' indicates that 'dat' col 1 will be used as mapping IDs
246 | tal <- att.mapv(g = tal, dat = group.df, refcol = 1)
247 | 
248 | #--- Set graph attributes using the 'att.setv' wrapper function
249 | tal <- att.setv(g = tal, from = "group", to = "nodeColor",
250 |                 cols = pal)
251 | tal <- att.setv(g = tal, from = "size", to = "nodeSize",
252 |                 xlim = c(10, 50, 5))
253 | 
254 | #--- Set graph attributes using 'att.addv' and 'att.adde' functions
255 | tal <- att.addv(tal, "nodeFontSize", value = 1)
256 | tal <- att.addv(tal, "nodeLineWidth", value = 0)
257 | tal <- att.addv(tal, "nodeColor", value = "black", index=!V(tal)$isLeaf)
258 | tal <- att.adde(tal, "edgeWidth", value = 3)
259 | tal <- att.adde(tal, "edgeColor", value = "black")
260 | ```
261 | 
262 | ```{r, eval=FALSE}
263 | #--- Call RedeR application
264 | rdp <- RedPort()
265 | calld(rdp)
266 | resetd(rdp)
267 | ```
268 | 
269 | ```{r, eval=FALSE}
270 | #--- Send the tree-and-leaf to the interactive R/Java interface
271 | addGraph(obj = rdp, g = tal, gzoom=50)
272 | 
273 | #--- Select inner nodes, preventing them from relaxing
274 | selectNodes(rdp, V(tal)$name[!V(tal)$isLeaf], anchor=TRUE)
275 | 
276 | #--- Call 'relax' to fine-tune the leaf nodes
277 | relax(rdp, p1=25, p2=100, p3=5, p5=1, p8=5, ps=TRUE)
278 | 
279 | #--- Add legends
280 | addLegend.color(obj = rdp, tal, title = "Group", 
281 |                 position = "topright",vertical=T)
282 | addLegend.size(obj = rdp, tal, title = "Size", 
283 |                position = "topleft", 
284 |                vertical=T, dxtitle=10)
285 | ```
286 | 
287 | <p align="center">
288 | <img height="350" src="ggtree_tal.png">
289 | </p>
290 | 
291 | # Case Study 1: visualizing a large dendrogram
292 | 
293 | ## Context
294 | 
295 | This section follows the same steps described in the *Quick Start*, but 
296 | using a larger dendrogram derived from the R built-in `quakes` dataset. 
297 | The `quakes` is a dataframe available in the user's workspace. To know more 
298 | about this dataframe, please query `?quakes` in the R console. 
299 | We will build a dendrogram from the `quakes` dataset, then transform the 
300 | dendrogram into a *tree-and-leaf* diagram, and the result will be visualized 
301 | in the **RedeR** application.
302 | 
303 | ## Package and data requirements
304 | 
305 | ```{r, eval=FALSE, message=FALSE}
306 | #-- Libraries required in this section:
307 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
308 | # BiocManager::install(c("TreeAndLeaf","RedeR"))
309 | # install.packages(c("igraph", "RColorBrewer"))
310 | 
311 | #-- Load packages
312 | library(TreeAndLeaf)
313 | library(RedeR)
314 | library(igraph)
315 | library(RColorBrewer)
316 | ```
317 | 
318 | ```{r echo=TRUE}
319 | #-- Check data
320 | dim(quakes)
321 | head(quakes)
322 | ```
323 | 
324 | ```{r, eval=TRUE, message=FALSE}
325 | #-- Building a large dendrogram
326 | hc <- hclust(dist(quakes), "ave")
327 | plot(hc, main="Dendrogram for the 'quakes' dataset",
328 |      xlab="", sub="")
329 | ```
330 | 
331 | ## Building and plotting a large *tree-and-leaf* diagram
332 | 
333 | ```{r, eval=FALSE}
334 | #-- Convert the 'hclust' object into a 'tree-and-leaf' object
335 | tal <- treeAndLeaf(hc)
336 | ```
337 | 
338 | ```{r, eval=FALSE}
339 | #--- Map attributes to the tree-and-leaf
340 | #Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
341 | tal <- att.mapv(tal, quakes, refcol = 0)
342 | 
343 | #--- Set graph attributes using the 'att.setv' wrapper function
344 | pal <- brewer.pal(9, "Greens")
345 | tal <- att.setv(g = tal, from = "mag", to = "nodeColor",
346 |                 cols = pal, nquant = 10)
347 | tal <- att.setv(g = tal, from = "depth", to = "nodeSize",
348 |                 xlim = c(40, 120, 20), nquant = 5)
349 | 
350 | #--- Set graph attributes using 'att.addv' and 'att.adde' functions
351 | tal <- att.addv(tal, "nodeFontSize", value = 1)
352 | tal <- att.adde(tal, "edgeWidth", value = 10)
353 | ```
354 | 
355 | The next steps will call the **RedeR** application, and then display the 
356 | *tree-and-leaf* diagram in an interactive R/Java interface. The initial layout
357 | will show an unrooted tree diagram, which will be subsequently relaxed by a 
358 | force-directed algorithm applied to the terminal nodes of the tree.
359 | 
360 | ```{r, eval=FALSE}
361 | #--- Call RedeR application
362 | rdp <- RedPort()
363 | calld(rdp)
364 | resetd(rdp)
365 | ```
366 | 
367 | ```{r, eval=FALSE}
368 | #--- Send the tree-and-leaf to the interactive R/Java interface
369 | addGraph(obj = rdp, g = tal, gzoom=10)
370 | 
371 | #--- Call 'relax' to fine-tune the leaf nodes
372 | relax(rdp, p1=25, p2=200, p3=10, p4=100, p5=10, ps=TRUE)
373 | ```
374 | 
375 | ```{r, eval=FALSE}
376 | #--- Add legends
377 | addLegend.color(obj = rdp, tal, title = "Richter Magnitude",
378 |                 position = "bottomright")
379 | addLegend.size(obj = rdp, tal, title = "Depth (km)")
380 | ```
381 | 
382 | <p align="center">
383 | <img height="400" src="QuakesTree.png">
384 | </p>
385 | 
386 | # Case Study 2: visualizing a phylogenetic tree
387 | 
388 | ## Context
389 | 
390 | This section generates a *tree-and-leaf* diagram from a pre-computed `phylo` 
391 | tree object. We will use a phylogenetic tree listing 121 eukaryotes, available 
392 | from the **geneplast** package.
393 | 
394 | ## Package and data requirements
395 | 
396 | ```{r, eval=TRUE, message=FALSE}
397 | #-- Libraries required in this section:
398 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
399 | # BiocManager::install(c("TreeAndLeaf","RedeR","geneplast))
400 | # install.packages(c("igraph","ape", "RColorBrewer"))
401 | 
402 | #-- Load packages
403 | library(TreeAndLeaf)
404 | library(RedeR)
405 | library(igraph)
406 | library(ape)
407 | library(geneplast)
408 | library(RColorBrewer)
409 | ```
410 | 
411 | ```{r, eval=TRUE, message=FALSE}
412 | #-- Load data and plot the phylogenetic tree
413 | data("spdata")
414 | data("gpdata.gs")
415 | plot(phyloTree)
416 | ```
417 | 
418 | ## Building and plotting a *tree-and-leaf* from a phylogenetic tree
419 | 
420 | ```{r, eval=FALSE}
421 | #--- Drop organisms not listed in the 'spdata' annotation
422 | phyloTree$tip.label <- as.character(phyloTree$tip.label)
423 | tokeep <- phyloTree$tip.label %in% spdata$tax_id
424 | pruned.phylo <- drop.tip(phyloTree, phyloTree$tip.label[!tokeep])
425 | ```
426 | 
427 | ```{r, eval=FALSE}
428 | #-- Convert the phylogenetic tree into a 'tree-and-leaf' object
429 | tal <- treeAndLeaf(pruned.phylo)
430 | 
431 | #--- Map attributes to the tree-and-leaf
432 | #Note: 'refcol = 1' indicates that 'dat' col 1 will be used as mapping IDs
433 | tal <- att.mapv(g = tal, dat = spdata, refcol = 1)
434 | 
435 | #--- Set graph attributes using the 'att.setv' wrapper function
436 | pal <- brewer.pal(9, "Purples")
437 | tal <- att.setv(g = tal, from = "genome_size_Mb",
438 |                 to = "nodeSize", xlim = c(120, 250, 1), nquant = 5)
439 | tal <- att.setv (g = tal, from = "proteins",
440 |                  to = "nodeColor", nquant = 5,
441 |                  cols = pal, na.col = "black")
442 | ```
443 | 
444 | ```{r, eval=FALSE}
445 | #--- Add graph attributes using 'att.adde' and 'att.addv' functions
446 | tal <- att.addv(tal, "nodeFontSize", value = 10)
447 | tal <- att.adde(tal, "edgeWidth", value = 20)
448 | 
449 | # Set species names to 'nodeAlias' attribute
450 | tal <- att.setv(tal, from = "sp_name", to = "nodeAlias")
451 | 
452 | # Select a few names to highlight in the graph
453 | tal <- att.addv(tal, "nodeFontSize", value = 100, 
454 |        filter=list('name'=sample(pruned.phylo$tip.label,30)))
455 | tal <- att.addv(tal, "nodeFontSize", value = 100, 
456 |                 filter=list('name'="9606")) #Homo sapiens
457 | ```
458 | 
459 | ```{r, eval=FALSE}
460 | # Call RedeR
461 | rdp <- RedPort()
462 | calld(rdp)
463 | resetd(rdp)
464 | 
465 | #--- Send the tree-and-leaf to the interactive R/Java interface
466 | addGraph(obj = rdp, g = tal, gzoom=10)
467 | 
468 | #--- Call 'relax' to fine-tune the leaf nodes
469 | relax(rdp, ps=TRUE)
470 | ```
471 | 
472 | ```{r, eval=FALSE}
473 | #--- Add legends
474 | addLegend.color(rdp, tal, title = "Proteome Size (n)")
475 | addLegend.size(rdp, tal, title = "Genome Size (Mb)")
476 | ```
477 | 
478 | <p align="center">
479 | <img height="400" src="PhyloPurple.png">
480 | </p>
481 | 
482 | # Case Study 3: visualizing a non-binary tree
483 | 
484 | ## Context
485 | 
486 | The **TreeAndLeaf** package is designed to layout binary trees, but it can also
487 | layout other graph configurations. To exemplify this case, we will use a 
488 | larger phylogenetic tree available from the **geneplast** package, and for 
489 | which some inner nodes have more than two children, or non-binary nodes.
490 | 
491 | ## Package and data requirements
492 | 
493 | ```{r, eval=FALSE}
494 | #-- Libraries required in this section:
495 | #-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
496 | # BiocManager::install(c("TreeAndLeaf","RedeR","geneplast))
497 | # install.packages(c("igraph","ape", "RColorBrewer"))
498 | 
499 | #-- Load packages
500 | library(TreeAndLeaf)
501 | library(RedeR)
502 | library(igraph)
503 | library(ape)
504 | library(geneplast)
505 | library(RColorBrewer)
506 | ```
507 | 
508 | ```{r, eval=FALSE, message=FALSE}
509 | #-- Load data 
510 | data("spdata")
511 | data("phylo_tree")
512 | ```
513 | 
514 | ```{r, eval=FALSE, message=FALSE}
515 | #--- Drop organisms not listed in the 'spdata' annotation
516 | tokeep <- phylo_tree$tip.label %in% spdata$tax_id
517 | pruned.phylo <- drop.tip(phylo_tree, phylo_tree$tip.label[!tokeep])
518 | ```
519 | 
520 | ## Building and plotting a *tree-and-leaf* for a non-binary tree
521 | 
522 | ```{r, eval=FALSE}
523 | #-- Convert the phylogenetic tree into a 'tree-and-leaf' object
524 | tal <- treeAndLeaf(pruned.phylo)
525 | ```
526 | 
527 | ```{r, eval=FALSE}
528 | #--- Map attributes to the tree-and-leaf using "%>%" operator
529 | tal <- tal %>%
530 |   att.mapv(dat = spdata, refcol = 1) %>%
531 |   att.setv(from = "genome_size_Mb", to = "nodeSize",
532 |            xlim = c(120, 250, 1), nquant = 5) %>%
533 |   att.setv(from = "proteins", to = "nodeColor", nquant = 5,
534 |            cols = brewer.pal(9, "Blues"), na.col = "black") %>%
535 |   att.setv(from = "sp_name", to = "nodeAlias") %>%
536 |   att.adde(to = "edgeWidth", value = 20) %>%
537 |   att.addv(to = "nodeFontSize", value = 10) %>%
538 |   att.addv(to = "nodeFontSize", value = 100,
539 |       filter = list("name" = sample(pruned.phylo$tip.label, 30))) %>%
540 |   att.addv(to = "nodeFontSize", value = 100, 
541 |            filter = list("name" = "9606"))
542 | ```
543 | 
544 | ```{r, eval=FALSE}
545 | # Call RedeR
546 | rdp <- RedPort()
547 | calld(rdp)
548 | resetd(rdp)
549 | 
550 | #--- Send the tree-and-leaf to the interactive R/Java interface
551 | addGraph(obj = rdp, g = tal, gzoom=5)
552 | 
553 | #--- Call 'relax' to fine-tune the leaf nodes
554 | relax(rdp, ps=TRUE)
555 | ```
556 | 
557 | ```{r, eval=FALSE}
558 | #--- Add legends
559 | addLegend.color(rdp, tal, title = "Proteome Size (n)")
560 | addLegend.size(rdp, tal, title = "Genome size (Mb)")
561 | ```
562 | 
563 | <p align="center">
564 | <img width="700" src="nBinPhylo.png">
565 | </p>
566 | 
567 | # Session information
568 | 
569 | ```{r label='Session information', eval=TRUE, echo=FALSE}
570 | sessionInfo()
571 | ```
572 | 


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