RedeR: bridging network analysis and visualization

Mauro A.A. Castro, Xin Wang, Michael N.C. Fletcher, Kerstin B. Meyer, and Florian Markowetz.

11 March 2024

Abstract

RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of nested networks. Graph, node, and edge attributes are set either using graphical or command-line methods following igraph syntax rules. The RedeR/R package sets all details to initialize the R-to-Java interface, which uses an interactive force-directed layout algorithm, adjusting the graph layout to the hierarchical levels of a nested network. Supporting information is available from Castro et al. (2016).

Package

RedeR 2.6.1

1 Overview

RedeR is an R-based package combined with a Java application for network analysis and visualization. The design of the software is depicted in Figure 1. RedeR is designed to deal with three key challenges in network analysis. Firstly, biological networks are modular and hierarchical, so network visualization needs to take advantage of such structural features. Secondly, network analysis relies on statistical methods, many of which are already available in resources like CRAN or Bioconductor. Thirdly, in larger networks user input is needed to focus the view of the network on the biologically relevant parts, rather than relying on an automatic layout function.

Schematic representation of RedeR calls. Complex graphs with many attributes can be transferred from-and-to R using *addGraph()* and *getGraph()* functions.

Figure 1: Schematic representation of RedeR calls
Complex graphs with many attributes can be transferred from-and-to R using addGraph() and getGraph() functions.

2 Quick start

#--- Load required packages
library("RedeR")
library("igraph")

2.1 Initializing the interface

The RedeR/R package sets all details to initialize the R-to-Java interface. Next, the RedPort() constructor will create the rdp object, which will be used in all methods calling the RedeR/Java application.

#--- Set the R-to-Java interface
rdp <- RedPort()

Note that RedeR >=2.0.0 is designed to run on Java>=11 (see system requirements). Next, the calld() function will use the rdp object to launch the RedeR application (Figure 2), initializing the R-to-Java interface:

#--- Initialize the R-to-Java interface
calld(rdp)

RedeR's graphical user interface. The calld() function initializes the R-to-Java interface. RedeR >=2.0.0 will need the Java Runtime Environment (JRE) version 11 or higher (Java >=11). The package will also run on earlier Java versions (Java >=8), but with reduced functionality. In order to check the Java on your system, see the System Requirements section.

Figure 2: RedeR’s graphical user interface
The calld() function initializes the R-to-Java interface. RedeR >=2.0.0 will need the Java Runtime Environment (JRE) version 11 or higher (Java >=11). The package will also run on earlier Java versions (Java >=8), but with reduced functionality. In order to check the Java on your system, see the System Requirements section.

2.2 Displaying graphs

The addGraph() method displays igraph graphs in the RedeR application. The following snippet will display the g1 graph in RedeR, using the layout_with_kk() function to set the network layout (Figure 3):

#--- Add a graph
g1 <- graph.lattice(c(3,3,3))
addGraph(rdp, g=g1, layout=layout_with_kk(g1))

Figure 3: A toy example generated by the addGraph() function

In the reverse process, graphs are transferred from the application to R using the getGraph() method:

#--- Get a graph
g2 <- getGraph(rdp)
summary(g2)
# IGRAPH f22cf52 UN-- 125 300 -- 
# + attr: zoom (g/n), name (v/c), coordX (v/n), coordY (v/n)

#--- Reset the application:
resetd(rdp)

2.3 Working with containers

The addGraph() method is also used to nest graphs into containers when setting isNested = TRUE. Next, the graphs g1 and g2 are nested into containers and then displayed in the RedeR application.

#--- Generate two scale-free graphs
g1 <- sample_pa(7)
g2 <- sample_pa(5)

#--- Set names to graph vertices
V(g1)$name <- paste("m",1:7,sep="")
V(g2)$name <- paste("n",1:5,sep="")

#--- Nest graphs into containers
addGraph(rdp, g=g1, isNested=TRUE, gcoord=c(30,30), gscale=50)
addGraph(rdp, g=g2, isNested=TRUE, gcoord=c(70,70), gscale=50)

Figure 4: Nested graphs in RedeR using the addGraph() function

In this case, g1 and g2 are nested into containers N1 and N2, respectively (Figure 4). Each subgraph will retain the network structure, allowing access to individual nodes and edges in subsequent jobs. For example, the following snippet selects all nodes in container N2 and then retrieves the corresponding subgraph.

#--- Select nodes within a container 
selectNodes(rdp, nodes="N2")

#--- Get selected nodes
getGraph(rdp, status="selected")
# IGRAPH 36b1f75 UN-- 5 4 -- 
# + attr: zoom (g/n), name (v/c), coordX (v/n), coordY (v/n)
# + edges from 36b1f75 (vertex names):
# [1] n1--n2 n1--n3 n1--n4 n4--n5

#--- Reset the application:
resetd(rdp)

2.4 Interactive layout

The following snippet generates a scale-free graph using igraph’s sample_pa() function and then displays the network in RedeR.

#--- Generate a larger scale-free graph
g1 <- sample_pa(300, directed=FALSE)

#--- Set names to igraph vertices
V(g1)$name <- paste0("V",1:vcount(g1))

#--- Check attributes in the 'g1' graph
summary(g1)

## IGRAPH 36c9dc9 UN-- 300 299 -- Barabasi graph
## + attr: name (g/c), power (g/n), m (g/n), zero.appeal (g/n), algorithm
## | (g/c), name (v/c)

#--- Send the 'g1' graph to RedeR
addGraph(rdp, g=g1, gzoom=50)

Next, the relax() function starts RedeR’s interactive force-directed layout algorithm, which will arrange the network as in Figure 5A. The layout algorithm has 9 parameters (p1 to p9), set either in the relax() function or in the interactive application. These parameters control the layout algorithm by adjusting the relaxing process to the hierarchical levels of the network.

#--- Run RedeR's interactive layout
relax(rdp, p1=20, p2=150, p3=20, p4=100, p5=10)

Graph layouts set by *RedeR*'s interactive force-directed algorithm. A) A scale-free graph generated by igraph's *sample_pa()* function. B,C) Examples of flat and nested networks available in the *RedeR* application.

Figure 5: Graph layouts set by RedeR’s interactive force-directed algorithm
A) A scale-free graph generated by igraph’s sample_pa() function. B,C) Examples of flat and nested networks available in the RedeR application.

#--- Reset the application:
resetd(rdp)

3 Command-line attributes

RedeR attributes can be set either using the graphical user interface or the command-line interface. When using command-line attributes, these must follow igraph syntax rules and valid RedeR’s attribute names. Graph attributes are set directly on a graph using igraph shortcuts, while node and edge attributes are set using igraph’s V() and E() functions, respectivelly. For example:

#--- Set a new graph attribute in 'g1'
g1$bgColor <- 'grey90'

#--- Set new node attributes in 'g1'
V(g1)$nodeColor <- "skyblue"
V(g1)$nodeSize <- 50

#--- Set new edge attributes in 'g1'
E(g1)$edgeColor <- "skyblue"
E(g1)$edgeWidth <- 10

#--- Check the new attributes in 'g1'
summary(g1)

## IGRAPH 36c9dc9 UN-- 300 299 -- Barabasi graph
## + attr: name (g/c), power (g/n), m (g/n), zero.appeal (g/n), algorithm
## | (g/c), bgColor (g/c), name (v/c), nodeColor (v/c), nodeSize (v/n),
## | edgeColor (e/c), edgeWidth (e/n)

Tables 1, 2, and 3 list all command-line attributes available for the current version (RedeR 2.6.1), including usage examples.

Table 1: **Graph attributes**. Examples of how to set *RedeR*’s graph attributes using *igraph* shortcuts.
RedeR attribute	Description	Value	Usage example
bgColor	Background color of the app panel	Hexadecimal or color name	g$bgColor <- ‘white’
zoom	Zoom scale applyed to graph objects and area	Numeric, 0-100	g$zoom <- 100
gzoom	Zoom scale applyed to graph objects	Numeric, 0-100	g$gzoom <- NULL
gscale	Expansion factor applyed to the graph area	Numeric, 0-100	g$gscale <- 100
isNested	Whether to nest nodes into a container	Logical	g$isNested <- FALSE
isAnchored	Whether to anchor the container	Logical	g$isAnchored <- FALSE
isAssigned	Whether to assign container name to nodes	Logical	g$isAssigned <- FALSE
nestAlias	Label of the node nesting container	Logical	g$nestAlias <- ‘a name’
nestSize	Size of the node nesting container	Numeric, >=0	g$nestSize <- 500
nestShape	Shape of the node nesting container	String: ELLIPSE, RECTANGLE, ROUNDED_RECTANGLE, TRIANGLE, DIAMOND	g$nestShape <- ‘ELLIPSE’
nestColor	Color of the node nesting container	Hexadecimal or color name	g$nestColor <- ‘grey’
nestFontSize	Container label font size	Numeric, >=0	g$nestFontSize <- 18
nestLineWidth	Container line width	Numeric, >=0	g$nestLineWidth <- 2
nestLineColor	Container line color	Hexadecimal or color name	g$nestLineColor <- ‘grey’
nestLineType	Container line type	String: SOLID, DOTTED, DASHED, LONG_DASH	g$nestLineType <- ‘SOLID’
nestImage	Status of the container on the screen	String: plain, transparent, or hide	g$nestImage <- ‘plain’

Table 2: **Node attributes**. Examples of how to set *RedeR*’s node attributes using *igraph* shortcuts.
RedeR attribute	Description	Value	Usage example
name	Node name	String, a unique ID	V(g)$name <- paste0(‘Node’,1:vcount(g))
nodeAlias	Node alias	String, a label	V(g)$nodeAlias <- V(g)$name
coordX	X-coordinate of a point in a plane	Numeric, (-Inf,Inf)	V(g)$coordX <- runif(vcount(g))
coordY	Y-coordinate of a point in a plane	Numeric, (-Inf,Inf)	V(g)$coordY <- runif(vcount(g))
nodeSize	Node size	Numeric, >=0	V(g)$nodeSize <- 20
nodeShape	Node shape	String: ELLIPSE, RECTANGLE, ROUNDED_RECTANGLE, TRIANGLE, DIAMOND	V(g)$nodeShape <- ‘ELLIPSE’
nodeColor	Node color	Hexadecimal or color name	V(g)$nodeColor <- ‘white’
nodeLineWidth	Line width	Numeric, >=0	V(g)$nodeLineWidth <- 1
nodeLineColor	Line color	Hexadecimal or color name	V(g)$nodeLineColor <- ‘grey’
nodeFontSize	Font size	Numeric, >=0	V(g)$nodeFontSize <- 12
nodeFontColor	Font color	Hexadecimal or color name	V(g)$nodeFontColor <- ‘black’
nodeBend	Node bend	Numeric, 0-100	V(g)$nodeBend <- 50
nodeWeight	Node weight (not implemented)	Numeric, >=0	V(g)$nodeWeight <- 0

Table 3: **Edge attributes**. Examples of how to set *RedeR*’s edge attributes using *igraph* shortcuts.
RedeR attribute	Description	Value	Usage example
edgeWeight	Edge weight	Numeric, >=0	E(g)$edgeWeight <- 0
edgeWidth	Edge width	Numeric, >=0	E(g)$edgeWidth <- 1
edgeColor	Edge color	Hexadecimal or color name	E(g)$edgeColor <- ‘grey’
edgeType	Edge type	String: SOLID, DOTTED, DASHED, LONG_DASH	E(g)$edgeType <- ‘SOLID’
arrowLength	Arrow length	Numeric, >=0	E(g)$arrowLength <- 15
arrowAngle	Arrowhead angle in degrees	Numeric, 10-75	E(g)$arrowAngle <- 20
arrowType	Associations in directed graphs	Integer: -1, 0, 1	E(g)$arrowType <- 1
arrowDirection	Associations in undirected graphs	Integer: 0 (A-B), 1 (A-> B), -1 (A-\| B), 2 (A <-B), -2 (A \|-B), 3 (A <-> B), -3 (A \|-\| B), 4 (A \|-> B), -4 (A <-\| B)	E(g)$arrowDirection <- 0
linkType	Assignment type between nodes and containers	String: nested, notnested	E(g)$linkType <- ‘nested’

RedeR provides two wrapper functions to add fixed values to igraph graphs. The att.addv() function adds a new attribute with a fixed value to all nodes or selected nodes, while att.adde() function adds a new attribute with a fixed value to all edges. These functions will require that the vertices are named.

#--- Make sure vertices are named!
V(g1)$name[1:5]

## [1] "V1" "V2" "V3" "V4" "V5"

#--- Add 'nodeFontSize' attribute from a fixed value
g1 <-  att.addv(g1, to = "nodeFontSize", value = 20)

#--- Same as above, but applied only to three nodes
g1 <- att.addv(g1, to = "nodeFontSize", value = 70, 
              filter = list("name" = V(g1)$name[1:3]))

#--- Add 'edgeType' attribute from a fixed value
g1 <-  att.adde(g1, to = "edgeType", value = "DOTTED")

#--- Check the new attributes added to 'g1'
summary(g1)

## IGRAPH 36c9dc9 UN-- 300 299 -- Barabasi graph
## + attr: name (g/c), power (g/n), m (g/n), zero.appeal (g/n), algorithm
## | (g/c), bgColor (g/c), name (v/c), nodeColor (v/c), nodeSize (v/n),
## | nodeFontSize (v/n), edgeColor (e/c), edgeWidth (e/n), edgeType (e/c)

Alternatively, RedeR’s attributes can be set using the att.mapv(), att.mape(),att.setv(), and att.sete() wrapper functions. The att.mapv() and att.mape() will map variables from a data frame to igraph vertices and edges, respectively, while the att.setv() and att.sete() will transform these variables into valid attribute types. Next, to demonstrate these functions, load an igraph graph and a data.frame object with compatible identifiers.

#--- Load an igraph graph and a data frame with compatible identifiers
data(ER.deg)
g2 <- ER.deg$ceg # an igraph graph with named nodes
df <- ER.deg$dat # a data frame

#--- Check attributes already available in the 'g2' graph
summary(g2)

## IGRAPH fd53520 UNW- 174 1016 -- 
## + attr: name (v/c), weight (e/n)

#--- Check colnames in the 'df' data frame
colnames(df)

##  [1] "ENTREZ"      "Symbol"      "logFC.t3"    "logFC.t6"    "logFC.t12"  
##  [6] "p.value.t3"  "p.value.t6"  "p.value.t12" "degenes.t3"  "degenes.t6" 
## [11] "degenes.t12" "ERbdist"

#--- Map 'df' to 'g2' using the att.mapv() function
#Note: 'refcol = 0' indicates that 'df' rownames will be used as mapping IDs
g2 <- att.mapv(g=g2, dat=df, refcol=0)

#--- Check the new attributes mapped to 'g2'
summary(g2)

## IGRAPH fd53520 UNW- 174 1016 -- 
## + attr: name (v/c), ENTREZ (v/c), Symbol (v/c), logFC.t3 (v/n),
## | logFC.t6 (v/n), logFC.t12 (v/n), p.value.t3 (v/n), p.value.t6 (v/n),
## | p.value.t12 (v/n), degenes.t3 (v/n), degenes.t6 (v/n), degenes.t12
## | (v/n), ERbdist (v/n), weight (e/n)

Note that new names were included in the g2 graph, but these names are not valid RedeR’s attributes yet. Next, the att.setv() and att.sete() functions are used to transform different data types into valid attributes.

# Set 'nodeAlias' from 'Symbol'
g2 <- att.setv(g2, from="Symbol", to="nodeAlias")

# Set 'nodeColor' from 'logFC.t3'
g2 <- att.setv(g2, from="logFC.t3", to="nodeColor", breaks=seq(-2,2,0.4), pal=2)

# Set 'nodeSize' from 'ERbdist'
g2 <- att.setv(g2, from="ERbdist", to="nodeSize", nquant=10, xlim=c(20,100,1))

# Set 'nodeFontSize' from 'ERbdist'
g2 <- att.setv(g2, from="ERbdist", to="nodeFontSize", nquant=10, xlim=c(1,50,1))

# Set 'edgeWidth' from 'weight'
g2 <- att.sete(g2, from="weight", to="edgeWidth", nquant=5, xlim=c(1,10,1))

#--- Check the new attributes set in 'g2'
summary(g2)

## IGRAPH fd53520 UNW- 174 1016 -- 
## + attr: legNodeColor (g/x), legNodeSize (g/x), legNodeFontSize (g/x),
## | legEdgeWidth (g/x), name (v/c), ENTREZ (v/c), Symbol (v/c), logFC.t3
## | (v/n), logFC.t6 (v/n), logFC.t12 (v/n), p.value.t3 (v/n), p.value.t6
## | (v/n), p.value.t12 (v/n), degenes.t3 (v/n), degenes.t6 (v/n),
## | degenes.t12 (v/n), ERbdist (v/n), nodeAlias (v/c), nodeColor (v/c),
## | nodeSize (v/n), nodeFontSize (v/n), weight (e/n), edgeWidth (e/n)

4 Workflow examples

This section provides some practical examples of how users might integrate its own pre-processed data into a graph visualization workflow. Please refer to Castro et al. (2016) and Cardoso et al. (2021) for more details about the biological background and experimental design of each example.

4.1 Subgraphs

Start the RedeR application (i.e. run the calld() function), and then load the ER.limma and hs.inter datasets. The ER.limma is a data frame with results from a time-course differential expression analysis, listing differentially expressed (DE) genes from estrogen-treated MCF-7 cells for 0, 3, 6, and 12 hours (contrasts: t3-t0, t6-t0, and t12-t0). The hs.inter is an igraph graph derived from the Human Protein Reference Database (HPRD, release 9; https://www.hprd.org/). The next snippets list a step-by-step preparation of three nested subgraphs to display in the RedeR application.

#--- Load required packages
library("RedeR")
library("igraph")

#--- If not running, initialize the ReadeR application
# rdp <- RedPort()
# calld(rdp)
resetd(rdp)

#--- Load a dataframe and an interactome
data(ER.limma)
data(hs.inter)

Extract a subgraph from the hs.inter graph and set its attributes using the att.setv() function. This subgraph will include DE genes called in the t3-t0 contrast. Note that some genes are not listed in the hs.inter, and that’s okay.

#-- Extract a subgraph from the hs.inter graph
gt3  <- subg(g=hs.inter, dat=ER.limma[ER.limma$degenes.t3!=0,], refcol=1)

## ...not all genes found in the network!

#-- Set attributes
gt3  <- att.setv(g=gt3, from="Symbol", to="nodeAlias")
gt3  <- att.setv(g=gt3, from="logFC.t3", to="nodeColor", breaks=seq(-2,2,0.4), pal=2)

Extract another subgraph from the hs.inter graph, for DE genes in the t6-t0 contrast:

#--- Extract another subgraph from the hs.inter graph
gt6  <- subg(g=hs.inter, dat=ER.limma[ER.limma$degenes.t6!=0,], refcol=1)

## ...not all genes found in the network!

#--- Set attributes
gt6  <- att.setv(g=gt6, from="Symbol", to="nodeAlias")
gt6  <- att.setv(g=gt6, from="logFC.t6", to="nodeColor", breaks=seq(-2,2,0.4), pal=2)

Extract another subgraph from the hs.inter graph, for DE genes in the t12-t0 contrast:

#--- Extract another subgraph from the hs.inter graph
gt12 <- subg(g=hs.inter, dat=ER.limma[ER.limma$degenes.t12!=0,], refcol=1)

## ...not all genes found in the network!

#--- Set attributes
gt12 <- att.setv(g=gt12, from="Symbol", to="nodeAlias")
gt12 <- att.setv(g=gt12, from="logFC.t12", to="nodeColor", breaks=seq(-2,2,0.4), pal=2)

#--- Customize subgraph names
gt3$nestAlias <- "3h"
gt6$nestAlias <- "6h"
gt12$nestAlias <- "12h"

Now use the addGraph() function to send the three subgraphs to the RedeR application, nesting them into containers. This should start building the nested network depicted in Figure 6. The addGraph() function will return the container IDs, N1 to N5, which will be used to identify graph parents.

#--- Send nested subgraphs to the RedeR application
N1 <- addGraph(rdp, gt3, gcoord=c(10,25), gscale=20, isNested=TRUE, theme='tm1', gzoom=30)
N2 <- addGraph(rdp, gt6, gcoord=c(20,70), gscale=50, isNested=TRUE, theme='tm1', gzoom=30)
N3 <- addGraph(rdp, gt12, gcoord=c(70,55), gscale=80, isNested=TRUE, theme='tm1', gzoom=30)

… and use the nestNodes() function to indicate overlapping genes in the time series:

#--- Nest sub-subgraphs
N4 <- nestNodes(rdp, nodes=V(gt3)$name, parent=N2, theme='tm2')
N5 <- nestNodes(rdp, nodes=V(gt6)$name, parent=N3, theme='tm2')
nestNodes(rdp, nodes=V(gt3)$name, parent=N5, theme='tm3')

To simplify the graph, the mergeOutEdges() function can be used to assign edges to containers:

#--- Assign edges to containers
mergeOutEdges(rdp, nlevels=2)

…then telax the network:

relax(rdp, p1=100, p2=100, p3=5, p4=150, p5=5, p8=10, p9=20)

…and add a color legend:

scl <- gt3$legNodeColor$scale
leg <- gt3$legNodeColor$legend 
addLegend.color(rdp, colvec=scl, labvec=leg, title="Node color (logFC)")

Next, the selectNodes() function will zoom-in on the RET gene at different time points:

selectNodes(rdp,"RET")
#repeat this line to see RET in all graph instances

Nested subnetworks. This graph shows genes differentially expressed in estrogen-treated MCF-7 cells at 3, 6 and 12 h (relative to 0 h). The insets correspond to the overlap between consecutive time points. Adapted from Castro *et al.* (2012, https://doi.org/10.1186/gb-2012-13-4-r29).

Figure 6: Nested subnetworks
This graph shows genes differentially expressed in estrogen-treated MCF-7 cells at 3, 6 and 12 h (relative to 0 h). The insets correspond to the overlap between consecutive time points. Adapted from Castro et al. (2012, https://doi.org/10.1186/gb-2012-13-4-r29).

4.2 Nested networks

This example will show a step-by-step preparation of a hierarchical network to display in the RedeR application. Please, load the ER.deg dataset and then get the dat and ceg objects. The dat object is a data frame listing DE genes with log2 fold change statistics (logFC), while the ceg object is an igraph graph with a co-expression gene network.

#--- Load required packages
library("RedeR")
library("igraph")

#--- If not running, initialize the ReadeR application
# rdp <- RedPort()
# calld(rdp)
resetd(rdp)

#--- Load a dataframe and an igraph graph
data(ER.deg)
dat <- ER.deg$dat
ceg <- ER.deg$ceg

Next, the att.mapv() function is used to map the dat data frame to the ceg co-expression network:

#--- Map the dataframe to the igraph graph
ceg <- att.mapv(ceg, dat=dat, refcol=1)

The att.setv() function is then used to set valid RedeR attributes in the ceg graph: Symbol will be mapped to nodeAlias, logFC.t3 will be mapped to nodeColor, and ERbdist will be mapped to nodeSize.

#--- Set RedeR attributes
ceg <- att.setv(ceg, from="Symbol", to="nodeAlias")
ceg <- att.setv(ceg, from="logFC.t3", to="nodeColor", breaks=seq(-2,2,0.4), pal=2)  
ceg <- att.setv(ceg, from="ERbdist", to="nodeSize", nquant=10, isrev=TRUE, xlim=c(5,40,1))

Then the ceg graph is displayed in the RedeR application:

#--- Send the ceg graph RedeR
addGraph(rdp, ceg)

Next, the hclust() function is used for an unsupervised hierarchical cluster analysis on the ceg adjacency matrix:

#--- Hierarchical clustering on the adjacency matrix
hc <- hclust(dist(get.adjacency(ceg, attr="weight")))

## Warning: `get.adjacency()` was deprecated in igraph 2.0.0.
## ℹ Please use `as_adjacency_matrix()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

…and the nesthc() function is used to superimpose the corresponding dendrogram onto the network:

#--- Map the hclust object onto the network
nesthc(rdp, hc, cutlevel=3, nmemb=5, cex=0.3, labels=V(ceg)$nodeAlias)

Hierarchical networks. This dendrogram is superimposed onto the network depicted in **Figure 8**. Adapted from Castro *et al.* (2012, https://doi.org/10.1186/gb-2012-13-4-r29).

Figure 7: Hierarchical networks
This dendrogram is superimposed onto the network depicted in Figure 8. Adapted from Castro et al. (2012, https://doi.org/10.1186/gb-2012-13-4-r29).

At this point nested subgraphs should appear in the RedeR application mapped to the dendrogram depicted in Figure 7. Next, the mergeOutEdges() function is used to assign edges to containers, simplifying the graph presented in Figure 8.

#--- Assign edges to containers
mergeOutEdges(rdp, nlevels=2)

…then relax the network:

relax(rdp,100,100,100,100,5)

…and add the legends:

scl <- ceg$legNodeColor$scale
leg <- ceg$legNodeColor$legend
addLegend.color(rdp, colvec=scl, labvec=leg, title="Diff. Gene Expression (logFC)")

scl <- ceg$legNodeSize$scale
leg <- ceg$legNodeSize$legend
addLegend.size(rdp, sizevec=scl, labvec=leg, title="TFBS Distance (kb)")

Hierarchical networks. This graph is obtained by superimposing a dendrogram onto a network. Adapted from Castro *et al.* (2012, https://doi.org/10.1186/gb-2012-13-4-r29).

Figure 8: Hierarchical networks
This graph is obtained by superimposing a dendrogram onto a network. Adapted from Castro et al. (2012, https://doi.org/10.1186/gb-2012-13-4-r29).

4.3 Tree-and-leaf diagrams

The TreeAndLeaf package combines tree and force-directed layout algorithms for drawing binary trees, aiming to improve the visualization of dendrogram leaves. RedeR is used to display tree-and-leaf diagrams. Next we will transform an hclust object into a tree-and-leaf object, and then display Figure 9 in the RedeR application. Please refer to the TreeAndLeaf package’s documentation for additional details and examples.

#--- Load required packages
library("RedeR")
library("igraph")
library("TreeAndLeaf")

#--- If not running, initialize the ReadeR application
# rdp <- RedPort()
# calld(rdp)
resetd(rdp)

#-- Generate an 'hclust' object from the 'iris' dataset
hc_iris <- hclust(dist(iris[,-5]))

#-- Convert the 'hclust' object into a 'tree-and-leaf' object
tal <- treeAndLeaf(hc_iris)

#--- Map 'iris' variables to the tree-and-leaf graph
#Note: 'refcol = 0' indicates that 'iris' rownames will be used as mapping IDs
tal <- att.mapv(g = tal, dat = iris, refcol = 0)

#--- Set node attributes using the 'att.setv' wrapper function
cols <- c("#80b1d3","#fb8072","#8dd3c7")
tal <- att.setv(tal, from="Species", to="nodeColor", cols=cols)
tal <- att.setv(tal, from="Species", to="nodeLineColor", cols=cols) 
tal <- att.setv(tal, from="Petal.Width", to="nodeSize", nquant=6, xlim=c(5,50,1))

#--- Set other attributes using igraph shortcuts
V(tal)$nodeAlias <- ""
E(tal)$edgeColor <- "grey70"

#--- Send the tree-and-leaf graph to RedeR
addGraph(rdp, tal, gzoom=50)

#--- Suggestion: anchor inner nodes to adjust the final layout
# selectNodes(rdp, V(tal)$name[!V(tal)$isLeaf], anchor=TRUE)

#--- Call 'relax' to fine-tune the leaf nodes
relax(rdp, p1=10, p2=100, p3=2, p4=120, p5=1)

#--- Add legends
addLegend.color(rdp, tal, title="Species")
addLegend.size(rdp, tal, title="PetalWidth", position="bottomright")

Figure 9: A tree-and-leaf diagram
This graph is obtained by transforming an hclust object into a tree-and-leaf object.

5 Citation

If you use RedeR, please cite:

Castro MA, Wang X, Fletcher MN, Meyer KB, Markowetz F (2012). “RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations.” Genome Biology, 13(4), R29. Doi:10.1186/gb-2012-13-4-r29.

6 Other useful links

Cardoso MA, Rizzardi LEA, Kume LW, Groeneveld C, Trefflich S, Morais DAA, Dalmolin RJS, Ponder BAJ, Meyer KB, Castro MAA. “TreeAndLeaf: an R/Bioconductor package for graphs and trees with focus on the leaves.” Bioinformatics, 38(5):1463-1464, 2022. https://bioconductor.org/packages/TreeAndLeaf/
Csardi G and Nepusz T. “The Igraph Software Package for Complex Network Research.” InterJournal, ComplexSystems:1695, 2006. https://igraph.org

7 System requirements

RedeR 2.6.1 will need the Java Runtime Environment (JRE) version 11 or higher (Java >=11). The package will also run on earlier Java versions (Java >=8), but with reduced functionality. In order to check the Java on your system, please use the RedPort() function with checkJava=TRUE, for example:

RedPort(checkJava=TRUE)
# RedeR will need Java Runtime Environment (Java >=11)
# Checking Java version installed on this system...
# openjdk version "11.0.13" 2021-10-19
# OpenJDK Runtime Environment (build 11.0.13+8-Ubuntu-0ubuntu1.20.04)
# OpenJDK 64-Bit Server VM (build 11.0.13+8-Ubuntu-0ubuntu1.20.04, mixed mode, sharing)

The exact output will vary, but you need to make sure the system meets the minimum version requirement.

8 Session information

## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] igraph_2.0.2     RedeR_2.6.1      BiocStyle_2.30.0
## 
## loaded via a namespace (and not attached):
##  [1] crayon_1.5.2        cli_3.6.2           knitr_1.45         
##  [4] rlang_1.1.3         xfun_0.42           highr_0.10         
##  [7] jsonlite_1.8.8      glue_1.7.0          htmltools_0.5.7    
## [10] sass_0.4.8          rmarkdown_2.26      grid_4.3.3         
## [13] evaluate_0.23       jquerylib_0.1.4     fastmap_1.1.1      
## [16] yaml_2.3.8          lifecycle_1.0.4     bookdown_0.38      
## [19] BiocManager_1.30.22 compiler_4.3.3      pkgconfig_2.0.3    
## [22] lattice_0.22-5      digest_0.6.35       R6_2.5.1           
## [25] magrittr_2.0.3      bslib_0.6.1         Matrix_1.6-5       
## [28] tools_4.3.3         cachem_1.0.8