# Group nodes

by Mark Grimes

#### 2020-08-21

#### Package

RCy3 2.8.1

This vignette will show you how to use node grouping functions to manipulate graphs in Cytoscape.

# 1 Installation

    if(!"RCy3" %in% installed.packages()){
        install.packages("BiocManager")
        BiocManager::install("RCy3")
    }
    library(RCy3)

# 2 Required Software

The whole point of RCy3 is to connect with Cytoscape. You will need to install and launch Cytoscape:

- Download the latest Cytoscape from http://www.cytoscape.org/download.php
- Complete installation wizard
- Launch Cytoscape

    cytoscapePing()
    cytoscapeVersionInfo ()

# 3 Background

The ability to group nodes together into “metanodes” and collapse them to a single node in a graph is useful for simplifying views of a complex network.

The example in this vignette describes application of node grouping functions to data that includes protein-protein interactions and clustered correlations of protein post-translational modifications (Grimes, et al., 2018). This vignette plots five proteins and their modifications, and uses the node grouping functions to manipulate the graph in Cytoscape.

# 4 Example

First we set up the node and edge data frames.

    net.nodes <- c("ALK", "ALK p Y1078", "ALK p Y1096", "ALK p Y1586", "CTNND1", "CTNND1 p Y193", "CTNND1 p Y217", "CTNND1 p Y228", "CTNND1 p Y241", "CTNND1 p Y248", "CTNND1 p Y302", "CTNND1 p Y904", "CTTN", "CTTN ack K107", "CTTN ack K124", "CTTN ack K147", "CTTN ack K161", "CTTN ack K235", "CTTN ack K390", "CTTN ack K87", "CTTN p S113", "CTTN p S224", "CTTN p Y104", "CTTN p Y154", "CTTN p Y162", "CTTN p Y228", "CTTN p Y334", "CTTN p Y421", "IRS1", "IRS1 p Y632", "IRS1 p Y941", "IRS1 p Y989", "NPM1", "NPM1 ack K154", "NPM1 ack K223", "NPM1 p S214", "NPM1 p S218")
    net.genes <- sapply(net.nodes,  function (x) unlist(strsplit(x, " ",  fixed=TRUE))[1])
    parent <- c("", "ALK", "ALK", "ALK", "", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "", "IRS1", "IRS1", "IRS1", "", "NPM1", "NPM1", "NPM1", "NPM1")
    nodeType <- c("protein", "modification", "modification", "modification", "protein", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "protein", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "modification", "protein", "modification", "modification", "modification", "protein", "modification", "modification", "modification", "modification")
    netnodes.df <- data.frame(id=net.nodes, Gene.Name=net.genes, parent, nodeType, stringsAsFactors = FALSE)

    # Define edge data
    source.nodes <- c("ALK", "ALK", "ALK", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "CTNND1", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "CTTN", "IRS1", "IRS1", "IRS1", "NPM1", "NPM1", "NPM1", "NPM1", "ALK p Y1096", "CTNND1 p Y193", "CTNND1 p Y193", "CTNND1 p Y228", "CTNND1 p Y904", "CTNND1 p Y217", "CTNND1 p Y241", "CTNND1 p Y248", "ALK p Y1078", "ALK p Y1096", "ALK p Y1586", "IRS1 p Y941", "CTTN ack K147", "CTTN ack K107", "CTTN ack K235", "CTTN ack K87", "CTTN ack K147", "CTTN ack K124", "CTTN ack K147", "CTTN ack K235", "CTTN ack K161", "CTTN ack K390", "NPM1 ack K223", "NPM1 ack K154", "NPM1 ack K223", "ALK", "CTNND1", "CTNND1", "CTTN", "IRS1")
    target.nodes <- c("ALK p Y1078", "ALK p Y1096", "ALK p Y1586", "CTNND1 p Y193", "CTNND1 p Y217", "CTNND1 p Y228", "CTNND1 p Y241", "CTNND1 p Y248", "CTNND1 p Y302", "CTNND1 p Y904", "CTTN ack K107", "CTTN ack K124", "CTTN ack K147", "CTTN ack K161", "CTTN ack K235", "CTTN ack K390", "CTTN ack K87", "CTTN p S113", "CTTN p S224", "CTTN p Y104", "CTTN p Y154", "CTTN p Y162", "CTTN p Y228", "CTTN p Y334", "CTTN p Y421", "IRS1 p Y632", "IRS1 p Y941", "IRS1 p Y989", "NPM1 ack K154", "NPM1 ack K223", "NPM1 p S214", "NPM1 p S218", "ALK p Y1586", "CTNND1 p Y228", "CTNND1 p Y302", "CTNND1 p Y302", "CTTN p Y154", "CTTN p Y162", "CTTN p Y162", "CTTN p Y334", "IRS1 p Y632", "IRS1 p Y989", "IRS1 p Y989", "IRS1 p Y989", "CTTN p S113", "CTTN p S224", "CTTN p S224", "CTTN p S224", "CTTN p Y104", "CTTN p Y228", "CTTN p Y228", "CTTN p Y228", "CTTN p Y421", "CTTN p Y421", "NPM1 p S214", "NPM1 p S218", "NPM1 p S218", "IRS1", "CTTN", "IRS1", "NPM1", "NPM1")
    Weight <- c(100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 0.8060606, 0.7575758, 0.7454545, 0.9393939, 0.8949096, 0.7329699, 0.7553845, 0.7866191, 0.775, 0.6969697, 0.7818182, 0.8424242, -0.7714286, -0.8385965, -0.5017544, -0.7473684, -0.5252838, -0.9428571, -0.8285714, -0.6713287, -0.5508772, -0.9428571, -0.8857143, -0.6310881, -0.8285714, 0.6123365, 2.115272, 0.002461723, 0.3354451, 0.5661711)
    netedges.df <- data.frame(source=source.nodes, target=target.nodes, Weight, stringsAsFactors = FALSE)

    #create network from data frames
    net.suid <- createNetworkFromDataFrames(netnodes.df, netedges.df, title=paste(paste("Group Nodes Test"), 1+length(getNetworkList())), collection = "RCy3 Vignettes")

    # Make sure nodes are spread out sufficiently
    layoutNetwork('force-directed defaultSpringCoefficient=0.00001 defaultSpringLength=50 defaultNodeMass=5')

Note that for convenience the data frame has defined whether a node is a protein or a modification, and also defined the parent node for each modification.

The function selectNodes looks by default for the node SUID, which can be retrieved by getTableColumns. Alternatively, the data frame can be used to distinguish proteins and modifications.

    nodedata <- getTableColumns("node")
    edgedata <- getTableColumns("edge")
    genes <- netnodes.df[grep("protein", netnodes.df$nodeType), "id"]

    #select by gene SUIDs
    geneSUIDs <- nodedata[grep("protein", nodedata$nodeType), 1]
    selectNodes(geneSUIDs, preserve.current.selection = FALSE)

    # or by names in the "id" column
    selectNodes(c("ALK","IRS1"), by.col="id", preserve.current.selection = FALSE)

    # or by names based on dataframe subsetting
    modifications <- netnodes.df[grep("modification", netnodes.df$nodeType), "id"]
    selectNodes(modifications, by='id', pre=FALSE)

    # Now select one protein and all its modifications
    deltacatnodes <- netnodes.df[grep("CTNND1", netnodes.df$Gene.Name), "id"]
    selectNodes(deltacatnodes, by.col="id", preserve=FALSE)

Let’s create a new group of the selected nodes and collapse it into one node…

    createGroup("delta catenin group")
    collapseGroup("delta catenin group")

…then expand it again.

    expandGroup("delta catenin group")

For these data, we can create groups of all proteins together with their modifications. Here we name the groups by their gene names.

    deleteGroup("delta catenin group")
    for(i in 1:length(genes)) {
        print(genes[i])
        selectNodes(netnodes.df[grep(genes[i], netnodes.df$Gene.Name), "id"], by.col="id", preserve=FALSE)
        createGroup(genes[i])
        collapseGroup(genes[i])
    }
    groups.1 <- listGroups()
    groups.1
    # should see 5 group SUIDs reported

These can all be expanded at once.

    expandGroup(genes)

An alternative method that might be quicker for large networks is to use the input data frame.

    deleteGroup(genes)
    for(i in 1:length(genes)) {
        print(genes[i])
        createGroup(genes[i], nodes=netnodes.df[grep(genes[i], netnodes.df$Gene.Name), "id"], nodes.by.col = "id")
    }
    collapseGroup(genes)
    expandGroup(genes)

This can be done more simply using sapply()

    deleteGroup(genes)
    sapply(genes, function(x) createGroup(x, nodes=netnodes.df[grep(x, netnodes.df$Gene.Name), "id"], nodes.by.col = "id"))
    collapseGroup(genes)

A groups’ information can be retrieved and independently expanded

    getGroupInfo("ALK")
    expandGroup("ALK")

    # Get all groups' info
    group.info <- list()
    group.info <- lapply(listGroups()$groups, getGroupInfo)
    print(group.info)

Reference

Grimes, et al., 2018. Sci. Signal. Vol. 11, Issue 531, DOI: 10.1126/scisignal.aaq1087, http://stke.sciencemag.org/content/11/531/eaaq1087.