mully

Introduction

Network theory has been used for many years in the modeling and analysis of complex systems, as epidemiology, biology and biomedicine . As the data evolves and becomes more heterogeneous and complex, monoplex networks become an oversimplification of the corresponding systems. This imposes a need to go beyond traditional networks into a richer framework capable of hosting objects and relations of different scales, called Multilayered Network Mully, multilayer networks, is an R package that provides a multilayer network framework. Using this package, the user can create, modify and visualize graphs with multiple layers. This package is an extension to the igraph package that provides a monolayer graph framework. The package is implemented as a part of the Multipath Project directed by Dr. Frank Kramer .

Publication

More information and references can be found in the mully paper:

https://www.mdpi.com/2073-4425/9/11/519

Installation

Installation from CRAN

mully is now available on CRAN !!

mully on CRAN

Installation via Github

R require(devtools) install_github("frankkramer-lab/mully") library(mully)

Test the package

In this section, we provide a demo to test the package by calling some of the function. After running this script, you will have a graph g with 3 layers and 8 nodes. the graph can also be modified by calling other functions. Please refer to help to see the available functions.

Create new mully graph

R g <- mully("MyFirstMully",direct = F)

Add Layers

R g <- addLayer(g, c("Gene", "Drug", "Drug", "Disease"))

Add/print Nodes

```R g=addNode(g,"d1","disease",attributes=list(type="t1")) print("Node d1 added as disease")

g=addNode(g,"d2","disease",attributes=list(type="t1")) print("Node d2 added as disease")

g=addNode(g,"d3","disease",attributes=list(type="t1")) print("Node d3 added as disease")

g=addNode(g,"dr1","drug",attributes=list(effect="strong")) print("Node dr1 added as drug")

g=addNode(g,"dr2","drug",attributes=list(effect="strong")) print("Node dr2 added as drug")

g=addNode(g,"dr3","drug",attributes=list(effect="moderate")) print("Node dr3 added as drug")

g=addNode(g,"g1","gene",attributes=list(desc="AF")) print("Node g1 added as gene")

g=addNode(g,"g2","gene",attributes=list(desc="BE")) print("Node g2 added as gene")

#See vertices attributes print(getNodeAttributes(g))

#The Result: # name n type effect desc # 1 d1 3 t1 # 2 d2 3 t1 # 3 d3 3 t1 # 4 dr1 2 strong # 5 dr2 2 strong # 6 dr3 2 moderate # 7 g1 1 AF # 8 g2 1 BE

```

Add/print/remove Edges

```R g=addEdge(g,"dr1","d2",list(name="treats")) g=addEdge(g,"dr1","d2",list(name="extraEdge")) g=addEdge(g,"d2","g1",list(name="targets")) g=addEdge(g,"g2","dr3",list(name="mutates and causes")) g=addEdge(g,"dr3","d3",list(name="treats"))

print(getEdgeAttributes(g)

#The Result: # V1 V2 name # 1 d2 dr1 treats # 2 d2 dr1 extraEdge # 3 d2 g1 targets # 4 dr3 g2 mutates and causes # 5 d3 dr3 treats

removeEdge(g,"d2","dr1",multi=T)

```

Merge two graphs

```R #Create a Second graph g1=mully()

g1=addLayer(g1,c("protein","drug","gene"))

g1=addNode(g1,"dr4","drug",attributes=list(effect="strong")) g1=addNode(g1,"dr5","drug",attributes=list(effect="strong")) g1=addNode(g1,"dr6","drug",attributes=list(effect="moderate"))

g1=addNode(g1,"p1","protein") g1=addNode(g1,"p2","protein") g1=addNode(g1,"p3","protein")

g1=addNode(g1,"g3","gene") g1=addNode(g1,"g4","gene")

g1=addEdge(g1,nodeStart = "p2",nodeDest = "p3",attributes = list(name="interacts")) g1=addEdge(g1,nodeStart = "dr6",nodeDest = "g4",attributes = list(name="targets"))

#Merge both graphs g12=merge(g,g1)

#Print the graph print(g12)

# Printing this graph gives this result: # mully -- MyFirstMully # 4 Layers: # ID Name NameLower # 1 1 Gene gene # 2 2 Drug drug # 3 3 Disease disease # 4 4 protein protein # # 16 Nodes: # name n type effect desc # 1 d1 3 t1 # 2 d2 3 t1 # 3 d3 3 t1 # 4 dr1 2 strong # 5 dr2 2 strong # 6 dr3 2 moderate # 7 g1 1 AF # 8 g2 1 BE # 9 dr4 2 strong # 10 dr5 2 strong # 11 dr6 2 moderate # 12 p1 4 # 13 p2 4 # 14 p3 4 # 15 g3 1 # 16 g4 1 #
# 7 Edges: # V1 V2 name # 1 d2 dr1 treats # 2 d2 dr1 extraEdge # 3 d2 g1 targets # 4 dr3 g2 mutates and causes # 5 d3 dr3 treats # 6 p2 p3 interacts # 7 dr6 g4 targets

```

Visualization

R plot(g12,layout = "scaled")

R plot3d(g12)

Available Functions

mully functions are divided into different files depending on their functionnality range: Constructor , Layers Functions , Node Functions , Edge Functions , Merge Function , Visualization Functions , Import Functions , Export Functions , Demo.

| Function |Description| | --------------- |-----------| |mully(name,direct)|Constructor Function, Create an empty multilayered graph| |print(g)|Print function| |addLayer(g, nameLayer)| Add a layer or a set of layers to a graph| |removeLayer(g, name,trans)|Delete a layer or a set of layers from a graph| |isLayer(g, name)|Verify if the layer exists in a graph| |getLayersCount(g)|Get the number of layers in a graph| |getLayer(g, nameLayer)|Get the nodes on a layer in a graph| |getNode(g,nameNode)|Get a node from a graph| |getIDNode(g,nameNode)|Get the id of a node| |addNode(g, nodeName, layerName, attributes)|Add a node with assigned layer and attributes to a graph| |removeNode(g, name,trans)|Delete a node or a set of nodes from a graph| |getNodeAttributes(g,nameNode)|Get the attributes of one or all nodes| |addEdge(g, nodeStart, nodeDest, attributes)|Add an edge| |removeEdge(g, nodeStart, nodeDest,attributes, multi)|Delete an edge| |getEdgeAttributes(g,nodeStart,nodeDest)|Get the attributes of the edges connecting two nodes or all the edges in the graph| |getIDEdge(g,nodeStart,nodeDest)|Get the ids of the edges connecting two nodes| |merge(g1,g2)|Merge or unite two graphs| |plot(g,layout)|Plot the graph in 2D| |plot3d(g)|Plot the graph in 3D using rgl| |importGraphCSV(name,direct,layers,nodes,edges)|Import a mully graph from csv files| |importLayersCSV(g,file)|Import layers to a mully graph from a CSV file| |importNodesCSV(g,file)|Import nodes to a mully graph from a CSV file| |importEdgesCSV(g,file)|Import edges to a mully graph from a CSV file|