alt text ## 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:


Installation from CRAN

mully is now available on CRAN !!

mully on CRAN

Installation via Github


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

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

Add Layers

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

Add/print Nodes

  print("Node d1 added as disease")
  print("Node d2 added as disease")
  print("Node d3 added as disease")
  print("Node dr1 added as drug")
  print("Node dr2 added as drug")
  print("Node dr3 added as drug")
  print("Node g1 added as gene")
  print("Node g2 added as gene")

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

Add/print/remove Edges

  g=addEdge(g,"g2","dr3",list(name="mutates and causes"))
  #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

Merge two graphs

  #Create a Second graph





  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

  #Print the graph
  # 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     <NA> <NA>
  #   2    d2 3   t1     <NA> <NA>
  #   3    d3 3   t1     <NA> <NA>
  #   4   dr1 2 <NA>   strong <NA>
  #   5   dr2 2 <NA>   strong <NA>
  #   6   dr3 2 <NA> moderate <NA>
  #   7    g1 1 <NA>     <NA>   AF
  #   8    g2 1 <NA>     <NA>   BE
  #   9   dr4 2 <NA>   strong <NA>
  #   10  dr5 2 <NA>   strong <NA>
  #   11  dr6 2 <NA> moderate <NA>
  #   12   p1 4 <NA>     <NA> <NA>
  #   13   p2 4 <NA>     <NA> <NA>
  #   14   p3 4 <NA>     <NA> <NA>
  #   15   g3 1 <NA>     <NA> <NA>
  #   16   g4 1 <NA>     <NA> <NA>
  # 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


  plot(g12,layout = "scaled")

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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