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

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

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

Add Layers

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

Add/print Nodes

  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     <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,"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

  #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     <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

Visualization

  plot(g12,layout = "scaled")

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.