community.tree. bio3d 2.3-0

Usage

community.tree(x, rescale=FALSE)

Arguments

x: A protein network graph object as obtained from the ‘cna’ function.
rescale: Logical, indicating whether to rescale the community names starting from 1. If FALSE, the community names will start from N+1, where N is the number of nodes.

Description

This function reconstructs the community tree of the community clustering analysis performed by the ‘cna’ function. It allows the user to explore different network community partitions.

Value

modularity: A numeric vector containing the modularity values.
tree: A numeric matrix containing in each row the community residue memberships corresponding to a modularity value. The rows are ordered according to the ‘modularity’ object.
num.of.comms: A numeric vector containing the number of communities per modularity value. The vector elements are ordered according to the ‘modularity’ object.

Details

The input of this function should be a ‘cna’ class object containing ‘network’ and ‘communities’ attributes.

This function reconstructs the community residue memberships for each modularity value. The purpose is to facilitate inspection of alternate community partitioning points, which in practice often corresponds to a value close to the maximum of the modularity, but not the maximum value itself.

Examples


# PDB server connection required - testing excluded

###-- Build a CNA object
pdb <- read.pdb("4Q21")

  Note: Accessing on-line PDB file

modes <- nma(pdb)

 Building Hessian...		Done in 0.063 seconds.
 Diagonalizing Hessian...	Done in 0.277 seconds.

cij <- dccm(modes)


  |======================================================================| 100%

net <- cna(cij, cutoff.cij=0.2)


##-- Reconstruct the community membership vector for each clustering step.
tree <- community.tree(net, rescale=TRUE)

## Plot modularity vs number of communities
plot( tree$num.of.comms, tree$modularity )

## Inspect the maximum modularity value partitioning
max.mod.ind <- which.max(tree$modularity)



## Number of communities (k) at max modularity
tree$num.of.comms[ max.mod.ind ]

[1] 9


## Membership vector at this partition point 
tree$tree[max.mod.ind,]

  [1] 1 1 1 1 1 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 2
 [38] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 6 5
 [75] 5 5 2 2 7 3 3 3 8 8 8 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9
[112] 7 3 3 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 4 4 4
[149] 4 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9


# Should be the same as that contained in the original CNA network object
net$communities$membership == tree$tree[max.mod.ind,]

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
  17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
  33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
  49   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
  65   66   67   68   69   70   71   72   73   74   75   76   77   78   79   80 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
  81   82   83   84   85   86   87   88   89   90   91   92   93   94   95   96 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
  97   98   99  100  101  102  103  104  105  106  107  108  109  110  111  112 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
 113  114  115  116  117  118  119  120  121  122  123  124  125  126  127  128 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
 129  130  131  132  133  134  135  136  137  138  139  140  141  142  143  144 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
 145  146  147  148  149  150  151  152  153  154  155  156  157  158  159  160 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
 161  162  163  164  165  166  167  168 
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 


# Inspect a new membership partitioning (at k=7)
memb.k7 <- tree$tree[ tree$num.of.comms == 7, ]

## Produce a new k=7 community network  
net.7 <- network.amendment(net, memb.k7)
plot(net.7, pdb)

Obtaining layout from PDB structure


#view.cna(net.7, trim.pdb(pdb, atom.select(pdb,"calpha")), launch=TRUE )

Author

Guido Scarabelli

Reconstruction of the Girvan-Newman Community Tree for a CNA Class Object.