mcode: MCODE network clustering
In ProNet: Biological Network Construction, Visualization and Analyses

Description Usage Arguments Value References See Also Examples

Clustering of the network using the MCODE method.

1 2	mcode(graph, vwp = 0.5, haircut = FALSE, fluff = FALSE, fdt = 0.8, loops = TRUE)

`graph`	An igraph object.
`vwp`	Vertex weight percentage. Default value is `0.5`.
`haircut`	Boolean value, whether to remove singly-connected nodes from clusters (`TRUE`) or not (`FALSE`).
`fluff`	Boolean value, whether to spand cluster cores by one neighbour shell outwards (`TRUE`) or not (`FALSE`).
`fdt`	Cluster density cutoff. Default value is `0.8`.
`loops`	Boolean value, whether to include self-loops (`TRUE`) or not (`FALSE`).

A list of clusters.

Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003 Jan 13;4(1):2.

cluster

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nlocal<-data.frame(c("DVL1","DVL2","DVL3"))
net<-construction(input=nlocal,db="HPRD",species="human",ID.type="Gene symbol",hierarchy=1)
mcode(net,vwp=0.9,haircut=TRUE,fluff=TRUE,fdt=0.1)

Loading required package: Rcpp
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: MCL
Loading required package: linkcomm
Loading required package: RColorBrewer

Welcome to linkcomm version 1.0-11

For a step-by-step guide to using linkcomm functions:
   > vignette(topic = "linkcomm", package = "linkcomm")
To run an interactive demo:
   > demo(topic = "linkcomm", package = "linkcomm")
To cite, see:
   > citation("linkcomm")
NOTE: To use linkcomm, you require read and write permissions in the current directory (see: help("getwd"), help("setwd"))

$COMPLEX
$COMPLEX[[1]]
 [1]  2  6  7  8  9 11 12 14 18 25 26 30 31 38 39 48 49 50 51 59 60 61 63 77 78
[26] 83 84  3  4  5 13 16 28 29 34 10 23 24 32 73 17 20 21 27 33 36 40 41 42 47
[51] 56 57 64 65 66 68 69 70 72  1 43

$COMPLEX[[2]]
 [1]  1  3  4  5 10 13 15 16 17 19 20 21 22 23 24 27 28 29 32 33 34 36 40 41 42
[26] 43 44 47 52 54 55 57 58 64 65 66 69 70 71 72 82 37 49  6  7 25 30  9 18 12
[51] 35 38 45 46 62 74 76 73 81


$score
[1] 6.903226 6.266667