Markov Cluster Algorithm

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Description

Contains the Markov cluster algorithm (MCL) by van Dongen (2000) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.

Details

Package: MCL
Type: Package
Version: 1.0
Date: 2015-03-10
License: GPL-2 | GPL-3 [expanded from: GPL (= 2)]

The Markov Cluster Algorithm (MCL) is a method to identify clusters in undirected network graphs. It is suitable for high-dimensional data (e.g. gene expression data).

The original MCL uses the adjacency matrix of a graph (propsed by van Dongen (2000)). The function mcl in this package allows in addition the input of a (n x n) matrix.

Note

We thank Moritz Hanke for his help in realizing this package.

Author(s)

Martin L. J├Ąger

Maintainer: Ronja Foraita <foraita@bips.uni-bremen.de>
Leibniz Institute for Prevention Research and Epidemiology (BIPS)

References

van Dongen, S.M. (2000) Graph Clustering by Flow Simulation. Ph.D. thesis, Universtiy of Utrecht. Utrecht University Repository: http://dspace.library.uu.nl/handle/1874/848

Examples

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### Load adjacency matrix
adjacency <- matrix(c(0,1,1,1,0,0,0,0,0,1,0,1,1,1,0,0,0,0,1,1,
             0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,
             0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,
             0,0,0,0,0,0,0,0,0,0,0,0,0), byrow=TRUE, nrow=9)

### Run MCL 
mcl(x = adjacency, addLoops = TRUE )

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