# MCL-package: Markov Cluster Algorithm In MCL: Markov Cluster Algorithm

## 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. Jäger

Maintainer: Ronja Foraita <[email protected]>
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

 1 2 3 4 5 6 7 8 ### 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 )

### Example output

\$K
[1] 3

\$n.iterations
[1] 14

\$Cluster
[1] 1 1 1 1 2 2 2 0 0

MCL documentation built on May 29, 2017, 10:59 a.m.