MMG-package {MMG} | R Documentation |
MMG is a Mixture Model which can integrate the structure of a network in the statistical analysis of data.
This implementation MMG assumes the existence of three classes of genes/proteins/etc. within a network: down-regulated, up-regulated, or unchanged. The underlying data are log-ratios, hence relative, measurements.
The package aims at identifying clusters of genes/proteins/etc. that behave consistently along the network's pathways. This is done by implementing the Bayesian model described in the Reference Sanguinetti et al. (2008), by running a Gibbs sampler, and by cutting the graph.
The Gibbs sampler is run by MMG.compute
. To cut the graph is
done by MMG.cut.graph
. Finally, MMG.make.dot
conveniently produces a DOT file, a file whose format can be used
for visualation using various softwares (including GraphViz
http://www.graphviz.org/
- see also the package
Rgraphviz
).
Package: | MMG |
Type: | Package |
Version: | 1.2.2 |
Date: | 2008-05-20 |
License: | GPL-3+ |
Josselin Noirel, based on an original implementation by Guido Sanguinetti (http://www.dcs.shef.ac.uk/~guido/).
Maintainer: Josselin Noirel <j.noirel@sheffield.ac.uk>
Sanguinetti, Noirel, and Wright., MMG: a probabilistic tool to identify submodules of metabolic pathways, Bioinformatics (2008)
## Not run: r <- MMG.compute(file.name = "NostocData/R_net.dat", steps = 100000, burn.in = 1000, sigma = 0.3, alpha = 1) ## End(Not run) ## Not run: n <- r$dat$n.nodes ## Not run: s <- MMG.cut.graph(r, descriptions = "NostocData/R_descr.dat", method = "THRESHOLD", threshold = 0.15, select = "UP") ## End(Not run) ## Not run: l <- (1:n)[s$components != 0] ## Not run: MMG.make.dot(r, file.name = "nostoc.dot", selection = l, type = "UNDIRECTED", rem.loops = TRUE) ## End(Not run)