SMITE (version 1.0.2)

runSpinglass: Run Spinglass algorithm on a Scored PvalueAnnotation

Description

This function is a function to prepare the data for calling the Spinglass network algorithm.

Usage

runSpinglass(pvalue_annotation, network, random_alpha = 0.05, gam = 0.5, 
node_alpha = 0.05, maxsize = 500, minsize = 8, num_iterations = 1000, simplify = TRUE)

Arguments

pvalue_annotation

An S4 object of class PvalueAnnotation

network

An graph object of class graphNEL or igraph

random_alpha

A numeric specifying a threshold with with to determine module signficance after randomization

gam

A parameter used by the Spinglass algorithm

node_alpha

The proportion of nodes to be used as seeds for the community detection

maxsize

The maximum module size

minsize

The minimum module size

num_iterations

The number of randomizations that will be computed to determine whether the module is significant by chance

simplify

A logical (TRUE(DEFAULT)/FALSE) that specifies whether network should be simplified by removing self loops and repeated edges

Value

An S4 object of class PvalueAnnotation with modules loaded

Details

In the provided Epimods reference, West et al outlined the advantages of using the spin-glass algorithm in the detection of modules. Please consult the reference for more detailed information on the spin-glass algorithm implemented in the package igraph.

Like Epimods, this function employs the spin-glass algorithm implemented in igraph and uses random permutations to assess the "modularity," the number and strength of connected nodes, of a module. However, SMITE scores are interpreted as Chi-square distributed statistics whenever possible, rather than the weighted-T-statistic in Epimods.

References

James West, Stephan Beck, Xiangdong Wang & Andrew E. Teschendorff An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathway. Scientific Reports 3, Article number: 1630 (2013)

https://code.google.com/p/epimods/

See Also

FEM runBioNet extractModules plotModule

Examples

data(test_annotation_score_data)

#load(system.file("data","Reactome.Symbol.Igraph.rda", package="SMITE"))
 
## NOTE: commented out for example.  See vignette for better explanation ##
#test_annotation <- runSpinglass(pvalue_annotation=test_annotation, 
#network=REACTOME, maxsize=50, num_iterations=10)

plotModule(test_annotation, which_network=6, layout="fr")