cliqueMS (version 0.3.1)

computeCliques: Computes clique groups from a similarity network

Description

This function splits the features in the network in clique groups. The cliques are fully connected components that have high similarity for inner edges and low similarity for edges outside the clique. This function finds the clique groups that better fit this criteria, moving nodes to different groups until we find the groups that have the best log-likelihood.

Usage

computeCliques(anclique, tol = 1e-05, silent = TRUE)

Arguments

anclique

This function uses S3 'anClique' object. Gives warning if clique groups have already been computed.

tol

Minimum relative increase in log-likelihood to do a new round of log-likelihood maximisation.

silent

If 'FALSE' print on the console the log-likelihood maximization progress. Default is 'TRUE'.

Value

It returns an 'anClique' object with the computed clique groups. It adds the column 'cliqueGroup' to the 'peaklist' in the 'anClique' object.

See Also

getCliques

Examples

# NOT RUN {
library(cliqueMS)
mzfile <- system.file("standards.mzXML", package = "cliqueMS")
msSet <- xcms::xcmsSet(files = mzfile, method = "centWave",
ppm = 15, peakwidth = c(5,20), snthresh = 10)
ex.anClique <- createanClique(msSet)
summary(ex.anClique)
netlist <- createNetwork(msSet, msSet@peaks, filter = TRUE)
ex.anClique$network <- netlist$network
ex.anClique$peaklist <- netlist$peaklist
ex.cliqueGroups <- computeCliques(ex.anClique)
summary(ex.cliqueGroups)
# }