computeCliques: Computes clique groups from a similarity network

In cliqueMS: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data

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

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)

cliqueMS documentation built on May 1, 2019, 6:32 p.m.