cliqueMS (version 0.3.1)

getCliques: Compute clique groups from processed m/z data

Description

This function splits features in groups to find isotope and adduct annotation within each group. To find them it uses a similarity network. This similarity network has nodes as features and weighted edges as the cosine similarity between features. Once the network is obtained we find clique groups in this network. The clique groups are fully connected components with high similarity in inner edges and lower similarity in edges outside the clique. We move nodes to different groups until we find the groups with the maximum log-likelihood.

Usage

getCliques(mzData, filter = TRUE, mzerror = 5e-06, intdiff = 1e-04,
  rtdiff = 1e-04, tol = 1e-05, silent = TRUE)

Arguments

mzData

An 'object with processed m/z data. Currently supported class types are 'xcmsSet' or 'XCMSnExp.

filter

If TRUE, filter out very similar features that have a correlation similarity > 0.99 and equal values of m/z, retention time and intensity.

mzerror

Relative error for m/z, if relative error between two features is below that value that features are considered with similar m/z value.

intdiff

Relative error for intensity, if relative error between two features is below that value that features are considered with similar intensity.

rtdiff

Relative error for retention time, if relative error between two features is below that value that features are considered with similar retention time.

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.

Details

Signal processing algorithms may output artefact features. Sometimes they produce two artefact features which are almost identical This artefacts may lead to errors in the computation of the clique groups, so it is recommended to set 'filter' = TRUE to drop repeated. features.

See Also

computeCliques createNetwork anClique

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.cliqueGroups <- getCliques(msSet)
# }