Group features using XCMS (new interface)

Source: R/generics.R, R/feature_groups-xcms3.R

Uses the new xcms3 interface from the xcms package to find features.

groupFeaturesXCMS3(feat, ...)

# S4 method for class 'features'
groupFeaturesXCMS3(
  feat,
  rtalign = TRUE,
  loadRawData = TRUE,
  groupParam = xcms::PeakDensityParam(sampleGroups = analysisInfo(feat)$group),
  preGroupParam = groupParam,
  retAlignParam = xcms::ObiwarpParam(),
  verbose = TRUE
)

# S4 method for class 'featuresSet'
groupFeaturesXCMS3(
  feat,
  groupParam = xcms::PeakDensityParam(sampleGroups = analysisInfo(feat)$group),
  verbose = TRUE
)

Arguments

feat

The features object with the features to be grouped.

...

Further parameters passed to the selected grouping algorithm.

rtalign

Set to TRUE to enable retention time alignment.

loadRawData

Set to TRUE if analyses are available as mzXML or mzML files. Otherwise MS data is not loaded, and some dummy data (e.g. file paths) is used in the returned object.

groupParam, retAlignParam

parameter object that is directly passed to xcms::groupChromPeaks and xcms::adjustRtime, respectively.

preGroupParam

grouping parameters applied when features are grouped prior to alignment (only with peak groups alignment).

verbose

if FALSE then no text output will be shown.

Value

An object of a class which is derived from featureGroups.

Details

This function uses XCMS3 to group features. This function is called when calling groupFeatures with algorithm="xcms3".

Grouping of features and alignment of their retention times are performed with the xcms::groupChromPeaks and xcms::adjustRtime functions, respectively. Both of these functions support an extensive amount of parameters that modify their behavior and may therefore require optimization.

Sets workflows

loadRawData and arguments related to retention time alignment are currently not supported for sets workflows.

References

Benton HP, Want EJ, Ebbels TMD (2010). “Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data.” BIOINFORMATICS, 26, 2488.

Smith, C.A., Want, E.J., O'Maille, G., Abagyan,R., Siuzdak, G. (2006). “XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching and identification.” Analytical Chemistry, 78, 779–787.

Tautenhahn R, Boettcher C, Neumann S (2008). “Highly sensitive feature detection for high resolution LC/MS.” BMC Bioinformatics, 9, 504.

See also

groupFeatures for more details and other algorithms.