Group features using XCMS (old interface)

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

Group and align features with the legacy xcmsSet function from the xcms package.

groupFeaturesXCMS(feat, ...)

# S4 method for features
groupFeaturesXCMS(
  feat,
  rtalign = TRUE,
  loadRawData = TRUE,
  groupArgs = list(mzwid = 0.015),
  retcorArgs = list(method = "obiwarp"),
  verbose = TRUE
)

# S4 method for featuresSet
groupFeaturesXCMS(feat, groupArgs = list(mzwid = 0.015), 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.

groupArgs

named character vector that may contain extra grouping parameters to be used by xcms::group

retcorArgs

named character vector that may contain extra parameters to be used by xcms::retcor.

verbose

if FALSE then no text output will be shown.

Value

An object of a class which is derived from featureGroups.

The featuresSet method (for sets workflows) returns a

featureGroupsSet object.

Details

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

Grouping of features and alignment of their retention times are performed with the xcms::group and xcms::retcor functions, respectively. Both functions have an extensive list of parameters to modify their behavior and may therefore be used to potentially optimize results.

Sets workflows

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

References

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

Ralf Tautenhahn, Christoph Boettcher, Steffen Neumann: Highly sensitive feature detection for high resolution LC/MS BMC Bioinformatics, 9:504 (2008)

H. Paul Benton, Elizabeth J. Want and Timothy M. D. Ebbels Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data Bioinformatics, 26:2488 (2010)

See also

groupFeatures for more details and other algorithms.