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MassSpecSettings_FindFeatures_xcms3_centwave

Source: R/class_S3_MassSpecSettings.R
MassSpecSettings_FindFeatures_xcms3_centwave.Rd

Settings for finding features (i.e., chromatographic peaks) in mzML/mzXML files using the package xcms (version 3) with the algorithm centWave. The function uses the package patRoon in the background.

Usage

MassSpecSettings_FindFeatures_xcms3_centwave(
  ppm = 12,
  peakwidth = c(5, 60),
  snthresh = 15,
  prefilter = c(5, 1500),
  mzCenterFun = "wMean",
  integrate = 1,
  mzdiff = -2e-04,
  fitgauss = TRUE,
  noise = 500,
  verboseColumns = TRUE,
  firstBaselineCheck = FALSE,
  extendLengthMSW = FALSE
)

Arguments

ppm

numeric(1) defining the maximal tolerated m/z deviation in consecutive scans in parts per million (ppm) for the initial ROI definition.

peakwidth

numeric(2) with the expected approximate feature width in chromatographic space. Given as a range (min, max) in seconds.

snthresh

numeric(1) defining the signal to noise ratio cutoff.

prefilter

numeric(2): c(k, I) specifying the prefilter step for the first analysis step (ROI detection). Mass traces are only retained if they contain at least k peaks with intensity >= I.

mzCenterFun

Name of the function to calculate the m/z center of the chromatographic peak (feature). Allowed are: "wMean": intensity weighted mean of the peak's m/z values, "mean": mean of the peak's m/z values, "apex": use the m/z value at the peak apex, "wMeanApex3": intensity weighted mean of the m/z value at the peak apex and the m/z values left and right of it and "meanApex3": mean of the m/z value of the peak apex and the m/z values left and right of it.

integrate

Integration method. For integrate = 1 peak limits are found through descent on the mexican hat filtered data, for integrate = 2 the descent is done on the real data. The latter method is more accurate but prone to noise, while the former is more robust, but less exact.

mzdiff

numeric(1) representing the minimum difference in m/z dimension required for peaks with overlapping retention times; can be negative to allow overlap. During peak post-processing, peaks defined to be overlapping are reduced to the one peak with the largest signal.

fitgauss

logical(1) whether or not a Gaussian should be fitted to each peak. This affects mostly the retention time position of the peak.

noise

numeric(1) allowing to set a minimum intensity required for centroids to be considered in the first analysis step (centroids with intensity < noise are omitted from ROI detection).

verboseColumns

logical(1) whether additional peak meta data columns should be returned.

firstBaselineCheck

logical(1). If TRUE continuous data within regions of interest is checked to be above the first baseline.

extendLengthMSW

Option to force centWave to use all scales when running centWave rather than truncating with the EIC length. Uses the "open" method to extend the EIC to a integer base-2 length prior to being passed to convolve rather than the default "reflect" method. See https://github.com/sneumann/xcms/issues/445 for more information.

Value

A ProcessingSettings S3 class object with subclass MassSpecSettings_FindFeatures_xcms3_centwave.

Details

See the findFeaturesXCMS3 function from the patRoon package for more information and requirements.

References

Helmus R, ter Laak TL, van Wezel AP, de Voogt P, Schymanski EL (2021). “patRoon: open source software platform for environmental mass spectrometry based non-target screening.” Journal of Cheminformatics, 13(1). doi:10.1186/s13321-020-00477-w .

Helmus R, van de Velde B, Brunner AM, ter Laak TL, van Wezel AP, Schymanski EL (2022). “patRoon 2.0: Improved non-target analysis workflows including automated transformation product screening.” Journal of Open Source Software, 7(71), 4029. doi:10.21105/joss.04029 .

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.

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