dimsPredictPurity-purityD-method: Using purityD object, assess anticipated purity from a DI-MS...

In msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics

Description

Assess the precursor purity of anticpated MS/MS spectra. i.e. it 'predicts' the precursor purity of the DI-MS peaks for a future MS/MS run.

Usage

## S4 method for signature 'purityD'
dimsPredictPurity(
  Object,
  ppm = 1.5,
  minOffset = 0.5,
  maxOffset = 0.5,
  iwNorm = FALSE,
  iwNormFun = NULL,
  ilim = 0.05,
  sampleOnly = FALSE,
  isotopes = TRUE,
  im = NULL
)

Arguments

Object	object = purityD object
ppm	numeric = tolerance for target mz value in each scan
minOffset	numeric = isolation window minimum offset
maxOffset	numeric = isolation window maximum offset
iwNorm	boolean = if TRUE then the intensity of the isolation window will be normalised based on the iwNormFun function
iwNormFun	function = A function to normalise the isolation window intensity. The default function is very generalised and just accounts for edge effects
ilim	numeric = All peaks less than this percentage of the target peak will be removed from the purity calculation, default is 5% (0.05)
sampleOnly	boolean = if TRUE will only calculate purity for sample peaklists
isotopes	boolean = TRUE if isotopes are to be removed
im	matrix = Isotope matrix, default removes C13 isotopes (single, double and triple bonds)

Value

purityD object with predicted purity of peaks

purityD object

See Also

dimsPredictPuritySingle

Examples

datapth <- system.file("extdata", "dims", "mzML", package="msPurityData")
inDF <- Getfiles(datapth, pattern=".mzML", check = FALSE, cStrt = FALSE)
ppDIMS <- purityD(fileList=inDF, cores=1, mzML=TRUE)
ppDIMS <- averageSpectra(ppDIMS)
ppDIMS <- filterp(ppDIMS)
ppDIMS <- subtract(ppDIMS)
ppDIMS <- dimsPredictPurity(ppDIMS)

msPurity documentation built on Jan. 14, 2021, 2:44 a.m.