dimsPredictPurity,purityD-method {msPurity}R Documentation

Using purityD object, assess anticipated purity from a DI-MS run

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)

[Package msPurity version 1.10.0 Index]