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 1.20.0 Index]