averageIntraFragSpectra-purityA-method: Using a purityA object, average and filter fragmentation...

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

Using a purityA object, average and filter fragmentation spectra for each XCMS feature within a MS data file

Description

General

Average and filter fragmentation spectra for each XCMS feature within a MS data file.

The averaging is performed using hierarchical clustering of the m/z values of each peaks, where m/z values within a set ppm tolerance will be clustered. The clustered peaks are then averaged (or summed).

The fragmentation can be filtered on the averaged spectra (with the arguments snr, rsd, minfrac and ra)

Example LC-MS/MS processing workflow

• Purity assessments

  • (mzML files) -> purityA -> (pa)

• XCMS processing

  • (mzML files) -> xcms.xcmsSet -> xcms.merge -> xcms.group -> xcms.retcor -> xcms.group -> (xcmsObj)

• XCMS processing (version >= 3)

  • (mzML files) -> MSnBase.readMSdata -> xcms.findChromPeaks -> xcms.groupChromPeaks-> xcms.adjustRtime -> xcms.groupChromPeaks -> (xcmsObj)

• Fragmentation processing

  • (xcmsObj, pa) -> frag4feature -> filterFragSpectra -> averageIntraFragSpectra -> averageIntraFragSpectra -> createDatabase -> spectralMatching -> (sqlite spectral database)

Usage

## S4 method for signature 'purityA'
averageIntraFragSpectra(
  pa,
  minfrac = 0.5,
  minnum = 1,
  ppm = 5,
  snr = 0,
  ra = 0,
  av = "median",
  sumi = TRUE,
  rmp = FALSE,
  cores = 1
)

Arguments

pa	object; purityA object
minfrac	numeric; minimum ratio of the peak fraction (peak count / total peaks) within each file
minnum	numeric; minimum number of times peak is present across fragmentation spectra within each file
ppm	numeric; ppm threshold to average within each file
snr	numeric; minimum signal-to-noise of the peak within each file
ra	numeric; minimum relative abundance of the peak within each file
av	character; type of averaging to use (median or mean)
sumi	boolean; TRUE if the intensity for each peak is summed across averaged spectra
rmp	boolean; TRUE if peaks are to be removed that do not meet the threshold criteria. Otherwise they will just be flagged
cores	numeric; Number of cores for multiprocessing

Value

Returns a purityA object (pa) with the following slots now with data

• pa@av_spectra: the average spectra is recorded here stored as a list. e.g. "pa@av_spectra$1$av_intra$1" would give the average spectra for grouped feature 1 and for file 1.

• pa@av_intra_params: The parameters used are recorded here

Each spectra in the av_spectra list contains the following columns:

• cl: id of clustered (averaged) peak

• mz: average m/z

• i: average intensity

• snr: average signal to noise ratio

• rsd: relative standard deviation

• count: number of clustered peaks

• total: total number of potential scans to be used for averaging

• inPurity: average precursor ion purity

• ra: average relative abundance

• frac: the fraction of clustered peaks (e.g. the count/total)

• snr_pass_flag: TRUE if snr threshold criteria met

• minfrac_pass_flag: TRUE if minfrac threshold criteria

• ra_pass_flag: TRUE if ra threshold criteria met

• pass_flag: TRUE if all threshold criteria met

Examples

#====== XCMS =================================
## Read in MS data
#msmsPths <- list.files(system.file("extdata", "lcms", "mzML",
#           package="msPurityData"), full.names = TRUE, pattern = "MSMS")
#ms_data = readMSData(msmsPths, mode = 'onDisk', msLevel. = 1)

## Find peaks in each file
#cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10, peakwidth = c(3, 30))
#xcmsObj  <- xcms::findChromPeaks(ms_data, param = cwp)

## Optionally adjust retention time
#xcmsObj  <- adjustRtime(xcmsObj , param = ObiwarpParam(binSize = 0.6))

## Group features across samples
#pdp <- PeakDensityParam(sampleGroups = c(1, 1), minFraction = 0, bw = 30)
#xcmsObj <- groupChromPeaks(xcmsObj , param = pdp)

#====== msPurity ============================
#pa  <- purityA(msmsPths)
#pa <- frag4feature(pa, xcmsObj)
#pa <- averageIntraFragSpectra(pa)

# Run from previously generated data (where class is 'XCMSnExp'):
pa <- readRDS(system.file("extdata", "tests", "purityA",
              "2_frag4feature_pa.rds", package="msPurity"))
pa <- averageIntraFragSpectra(pa)

computational-metabolomics/msPurity documentation built on May 13, 2024, 7:36 p.m.