filterFragSpectra,purityA-method | R Documentation |
General
Flag and filter features based on signal-to-noise ratio, relative abundance, intensity threshold and purity of the precursor ion.
Example LC-MS/MS processing workflow
Purity assessments
(mzML files) -> purityA -> (pa)
XCMS processing
(mzML files) -> xcms.findChromPeaks -> (optionally) xcms.adjustRtime -> xcms.groupChromPeaks -> (xcmsObj)
— Older versions of XCMS — (mzML files) -> xcms.xcmsSet -> xcms.group -> xcms.retcor -> xcms.group -> (xcmsObj)
Fragmentation processing
(xcmsObj, pa) -> frag4feature -> filterFragSpectra -> averageAllFragSpectra -> createDatabase -> spectralMatching -> (sqlite spectral database)
## S4 method for signature 'purityA' filterFragSpectra( pa, ilim = 0, plim = 0.8, ra = 0, snr = 3, rmp = FALSE, snmeth = "median", allfrag = FALSE )
pa |
object; purityA object |
ilim |
numeric; min intensity of a peak |
plim |
numeric; min precursor ion purity of the associated precursor for fragmentation spectra scan |
ra |
numeric; minimum relative abundance of a peak |
snr |
numeric; minimum signal-to-noise of a peak within each file |
rmp |
boolean; TRUE if peaks are to be removed that do not meet the threshold criteria. Otherwise they will just be flagged. |
snmeth |
character; Method to calculate signal to noise ration (either median or mean) |
allfrag |
boolean; Whether to filter on all fragmentation spectra or just the fragmentation spectra grouped to XCMS feature |
Returns a purityA object with the pa@grped_msms spectra matrices are updated with the following columns
snr: Signal to noise ratio (calculated at scan level)
ra: Relative abundance (calculated at scan level)
purity_pass_flag: Precursor ion purity flag (1 pass, 0 fail)
intensity_pass_flag: Intensity flag (1 pass, 0 fail)
snr_pass_flag: Signal-to-noise pass flag (1 pass, 0 fail)
ra_pass_flag: Relative abundance pass flag (1 pass, 0 fail)
pass_flag: Overall pass flag, all flags must pass for this to pass (1 pass, 0 fail)
#====== 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 <- filterFragSpectra(pa) ## Run from previously generated data pa <- readRDS(system.file("extdata", "tests", "purityA", "2_frag4feature_pa.rds", package="msPurity")) pa <- filterFragSpectra(pa)
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