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Identify peaks in chromatographic data using centWave

Source: R/do_findChromPeaks-functions.R
peaksWithCentWave.Rd

peaksWithCentWave identifies (chromatographic) peaks in purely chromatographic data, i.e. based on intensity and retention time values without m/z values.

Usage

peaksWithCentWave(
  int,
  rt,
  peakwidth = c(20, 50),
  snthresh = 10,
  prefilter = c(3, 100),
  integrate = 1,
  fitgauss = FALSE,
  noise = 0,
  verboseColumns = FALSE,
  firstBaselineCheck = TRUE,
  extendLengthMSW = FALSE,
  ...
)

Arguments

int

numeric with intensity values.

rt

numeric with the retention time for the intensities. Length has to be equal to length(int).

peakwidth

numeric(2) with the lower and upper bound of the expected peak width.

snthresh

numeric(1) defining the signal to noise ratio cutoff. Peaks with a signal to noise ratio < snthresh are omitted.

prefilter

numeric(2) (c(k, I)): only regions of interest with at least k centroids with signal >= I are returned in the first step.

integrate

numeric(1), integration method. For integrate = 1 peak limits are found through descending on the mexican hat filtered data, for integrate = 2 the descend is done on the real data. The latter method is more accurate but prone to noise, while the former is more robust, but less exact.

fitgauss

logical(1) whether or not a Gaussian should be fitted to each peak.

noise

numeric(1) defining the minimum required intensity for centroids to be considered in the first analysis step (definition of the regions of interest).

verboseColumns

logical(1): whether additional peak meta data columns should be returned.

firstBaselineCheck

logical(1). If TRUE continuous data within regions of interest is checked to be above the first baseline. In detail, a first rough estimate of the noise is calculated and peak detection is performed only in regions in which multiple sequential signals are higher than this first estimated baseline/noise level.

extendLengthMSW

logical(1). If TRUE the "open" method of EIC extension is used, rather than the default "reflect" method.

...

currently ignored.

Value

A matrix, each row representing an identified chromatographic peak, with columns:

  • "rt": retention time of the peak's midpoint (time of the maximum signal).

  • "rtmin": minimum retention time of the peak.

  • "rtmax": maximum retention time of the peak.

  • "into": integrated (original) intensity of the peak.

  • "intb": per-peak baseline corrected integrated peak intensity.

  • "maxo": maximum (original) intensity of the peak.

  • "sn": signal to noise ratio of the peak defined as (maxo - baseline)/sd with sd being the standard deviation of the local chromatographic noise.

Additional columns for verboseColumns = TRUE:

  • "mu": gaussian parameter mu.

  • "sigma": gaussian parameter sigma.

  • "h": gaussian parameter h.

  • "f": region number of the m/z ROI where the peak was localized.

  • "dppm": m/z deviation of mass trace across scans in ppm (always NA).

  • "scale": scale on which the peak was localized.

  • "scpos": peak position found by wavelet analysis (index in int).

  • "scmin": left peak limit found by wavelet analysis (index in int).

  • "scmax": right peak limit found by wavelet analysis (index in int).

Details

The method uses the same algorithm for the peak detection than centWave, employs however a different approach to identify the initial regions in which the peak detection is performed (i.e. the regions of interest ROI). The method first identifies all local maxima in the chromatographic data and defines the corresponding positions +/- peakwidth[2] as the ROIs. Noise estimation bases also on these ROIs and can thus be different from centWave resulting in different signal to noise ratios.

See also

centWave for a detailed description of the peak detection method.

Other peak detection functions for chromatographic data: peaksWithMatchedFilter()

Author

Johannes Rainer

Examples


## Reading a file
library(MsExperiment)
library(xcms)
od <- readMsExperiment(system.file("cdf/KO/ko15.CDF", package = "faahKO"))

## Extract chromatographic data for a small m/z range
mzr <- c(272.1, 272.2)
chr <- chromatogram(od, mz = mzr, rt = c(3000, 3300))[1, 1]

int <- intensity(chr)
rt <- rtime(chr)

## Plot the region
plot(chr, type = "h")

## Identify peaks in the chromatographic data
pks <- peaksWithCentWave(intensity(chr), rtime(chr))
pks
#>            rt    rtmin    rtmax     into     intb  maxo sn
#> [1,] 3119.535 3097.625 3127.359 247211.6 227217.9 16496 24

## Highlight the peaks
rect(xleft = pks[, "rtmin"], xright = pks[, "rtmax"],
    ybottom = rep(0, nrow(pks)), ytop = pks[, "maxo"], col = "#ff000040",
    border = "#00000040")