MS Data Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

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

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

`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.
`...`	currently ignored.

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.

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 defiatio 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).

Johannes Rainer

centWave for a detailed description of the peak detection method.

Other peak detection functions for chromatographic data: peaksWithMatchedFilter

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

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

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