do_findChromPeaks_centWave: Core API function for centWave peak detection

In xiaodfeng/DynamicXCMS: LC-MS and GC-MS Data Analysis

Core API function for centWave peak detection

Description

This function performs peak density and wavelet based chromatographic peak detection for high resolution LC/MS data in centroid mode [Tautenhahn 2008].

Usage

do_findChromPeaks_centWave(mz, int, scantime, valsPerSpect, ppm = 25,
  peakwidth = c(20, 50), snthresh = 10, prefilter = c(3, 100),
  mzCenterFun = "wMean", integrate = 1, mzdiff = -0.001,
  fitgauss = FALSE, noise = 0, verboseColumns = FALSE,
  roiList = list(), firstBaselineCheck = TRUE, roiScales = NULL,
  sleep = 0)

Arguments

mz	Numeric vector with the individual m/z values from all scans/ spectra of one file/sample.
int	Numeric vector with the individual intensity values from all scans/spectra of one file/sample.
scantime	Numeric vector of length equal to the number of spectra/scans of the data representing the retention time of each scan.
valsPerSpect	Numeric vector with the number of values for each spectrum.
ppm	numeric(1) defining the maximal tolerated m/z deviation in consecutive scans in parts per million (ppm) for the initial ROI definition.
peakwidth	numeric(2) with the expected approximate peak width in chromatographic space. Given as a range (min, max) in seconds.
snthresh	numeric(1) defining the signal to noise ratio cutoff.
prefilter	numeric(2): c(k, I) specifying the prefilter step for the first analysis step (ROI detection). Mass traces are only retained if they contain at least k peaks with intensity >= I.
mzCenterFun	Name of the function to calculate the m/z center of the chromatographic peak. Allowed are: "wMean": intensity weighted mean of the peak's m/z values, "mean": mean of the peak's m/z values, "apex": use the m/z value at the peak apex, "wMeanApex3": intensity weighted mean of the m/z value at the peak apex and the m/z values left and right of it and "meanApex3": mean of the m/z value of the peak apex and the m/z values left and right of it.
integrate	Integration method. For integrate = 1 peak limits are found through descent on the mexican hat filtered data, for integrate = 2 the descent 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.
mzdiff	numeric(1) representing the minimum difference in m/z dimension required for peaks with overlapping retention times; can be negative to allow overlap. During peak post-processing, peaks defined to be overlapping are reduced to the one peak with the largest signal.
fitgauss	logical(1) whether or not a Gaussian should be fitted to each peak. This affects mostly the retention time position of the peak.
noise	numeric(1) allowing to set a minimum intensity required for centroids to be considered in the first analysis step (centroids with intensity < noise are omitted from ROI detection).
verboseColumns	logical(1) whether additional peak meta data columns should be returned.
roiList	An optional list of regions-of-interest (ROI) representing detected mass traces. If ROIs are submitted the first analysis step is omitted and chromatographic peak detection is performed on the submitted ROIs. Each ROI is expected to have the following elements specified: scmin (start scan index), scmax (end scan index), mzmin (minimum m/z), mzmax (maximum m/z), length (number of scans), intensity (summed intensity). Each ROI should be represented by a list of elements or a single row data.frame.
firstBaselineCheck	logical(1). If TRUE continuous data within regions of interest is checked to be above the first baseline.
roiScales	Optional numeric vector with length equal to roiList defining the scale for each region of interest in roiList that should be used for the centWave-wavelets.
sleep	numeric(1) defining the number of seconds to wait between iterations. Defaults to sleep = 0. If > 0 a plot is generated visualizing the identified chromatographic peak. Note: this argument is for backward compatibility only and will be removed in future.

Details

This algorithm is most suitable for high resolution LC/{TOF,OrbiTrap,FTICR}-MS data in centroid mode. In the first phase the method identifies regions of interest (ROIs) representing mass traces that are characterized as regions with less than ppm m/z deviation in consecutive scans in the LC/MS map. In detail, starting with a single m/z, a ROI is extended if a m/z can be found in the next scan (spectrum) for which the difference to the mean m/z of the ROI is smaller than the user defined ppm of the m/z. The mean m/z of the ROI is then updated considering also the newly included m/z value.

These ROIs are then, after some cleanup, analyzed using continuous wavelet transform (CWT) to locate chromatographic peaks on different scales. The first analysis step is skipped, if regions of interest are passed with the roiList parameter.

Value

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

mz

Intensity weighted mean of m/z values of the peak across scans.

mzmin

Minimum m/z of the peak.

mzmax

Maximum m/z of the peak.

rt

Retention time of the peak's midpoint.

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 intensity of the peak.

sn

Signal to noise ratio, defined as (maxo - baseline)/sd, sd being the standard deviation of local chromatographic noise.

egauss

RMSE of Gaussian fit.

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.

scale

Scale on which the peak was localized.

scpos

Peak position found by wavelet analysis (scan number).

scmin

Left peak limit found by wavelet analysis (scan number).

scmax

Right peak limit found by wavelet analysis (scan numer).

Note

The centWave was designed to work on centroided mode, thus it is expected that such data is presented to the function.

This function exposes core chromatographic peak detection functionality of the centWave method. While this function can be called directly, users will generally call the corresponding method for the data object instead.

Author(s)

Ralf Tautenhahn, Johannes Rainer

References

Ralf Tautenhahn, Christoph B\"ottcher, and Steffen Neumann "Highly sensitive feature detection for high resolution LC/MS" BMC Bioinformatics 2008, 9:504

See Also

centWave for the standard user interface method.

Other core peak detection functions: do_findChromPeaks_centWaveWithPredIsoROIs, do_findChromPeaks_massifquant, do_findChromPeaks_matchedFilter, do_findPeaks_MSW

Examples

## Load the test file
library(faahKO)
fs <- system.file('cdf/KO/ko15.CDF', package = "faahKO")
xr <- xcmsRaw(fs, profstep = 0)

## Extracting the data from the xcmsRaw for do_findChromPeaks_centWave
mzVals <- xr@env$mz
intVals <- xr@env$intensity
## Define the values per spectrum:
valsPerSpect <- diff(c(xr@scanindex, length(mzVals)))

## Calling the function. We're using a large value for noise to speed up
## the call in the example performance - in a real use case we would either
## set the value to a reasonable value or use the default value.
res <- do_findChromPeaks_centWave(mz = mzVals, int = intVals,
scantime = xr@scantime, valsPerSpect = valsPerSpect, noise = 10000)
head(res)

xiaodfeng/DynamicXCMS documentation built on Aug. 6, 2023, 3:02 p.m.