Core API function for centWave peak detection

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,
  extendLengthMSW = FALSE,
  verboseBetaColumns = FALSE
)

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. 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.
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.
extendLengthMSW: Option to force centWave to use all scales when running centWave rather than truncating with the EIC length. Uses the "open" method to extend the EIC to a integer base-2 length prior to being passed to convolve rather than the default "reflect" method. See https://github.com/sneumann/xcms/issues/445 for more information.
verboseBetaColumns: Option to calculate two additional metrics of peak quality via comparison to an idealized bell curve. Adds beta_cor and beta_snr to the chromPeaks output, corresponding to a Pearson correlation coefficient to a bell curve with several degrees of skew as well as an estimate of signal-to-noise using the residuals from the best-fitting bell curve. See https://github.com/sneumann/xcms/pull/685 and https://doi.org/10.1186/s12859-023-05533-4 for more information.

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).

Additional columns for verboseBetaColumns = TRUE:

beta_cor: Correlation between an "ideal" bell curve and the raw data
beta_snr: Signal-to-noise residuals calculated from the beta_cor fit

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.

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.

References

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

Author

Ralf Tautenhahn, Johannes Rainer

Examples

## Load the test file
faahko_sub <- loadXcmsData("faahko_sub")

## Subset to one file and restrict to a certain retention time range
data <- filterRt(filterFile(faahko_sub, 1), c(2500, 3000))

## Get m/z and intensity values
mzs <- mz(data)
ints <- intensity(data)

## Define the values per spectrum:
valsPerSpect <- lengths(mzs)

## Calling the function. We're using a large value for noise and prefilter
## to speed up the call in the example - 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 = unlist(mzs), int = unlist(ints),
    scantime = rtime(data), valsPerSpect = valsPerSpect, noise = 10000,
    prefilter = c(3, 10000))
#> Detecting mass traces at 25 ppm ... 
#> OK
#> Detecting chromatographic peaks in 186 regions of interest ...
#>  OK: 47 found.
head(res)
#>         mz mzmin mzmax       rt    rtmin    rtmax      into      intb   maxo
#> [1,] 453.2 453.2 453.2 2506.073 2501.378 2527.982 1007409.0 1007380.8  38152
#> [2,] 307.0 307.0 307.0 2618.750 2592.145 2645.354  284782.4  268039.8  16872
#> [3,] 302.0 302.0 302.0 2617.185 2595.275 2640.659  687146.6  671297.8  30552
#> [4,] 360.0 360.0 360.0 2682.913 2668.828 2698.562 5641322.3 5420634.7 317568
#> [5,] 361.1 361.1 361.1 2684.478 2665.698 2698.562 1158340.2 1116522.0  72272
#> [6,] 416.1 416.1 416.1 2682.913 2635.964 2709.517  487698.6  446552.1  12036
#>         sn
#> [1,] 38151
#> [2,]    20
#> [3,]    46
#> [4,]    11
#> [5,]    11
#> [6,]    11