Core API function for peak density based chromatographic peak grouping

The do_groupChromPeaks_density function performs chromatographic peak grouping based on the density (distribution) of peaks, found in different samples, along the retention time axis in slices of overlapping m/z ranges. By default (with parameter ppm = 0) these m/z ranges have all the same (constant) size (depending on parameter binSize). For values of ppm larger than 0 the m/z bins (ranges or slices) will have increasing sizes depending on the m/z value. This better models the m/z-dependent measurement error/precision seen on some MS instruments.

Usage

do_groupChromPeaks_density(
  peaks,
  sampleGroups,
  bw = 30,
  minFraction = 0.5,
  minSamples = 1,
  binSize = 0.25,
  maxFeatures = 50,
  sleep = 0,
  index = seq_len(nrow(peaks)),
  ppm = 0
)

Arguments

peaks: A matrix or data.frame with the mz values and retention times of the identified chromatographic peaks in all samples of an experiment. Required columns are "mz", "rt" and "sample". The latter should contain numeric values representing the index of the sample in which the peak was found.
sampleGroups: For PeakDensityParam: A vector of the same length than samples defining the sample group assignments (i.e. which samples belong to which sample group). This parameter is mandatory for the PeakDensityParam and has to be provided also if there is no sample grouping in the experiment (in which case all samples should be assigned to the same group).
bw: For PeakDensityParam: numeric(1) defining the bandwidth (standard deviation ot the smoothing kernel) to be used. This argument is passed to the [density() method.
minFraction: For PeakDensityParam: numeric(1) defining the minimum fraction of samples in at least one sample group in which the peaks have to be present to be considered as a peak group (feature).
minSamples: For PeakDensityParam: numeric(1) with the minimum number of samples in at least one sample group in which the peaks have to be detected to be considered a peak group (feature).
binSize: For PeakDensityParam: numeric(1) defining the size of the overlapping slices in m/z dimension.
maxFeatures: For PeakDensityParam: numeric(1) with the maximum number of peak groups to be identified in a single mz slice.
sleep: numeric(1) defining the time to sleep between iterations and plot the result from the current iteration.
index: An optional integer providing the indices of the peaks in the original peak matrix.
ppm: For MzClustParam: numeric(1) representing the relative m/z error for the clustering/grouping (in parts per million). For PeakDensityParam: numeric(1) to define m/z-dependent, increasing m/z bin sizes. If ppm = 0 (the default) m/z bins are defined by the sequence of values from the smallest to the larges m/z value with a constant bin size of binSize. For ppm > 0 the size of each bin is increased in addition by the ppm of the (upper) m/z boundary of the bin. The maximal bin size (used for the largest m/z values) would then be binSize plus ppm parts-per-million of the largest m/z value of all peaks in the data set.

Value

A data.frame, each row representing a (mz-rt) feature (i.e. a peak group) with columns:

"mzmed": median of the peaks' apex mz values.
"mzmin": smallest mz value of all peaks' apex within the feature.
"mzmax":largest mz value of all peaks' apex within the feature.
"rtmed": the median of the peaks' retention times.
"rtmin": the smallest retention time of the peaks in the group.
"rtmax": the largest retention time of the peaks in the group.
"npeaks": the total number of peaks assigned to the feature.
"peakidx": a list with the indices of all peaks in a feature in the peaks input matrix.

Note that this number can be larger than the total number of samples, since multiple peaks from the same sample could be assigned to a feature.

Details

For overlapping slices along the mz dimension, the function calculates the density distribution of identified peaks along the retention time axis and groups peaks from the same or different samples that are close to each other. See (Smith 2006) for more details.

Note

The default settings might not be appropriate for all LC/GC-MS setups, especially the bw and binSize parameter should be adjusted accordingly.

References

Colin A. Smith, Elizabeth J. Want, Grace O'Maille, Ruben Abagyan and Gary Siuzdak. "XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification" Anal. Chem. 2006, 78:779-787.

Author

Colin Smith, Johannes Rainer

Examples

## Load the test file
library(xcms)
library(MsExperiment)
faahko_sub <- loadXcmsData("faahko_sub2")

## Disable parallel processing for this example
register(SerialParam())

## Extract the matrix with the identified peaks from the xcmsSet:
pks <- chromPeaks(faahko_sub)

## Perform the peak grouping with default settings:
res <- do_groupChromPeaks_density(pks, sampleGroups = rep(1, 3))

## The feature definitions:
head(res)
#>   mzmed mzmin mzmax    rtmed    rtmin    rtmax npeaks 1      peakidx
#> 1 279.0 279.0 279.0 2787.765 2787.765 2787.766      2 2      11, 199
#> 2 286.2 286.2 286.2 3254.904 3250.992 3258.815      2 2     115, 205
#> 3 300.2 300.2 300.2 3387.143 3379.317 3390.271      4 3 35, 125,....
#> 4 301.0 301.0 301.0 2787.766 2786.200 2792.459      3 3  10, 97, 198
#> 5 305.1 305.1 305.1 2994.338 2994.338 2994.339      2 2      15, 203
#> 6 305.1 305.1 305.1 2923.917 2923.916 2923.917      2 2      14, 202