Chromatographic peak detection using the centWave method

The centWave algorithm perform peak density and wavelet based chromatographic peak detection for high resolution LC/MS data in centroid mode Tautenhahn 2008.

The findChromPeaks,OnDiskMSnExp,CentWaveParam() method performs chromatographic peak detection using the centWave algorithm on all samples from an OnDiskMSnExp object. OnDiskMSnExp objects encapsule all experiment specific data and load the spectra data (mz and intensity values) on the fly from the original files applying also all eventual data manipulations.

Usage

CentWaveParam(
  ppm = 25,
  peakwidth = c(20, 50),
  snthresh = 10,
  prefilter = c(3, 100),
  mzCenterFun = "wMean",
  integrate = 1L,
  mzdiff = -0.001,
  fitgauss = FALSE,
  noise = 0,
  verboseColumns = FALSE,
  roiList = list(),
  firstBaselineCheck = TRUE,
  roiScales = numeric(),
  extendLengthMSW = FALSE,
  verboseBetaColumns = FALSE
)

# S4 method for class 'OnDiskMSnExp,CentWaveParam'
findChromPeaks(
  object,
  param,
  BPPARAM = bpparam(),
  return.type = "XCMSnExp",
  msLevel = 1L,
  ...
)

# S4 method for class 'CentWaveParam'
as.list(x, ...)

Arguments

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.

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.

object

For findChromPeaks(): an MSnbase::OnDiskMSnExp() object containing the MS- and all other experiment-relevant data.

For all other methods: a parameter object.

param

An CentWaveParam() object containing all settings for the centWave algorithm.

BPPARAM

A parameter class specifying if and how parallel processing should be performed. It defaults to BiocParallel::bpparam(). See documentation of the BiocParallel package for more details. If parallel processing is enabled, peak detection is performed in parallel on several of the input samples.

return.type

Character specifying what type of object the method should return. Can be either "XCMSnExp" (default), "list" or "xcmsSet".

msLevel

integer(1) defining the MS level on which the peak detection should be performed. Defaults to msLevel = 1.

...

ignored.

x

The parameter object.

Value

The CentWaveParam() function returns a CentWaveParam class instance with all of the settings specified for chromatographic peak detection by the centWave method.

For findChromPeaks(): if return.type = "XCMSnExp" an XCMSnExp() object with the results of the peak detection. If return.type = "list" a list of length equal to the number of samples with matrices specifying the identified peaks. If return.type = "xcmsSet" an xcmsSet object with the results of the peak detection.

Details

The centWave 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 via the param parameter.

Parallel processing (one process per sample) is supported and can be configured either by the BPPARAM parameter or by globally defining the parallel processing mode using the BiocParallel::register() method from the BiocParallel package.

Note

These methods and classes are part of the updated and modernized xcms user interface which will eventually replace the findPeaks() methods.

References

Ralf Tautenhahn, Christoph Böttcher, and Steffen Neumann "Highly sensitive feature detection for high resolution LC/MS" BMC Bioinformatics 2008, 9:504 doi: 10.1186/1471-2105-9-504

Author

Ralf Tautenhahn, Johannes Rainer

Examples


## Create a CentWaveParam object. Note that the noise is set to 10000 to
## speed up the execution of the example - in a real use case the default
## value should be used, or it should be set to a reasonable value.
cwp <- CentWaveParam(ppm = 25, noise = 10000, prefilter = c(3, 10000))
cwp
#> Object of class:  CentWaveParam 
#>  Parameters:
#>  - ppm: [1] 25
#>  - peakwidth: [1] 20 50
#>  - snthresh: [1] 10
#>  - prefilter: [1]     3 10000
#>  - mzCenterFun: [1] "wMean"
#>  - integrate: [1] 1
#>  - mzdiff: [1] -0.001
#>  - fitgauss: [1] FALSE
#>  - noise: [1] 10000
#>  - verboseColumns: [1] FALSE
#>  - roiList: list()
#>  - firstBaselineCheck: [1] TRUE
#>  - roiScales: numeric(0)
#>  - extendLengthMSW: [1] FALSE
#>  - verboseBetaColumns: [1] FALSE

## Perform the peak detection using centWave on some of the files from the
## faahKO package. Files are read using the `readMsExperiment` function
## from the MsExperiment package
library(faahKO)
library(xcms)
library(MsExperiment)
fls <- dir(system.file("cdf/KO", package = "faahKO"), recursive = TRUE,
           full.names = TRUE)
raw_data <- readMsExperiment(fls[1])

## Perform the peak detection using the settings defined above.
res <- findChromPeaks(raw_data, param = cwp)
head(chromPeaks(res))
#>          mz mzmin mzmax       rt    rtmin    rtmax      into      intb   maxo
#> CP001 453.2 453.2 453.2 2506.073 2501.378 2527.982 1007409.0 1007380.8  38152
#> CP002 307.0 307.0 307.0 2618.750 2592.145 2645.354  284782.4  268039.8  16872
#> CP003 302.0 302.0 302.0 2617.185 2595.275 2640.659  687146.6  671297.8  30552
#> CP004 360.0 360.0 360.0 2682.913 2668.828 2698.562 5641322.3 5420634.7 317568
#> CP005 361.1 361.1 361.1 2684.478 2665.698 2698.562 1158340.2 1116522.0  72272
#> CP006 416.1 416.1 416.1 2682.913 2635.964 2709.517  487698.6  446552.1  12036
#>          sn sample
#> CP001 38151      1
#> CP002    20      1
#> CP003    46      1
#> CP004    11      1
#> CP005    11      1
#> CP006    11      1