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 CentWaveParam class allows to specify all settings for a chromatographic peak detection using the centWave method. Instances should be created with the CentWaveParam constructor.

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.

ppm,ppm<-: getter and setter for the ppm slot of the object.

peakwidth,peakwidth<-: getter and setter for the peakwidth slot of the object.

snthresh,snthresh<-: getter and setter for the snthresh slot of the object.

prefilter,prefilter<-: getter and setter for the prefilter slot of the object.

mzCenterFun,mzCenterFun<-: getter and setter for the mzCenterFun slot of the object.

integrate,integrate<-: getter and setter for the integrate slot of the object.

mzdiff,mzdiff<-: getter and setter for the mzdiff slot of the object.

fitgauss,fitgauss<-: getter and setter for the fitgauss slot of the object.

noise,noise<-: getter and setter for the noise slot of the object.

verboseColumns,verboseColumns<-: getter and setter for the verboseColumns slot of the object.

roiList,roiList<-: getter and setter for the roiList slot of the object.

fistBaselineCheck,firstBaselineCheck<-: getter and setter for the firstBaselineCheck slot of the object.

roiScales,roiScales<-: getter and setter for the roiScales slot of the object.

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'
ppm(object)

# S4 method for class 'CentWaveParam'
ppm(object) <- value

# S4 method for class 'CentWaveParam'
peakwidth(object)

# S4 method for class 'CentWaveParam'
peakwidth(object) <- value

# S4 method for class 'CentWaveParam'
snthresh(object)

# S4 method for class 'CentWaveParam'
snthresh(object) <- value

# S4 method for class 'CentWaveParam'
prefilter(object)

# S4 method for class 'CentWaveParam'
prefilter(object) <- value

# S4 method for class 'CentWaveParam'
mzCenterFun(object)

# S4 method for class 'CentWaveParam'
mzCenterFun(object) <- value

# S4 method for class 'CentWaveParam'
integrate(f)

# S4 method for class 'CentWaveParam'
integrate(object) <- value

# S4 method for class 'CentWaveParam'
mzdiff(object)

# S4 method for class 'CentWaveParam'
mzdiff(object) <- value

# S4 method for class 'CentWaveParam'
fitgauss(object)

# S4 method for class 'CentWaveParam'
fitgauss(object) <- value

# S4 method for class 'CentWaveParam'
noise(object)

# S4 method for class 'CentWaveParam'
noise(object) <- value

# S4 method for class 'CentWaveParam'
verboseColumns(object)

# S4 method for class 'CentWaveParam'
verboseColumns(object) <- value

# S4 method for class 'CentWaveParam'
roiList(object)

# S4 method for class 'CentWaveParam'
roiList(object) <- value

# S4 method for class 'CentWaveParam'
firstBaselineCheck(object)

# S4 method for class 'CentWaveParam'
firstBaselineCheck(object) <- value

# S4 method for class 'CentWaveParam'
roiScales(object)

# S4 method for class 'CentWaveParam'
roiScales(object) <- value

# 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 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 bpparam. See documentation of the BiocParallel 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.

value

The value for the slot.

f

For integrate: a CentWaveParam object.

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 register method from the BiocParallel package.

Slots

ppm,peakwidth,snthresh,prefilter,mzCenterFun,integrate,mzdiff,fitgauss,noise,verboseColumns,roiList,firstBaselineCheck,roiScales,extendLengthMSW,verboseBetaColumns: See corresponding parameter above. Slots values should exclusively be accessed via the corresponding getter and setter methods listed above.

Note

These methods and classes are part of the updated and modernized xcms user interface which will eventually replace the findPeaks methods. It supports peak detection on OnDiskMSnExp objects (defined in the MSnbase package). All of the settings to the centWave algorithm can be passed with a CentWaveParam object.

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


## 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 = 20, noise = 10000, prefilter = c(3, 10000))
## Change snthresh parameter
snthresh(cwp) <- 25
cwp
#> Object of class:  CentWaveParam 
#>  Parameters:
#>  - ppm: [1] 20
#>  - peakwidth: [1] 20 50
#>  - snthresh: [1] 25
#>  - 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 302.0 302.0 302.0 2617.185 2595.275 2640.659  687146.6  671297.8  30552
#> CP003 344.0 344.0 344.0 2679.783 2646.919 2709.517 5210015.9 5135916.9 152320
#> CP004 381.0 381.0 381.0 2678.218 2637.529 2720.472 2180565.2 2023571.9  52504
#> CP005 430.1 430.1 430.1 2681.348 2639.094 2712.647 2395840.3 2299899.6  65752
#> CP006 366.0 366.0 366.0 2679.783 2642.224 2718.907 3365174.0 3279468.3  79928
#>          sn sample
#> CP001 38151      1
#> CP002    46      1
#> CP003    68      1
#> CP004    37      1
#> CP005    42      1
#> CP006    49      1