findChromPeaks-centWave {xcms} | R Documentation

## Chromatographic peak detection using the centWave method

### Description

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 `detectChromPeaks,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())

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

    ## S4 method for signature 'CentWaveParam'
    show(object)

    ## S4 method for signature 'CentWaveParam'
    ppm(object)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

### 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 for peaks with overlapping retention times; can be negative to allow overlap.
`fitgauss` | `logical(1)` whether or not a Gaussian should be fitted to each 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.
`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`.
`value` | The value for the slot.
`f` | For `integrate`: a `CentWaveParam` object.

### 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. These ROIs are then subsequently 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.

### 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.

### Slots

`.__classVersion__,ppm,peakwidth,snthresh,prefilter,mzCenterFun,integrate,mzdiff,fitgauss,noise,verboseColumns,roiList,firstBaselineCheck,roiScales`

  See corresponding parameter above. `.__classVersion__` stores the version from the class. 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 `MSnExp` and `OnDiskMSnExp` objects (both defined in the `MSnbase` package). All of the settings to the centWave algorithm can be passed with a `CentWaveParam` object.

### 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

The `do_findChromPeaks_centWave` core API function and `findPeaks.centWave` for the old user interface.

`XCMSnExp` for the object containing the results of the peak detection.

Other peak detection methods: `chromatographic-peak-detection`, `findChromPeaks-centWaveWithPredIsoROIs`, `findChromPeaks-massifquant`, `findChromPeaks-matchedFilter`, `findPeaks-MSW`

### 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)
    ## Change snthresh parameter
    snthresh(cwp) <- 25
    cwp

    ## Perform the peak detection using centWave on some of the files from the
    ## faahKO package. Files are read using the readMSData from the MSnbase
    ## package
    library(faahKO)
    library(xcms)
    fls <- dir(system.file("cdf/KO", package = "faahKO"), recursive = TRUE,
               full.names = TRUE)
    raw_data <- readMSData(fls[1:2], mode = "onDisk")

    ## Perform the peak detection using the settings defined above.
    res <- findChromPeaks(raw_data, param = cwp)
    head(chromPeaks(res))

[Package xcms version 3.2.0 Index]