groupChromPeaks-mzClust {xcms} | R Documentation

## High resolution peak grouping for single spectra samples

### Description

This method performs high resolution correspondence for single spectra samples.

The `MzClustParam` class allows to specify all settings for the peak grouping based on the mzClust algorithm. Instances should be created with the `MzClustParam` constructor.

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

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

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

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

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

`groupChromPeaks,XCMSnExp,MzClustParam`: performs high resolution peak grouping for single spectrum metabolomics data.

### Usage

    MzClustParam(sampleGroups = numeric(), ppm = 20, absMz = 0,
      minFraction = 0.5, minSamples = 1)

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

    ## S4 method for signature 'MzClustParam'
    sampleGroups(object)

    ## S4 replacement method for signature 'MzClustParam'
    sampleGroups(object) <- value

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

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

    ## S4 method for signature 'MzClustParam'
    absMz(object)

    ## S4 replacement method for signature 'MzClustParam'
    absMz(object) <- value

    ## S4 method for signature 'MzClustParam'
    minFraction(object)

    ## S4 replacement method for signature 'MzClustParam'
    minFraction(object) <- value

    ## S4 method for signature 'MzClustParam'
    minSamples(object)

    ## S4 replacement method for signature 'MzClustParam'
    minSamples(object) <- value

    ## S4 method for signature 'XCMSnExp,MzClustParam'
    groupChromPeaks(object, param)

### Arguments

`sampleGroups` | 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).
`ppm` | `numeric(1)` representing the relative mz error for the clustering/grouping (in parts per million).
`absMz` | `numeric(1)` representing the absolute mz error for the clustering.
`minFraction` | `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` | `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).
`object` | For `groupChromPeaks`: an `XCMSnExp` object containing the results from a previous chromatographic peak detection analysis (see `findChromPeaks`). For all other methods: a `MzClustParam` object.
`value` | The value for the slot.
`param` | A `MzClustParam` object containing all settings for the peak grouping algorithm.

### Value

The `MzClustParam` function returns a `MzClustParam` class instance with all of the settings specified for high resolution single spectra peak alignment.

For `groupChromPeaks`: a `XCMSnExp` object with the results of the peak grouping step (i.e. the features). These can be accessed with the `featureDefinitions` method.

### Slots

`.__classVersion__,sampleGroups,ppm,absMz,minFraction,minSamples`

  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 `group` methods. All of the settings to the algorithm can be passed with a `MzClustParam` object.

Calling `groupChromPeaks` on an `XCMSnExp` object will cause all eventually present previous correspondence results to be dropped.

### References

Saira A. Kazmi, Samiran Ghosh, Dong-Guk Shin, Dennis W. Hill and David F. Grant
Alignment of high resolution mass spectra: development of a heuristic approach for metabolomics.
Metabolomics, Vol. 2, No. 2, 75-83 (2006)

### See Also

The `do_groupPeaks_mzClust` core API function and `group.mzClust` for the old user interface. `featureDefinitions` and `featureValues,XCMSnExp-method` for methods to access peak grouping results (i.e. the features).

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

Other peak grouping methods: `groupChromPeaks-density`, `groupChromPeaks-nearest`, `groupChromPeaks`

### Examples

    ## Loading a small subset of direct injection, single spectrum files
    library(msdata)
    fticrf <- list.files(system.file("fticr", package = "msdata"),
                        recursive = TRUE, full.names = TRUE)
    fticr <- readMSData(fticrf[1:2], msLevel. = 1, mode = "onDisk")

    ## Perform the MSW peak detection on these:
    p <- MSWParam(scales = c(1, 7), peakThr = 80000, ampTh = 0.005,
                 SNR.method = "data.mean", winSize.noise = 500)
    fticr <- findChromPeaks(fticr, param = p)

    head(chromPeaks(fticr))

    ## Now create the MzClustParam parameter object: we're assuming here that
    ## both samples are from the same sample group.
    p <- MzClustParam(sampleGroups = c(1, 1))

    fticr <- groupChromPeaks(fticr, param = p)

    ## Get the definition of the features.
    featureDefinitions(fticr)

[Package xcms version 3.2.0 Index]