findChromPeaks-massifquant {xcms} | R Documentation

## Chromatographic peak detection using the massifquant method

### Description

Massifquant is a Kalman filter (KF)-based chromatographic peak detection for XC-MS data in centroid mode. The identified peaks can be further refined with the centWave method (see `findChromPeaks-centWave` for details on centWave) by specifying `withWave = TRUE`.

The `MassifquantParam` class allows to specify all settings for a chromatographic peak detection using the massifquant method eventually in combination with the centWave algorithm. Instances should be created with the `MassifquantParam` constructor.

The `findChromPeaks,OnDiskMSnExp,MassifquantParam` method performs chromatographic peak detection using the massifquant 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.

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

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

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

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

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

### Usage

    MassifquantParam(
      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,
      criticalValue = 1.125,
      consecMissedLimit = 2,
      unions = 1,
      checkBack = 0,
      withWave = FALSE
    )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    ## S4 method for signature 'MassifquantParam'
    criticalValue(object)

    ## S4 replacement method for signature 'MassifquantParam'
    criticalValue(object) <- value

    ## S4 method for signature 'MassifquantParam'
    consecMissedLimit(object)

    ## S4 replacement method for signature 'MassifquantParam'
    consecMissedLimit(object) <- value

    ## S4 method for signature 'MassifquantParam'
    unions(object)

    ## S4 replacement method for signature 'MassifquantParam'
    unions(object) <- value

    ## S4 method for signature 'MassifquantParam'
    checkBack(object)

    ## S4 replacement method for signature 'MassifquantParam'
    checkBack(object) <- value

    ## S4 method for signature 'MassifquantParam'
    withWave(object)

    ## S4 replacement method for signature 'MassifquantParam'
    withWave(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)`. Only the first element is used by massifquant, which specifices the minimum peak length in time scans. For `withWave = TRUE` the second argument represents the maximum peak length subject to being greater than the mininum peak length (see also documentation of `do_findChromPeaks_centWave`).
`snthresh` | `numeric(1)` defining the signal to noise ratio cutoff.
`prefilter` | `numeric(2)`. The first argument is only used if (`withWave = TRUE`); see `findChromPeaks-centWave` for details. The second argument specifies the minimum threshold for the maximum intensity of a chromatographic peak that must be met.
`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.
`criticalValue` | `numeric(1)`. Suggested values: (`0.1-3.0`). This setting helps determine the the Kalman Filter prediciton margin of error. A real centroid belonging to a bonafide peak must fall within the KF prediction margin of error. Much like in the construction of a confidence interval, `criticalVal` loosely translates to be a multiplier of the standard error of the prediction reported by the Kalman Filter. If the peak in the XC-MS sample have a small mass deviance in ppm error, a smaller critical value might be better and vice versa.
`consecMissedLimit` | `integer(1)` Suggested values: (`1,2,3`). While a peak is in the proces of being detected by a Kalman Filter, the Kalman Filter may not find a predicted centroid in every scan. After 1 or more consecutive failed predictions, this setting informs Massifquant when to stop a Kalman Filter from following a candidate peak.
`unions` | `integer(1)` set to `1` if apply t-test union on segmentation; set to `0` if no t-test to be applied on chromatographically continous peaks sharing same m/z range. Explanation: With very few data points, sometimes a Kalman Filter stops tracking a peak prematurely. Another Kalman Filter is instantiated and begins following the rest of the signal. Because tracking is done backwards to forwards, this algorithmic defect leaves a real peak divided into two segments or more. With this option turned on, the program identifies segmented peaks and combines them (merges them) into one with a two sample t-test. The potential danger of this option is that some truly distinct peaks may be merged.
`checkBack` | `integer(1)` set to `1` if turned on; set to `0` if turned off. The convergence of a Kalman Filter to a peak's precise m/z mapping is very fast, but sometimes it incorporates erroneous centroids as part of a peak (especially early on). The `scanBack` option is an attempt to remove the occasional outlier that lies beyond the converged bounds of the Kalman Filter. The option does not directly affect identification of a peak because it is a postprocessing measure; it has not shown to be a extremely useful thus far and the default is set to being turned off.
`withWave` | `logical(1)` if `TRUE`, the peaks identified first with Massifquant are subsequently filtered with the second step of the centWave algorithm, which includes wavelet estimation.
`object` | For `findChromPeaks`: an `OnDiskMSnExp` object containing the MS- and all other experiment-relevant data. For all other methods: a parameter object.
`param` | An `MassifquantParam` object containing all settings for the massifquant 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 `MassifquantParam` object.

### Details

This algorithm's performance has been tested rigorously on high resolution LC/OrbiTrap, TOF-MS data in centroid mode. Simultaneous kalman filters identify chromatographic peaks and calculate their area under the curve. The default parameters are set to operate on a complex LC-MS Orbitrap sample. Users will find it useful to do some simple exploratory data analysis to find out where to set a minimum intensity, and identify how many scans an average peak spans. The `consecMissedLimit` parameter has yielded good performance on Orbitrap data when set to (`2`) and on TOF data it was found best to be at (`1`). This may change as the algorithm has yet to be tested on many samples. The `criticalValue` parameter is perhaps most dificult to dial in appropriately and visual inspection of peak identification is the best suggested tool for quick optimization. The `ppm` and `checkBack` parameters have shown less influence than the other parameters and exist to give users flexibility and better accuracy.

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 `MassifquantParam` function returns a `MassifquantParam` class instance with all of the settings specified for chromatographic peak detection by the massifquant 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

`ppm,peakwidth,snthresh,prefilter,mzCenterFun,integrate,mzdiff,fitgauss,noise,verboseColumns,criticalValue,consecMissedLimit,unions,checkBack,withWave`

  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 chromatographic peak detection on `OnDiskMSnExp` objects (defined in the `MSnbase` package). All of the settings to the massifquant and centWave algorithm can be passed with a `MassifquantParam` object.

### Author(s)

Christopher Conley, Johannes Rainer

### References

Conley CJ, Smith R, Torgrip RJ, Taylor RM, Tautenhahn R and Prince JT "Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection" Bioinformatics 2014, 30(18):2636-43.

### See Also

The `do_findChromPeaks_massifquant` core API function and `findPeaks.massifquant` 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-centWave`, `findChromPeaks-matchedFilter`, `findPeaks-MSW`

### Examples

    ## Create a MassifquantParam object.
    mqp <- MassifquantParam()
    ## Change snthresh prefilter parameters
    snthresh(mqp) <- 30
    prefilter(mqp) <- c(6, 10000)
    mqp

    ## Perform the peak detection using massifquant on the files from the
    ## faahKO package. Files are read using the readMSData from the MSnbase
    ## package
    library(faahKO)
    library(MSnbase)
    fls <- dir(system.file("cdf/KO", package = "faahKO"), recursive = TRUE,
               full.names = TRUE)
    raw_data <- readMSData(fls[1], mode = "onDisk")
    ## Perform the peak detection using the settings defined above.
    res <- findChromPeaks(raw_data, param = mqp)
    head(chromPeaks(res))

[Package xcms version 3.16.1 Index]