findChromPeaks-centWave: Chromatographic peak detection using the centWave method
In xiaodfeng/DynamicXCMS: LC-MS and GC-MS Data Analysis

findChromPeaks-centWave

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 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.
`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. 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.

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 OnDiskMSnExp objects (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

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))

xiaodfeng/DynamicXCMS documentation built on Aug. 6, 2023, 3:02 p.m.