R: High resolution peak grouping for single spectra samples

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)

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 3.0.2 Index]