Description Usage Arguments Value Slots Note Author(s) References See Also Examples
This method performs performs correspondence (chromatographic peak grouping) based on the density (distribution) of identified peaks along the retention time axis within slices of overlapping mz ranges. All peaks (from the same or from different samples) being close on the retention time axis are grouped into a feature (peak group).
The PeakDensityParam
class allows to specify all settings for the peak
grouping based on peak densities along the time dimension. Instances should
be created with the PeakDensityParam()
constructor.
sampleGroups
,sampleGroups<-
: getter and setter
for the sampleGroups
slot of the object. Its length should match
the number of samples in the experiment and it should not contain
NA
s.
bw
,bw<-
: getter and setter for the bw
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.
binSize
,binSize<-
: getter and setter for the
binSize
slot of the object.
maxFeatures
,maxFeatures<-
: getter and setter for
the maxFeatures
slot of the object.
groupChromPeaks,XCMSnExp,PeakDensityParam
:
performs correspondence (peak grouping within and across samples) within
in mz dimension overlapping slices of MS data based on the density
distribution of the identified chromatographic peaks in the slice along
the time axis.
The correspondence analysis can be performed on chromatographic peaks of
any MS level (if present and if chromatographic peak detection has been
performed for that MS level) defining features combining these peaks. The
MS level can be selected with the parameter msLevel
. By default, calling
groupChromPeaks
will remove any previous correspondence results. This can
be disabled with add = TRUE
, which will add newly defined features to
already present feature definitions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | PeakDensityParam(
sampleGroups = numeric(),
bw = 30,
minFraction = 0.5,
minSamples = 1,
binSize = 0.25,
maxFeatures = 50
)
## S4 method for signature 'PeakDensityParam'
show(object)
## S4 method for signature 'PeakDensityParam'
sampleGroups(object)
## S4 replacement method for signature 'PeakDensityParam'
sampleGroups(object) <- value
## S4 method for signature 'PeakDensityParam'
bw(object)
## S4 replacement method for signature 'PeakDensityParam'
bw(object) <- value
## S4 method for signature 'PeakDensityParam'
minFraction(object)
## S4 replacement method for signature 'PeakDensityParam'
minFraction(object) <- value
## S4 method for signature 'PeakDensityParam'
minSamples(object)
## S4 replacement method for signature 'PeakDensityParam'
minSamples(object) <- value
## S4 method for signature 'PeakDensityParam'
binSize(object)
## S4 replacement method for signature 'PeakDensityParam'
binSize(object) <- value
## S4 method for signature 'PeakDensityParam'
maxFeatures(object)
## S4 replacement method for signature 'PeakDensityParam'
maxFeatures(object) <- value
## S4 method for signature 'XCMSnExp,PeakDensityParam'
groupChromPeaks(object, param, msLevel = 1L, add = FALSE)
|
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 |
bw |
|
minFraction |
|
minSamples |
|
binSize |
|
maxFeatures |
|
object |
For For all other methods: a `PeakDensityParam` object. |
value |
The value for the slot. |
param |
A |
msLevel |
|
add |
|
The PeakDensityParam
function returns a
PeakDensityParam
class instance with all of the settings
specified for chromatographic peak alignment based on peak densities.
Note that argument sampleGroups
is mandatory and should represent
either the sample grouping in the experiment. It's length has to match
the number of sample in the experiments.
For groupChromPeaks
: a XCMSnExp object with the
results of the correspondence analysis. The definition of the resulting
mz-rt features can be accessed with the featureDefinitions()
method
.__classVersion__,sampleGroups,bw,minFraction,minSamples,binSize,maxFeatures
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.
These methods and classes are part of the updated and modernized
xcms
user interface. All of the settings to the algorithm
can be passed with a PeakDensityParam
object.
Colin Smith, Johannes Rainer
Colin A. Smith, Elizabeth J. Want, Grace O'Maille, Ruben Abagyan and Gary Siuzdak. "XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification" Anal. Chem. 2006, 78:779-787.
The do_groupChromPeaks_density()
core API function and group.density()
for the old user interface.
plotChromPeakDensity()
to plot peak densities and evaluate different
algorithm settings.
featureDefinitions()
and featureValues()
for methods to access the
features (i.e. the peak grouping results).
XCMSnExp for the object containing the results of the correspondence.
plotChromPeakDensity()
for plotting chromatographic peak density with the
possibility to test different parameter settings.
Other peak grouping methods:
groupChromPeaks-mzClust
,
groupChromPeaks-nearest
,
groupChromPeaks()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ## Create a PeakDensityParam object
p <- PeakDensityParam(binSize = 0.05, sampleGroups = c(1, 1, 2, 2))
## Change hte minSamples slot
minSamples(p) <- 3
p
##############################
## Chromatographic peak detection and grouping.
##
## Load a test data set with detected peaks
data(faahko_sub)
## Update the path to the files for the local system
dirname(faahko_sub) <- system.file("cdf/KO", package = "faahKO")
## Disable parallel processing for this example
register(SerialParam())
res <- faahko_sub
head(chromPeaks(res))
## The number of peaks identified per sample:
table(chromPeaks(res)[, "sample"])
## Performing the chromatographic peak grouping. Assigning all samples to
## the same sample group.
fdp <- PeakDensityParam(sampleGroups = rep(1, length(fileNames(res))))
res <- groupChromPeaks(res, fdp)
## The definition of the features (peak groups):
featureDefinitions(res)
## Using the featureValues method to extract a matrix with the
## intensities of the features per sample.
head(featureValues(res, value = "into"))
## The process history:
processHistory(res)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.