centWave-based peak detection in purely chromatographic data

findChromPeaks on a Chromatogram or MChromatograms object with a CentWaveParam parameter object performs centWave-based peak detection on purely chromatographic data. See centWave for details on the method and CentWaveParam for details on the parameter class. Note that not all settings from the CentWaveParam will be used. See peaksWithCentWave() for the arguments used for peak detection on purely chromatographic data.

After chromatographic peak detection, identified peaks can also be refined with the refineChromPeaks() method, which can help to reduce peak detection artifacts.

Usage

# S4 method for class 'Chromatogram,CentWaveParam'
findChromPeaks(object, param, ...)

# S4 method for class 'MChromatograms,CentWaveParam'
findChromPeaks(object, param, BPPARAM = bpparam(), ...)

# S4 method for class 'MChromatograms,MatchedFilterParam'
findChromPeaks(object, param, BPPARAM = BPPARAM, ...)

Arguments

object

a Chromatogram or MChromatograms object.

param

a CentWaveParam object specifying the settings for the peak detection. See peaksWithCentWave() for the description of arguments used for peak detection.

...

currently ignored.

BPPARAM

a parameter class specifying if and how parallel processing should be performed (only for XChromatograms objects). It defaults to bpparam(). See bpparam() for more information.

Value

If called on a Chromatogram object, the method returns an XChromatogram object with the identified peaks. See peaksWithCentWave() for details on the peak matrix content.

See also

peaksWithCentWave() for the downstream function and centWave for details on the method.

Author

Johannes Rainer

Examples

library(MSnbase)
## Loading a test data set with identified chromatographic peaks
faahko_sub <- loadXcmsData("faahko_sub2")
faahko_sub <- filterRt(faahko_sub, c(2500, 3700))
#> Filter spectra

##
od <- as(filterFile(faahko_sub, 1L), "MsExperiment")

## Extract chromatographic data for a small m/z range
chr <- chromatogram(od, mz = c(272.1, 272.3))[1, 1]

## Identify peaks with default settings
xchr <- findChromPeaks(chr, CentWaveParam())
xchr
#> Object of class: XChromatogram
#> Intensity values aggregated using: sum 
#> length of object: 766
#> from file: 1
#> mz range: [272.1, 272.3]
#> rt range: [2501.378, 3698.567]
#> MS level: 1
#> Identified chromatographic peaks (3):
#>  rt	rtmin	rtmax	into	maxo	sn (1 more column(s))
#>  2734.556	2709.517	2770.55	286653.221615386	8907	15
#>  3117.97	3097.625	3139.879	372049.599925925	16496	33
#>  3487.299	3457.565	3509.208	93049.7314242425	2651	15

## Plot data and identified peaks.
plot(xchr)


library(MsExperiment)
library(xcms)
## Perform peak detection on an MChromatograms object

fls <- c(system.file("cdf/KO/ko15.CDF", package = "faahKO"),
    system.file("cdf/KO/ko16.CDF", package = "faahKO"),
    system.file("cdf/KO/ko18.CDF", package = "faahKO"))
od3 <- readMsExperiment(fls)

## Disable parallel processing for this example
register(SerialParam())

## Extract chromatograms for a m/z - retention time slice
chrs <- chromatogram(od3, mz = 344, rt = c(2500, 3500))

## Perform peak detection using CentWave
xchrs <- findChromPeaks(chrs, param = CentWaveParam())
xchrs
#> XChromatograms with 1 row and 3 columns
#>                    1               2               3
#>      <XChromatogram> <XChromatogram> <XChromatogram>
#> [1,]        peaks: 2        peaks: 2        peaks: 1
#> phenoData with 2 variables
#> featureData with 4 variables
#> - - - xcms preprocessing - - -
#> Chromatographic peak detection:
#>  method: centWave 

## Extract the identified chromatographic peaks
chromPeaks(xchrs)
#>            rt    rtmin    rtmax      into      intb   maxo sn row column
#> [1,] 2589.015 2571.801 2612.490  592297.3  554533.9  27600 14   1      1
#> [2,] 2679.783 2646.919 2709.517 5210015.9 5071346.6 152320 30   1      1
#> [3,] 2593.710 2559.281 2637.529  722415.3  640838.6  30640 14   1      2
#> [4,] 2686.042 2648.483 2714.211 5700652.0 5587464.2 151744 45   1      2
#> [5,] 2682.914 2643.790 2731.427 5255689.5 4983388.9 125632 28   1      3

## plot the result
plot(xchrs)