Identify overlapping features — overlappingFeatures • xcms

overlappingFeatures identifies features that are overlapping or close in the m/z - rt space.

Usage

overlappingFeatures(x, expandMz = 0, expandRt = 0, ppm = 0)

Arguments

x: XcmsExperiment() or XCMSnExp() object with the features.
expandMz: numeric(1) with the value to expand each feature (on each side) in m/z dimension before identifying overlapping features. The resulting "mzmin" for the feature is thus mzmin - expandMz and the "mzmax" mzmax + expandMz.
expandRt: numeric(1) with the value to expand each feature (on each side) in retention time dimension before identifying overlapping features. The resulting "rtmin" for the feature is thus rtmin - expandRt and the "rtmax" rtmax + expandRt.
ppm: numeric(1) to grow the m/z width of the feature by a relative value: mzmin - mzmin * ppm / 2e6, mzmax + mzmax * ppm / 2e6. Each feature is thus expanded in m/z dimension by ppm/2 on each side before identifying overlapping features.

Value

list with indices of features (in featureDefinitions()) that are overlapping.

Author

Johannes Rainer

Examples


## Load a test data set with detected peaks
library(MSnbase)
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())

## Correspondence analysis
xdata <- groupChromPeaks(faahko_sub, param = PeakDensityParam(sampleGroups = c(1, 1, 1)))

## Identify overlapping features
overlappingFeatures(xdata)
#> list()

## Identify features that are separated on retention time by less than
## 2 minutes
overlappingFeatures(xdata, expandRt = 60)
#> [[1]]
#> [1] 5 6
#> 
#> [[2]]
#> [1] 31 32
#>