overlappingFeatures {xcms} | R Documentation

## Identify overlapping features

### Description

`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` | `XCMSnExp` 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(s)

Johannes Rainer

### Examples

    ## Load 2 test files.
    data <- readMSData(c(system.file("cdf/KO/ko15.CDF", package = "faahKO"),
                         system.file("cdf/KO/ko16.CDF", package = "faahKO")),
                       mode = "onDisk")

    ## Perform peak detection; parameters set to reduce processing speed
    data <- findChromPeaks(data, CentWaveParam(noise = 10000, snthresh = 40))

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

    ## Identify overlapping features
    overlappingFeatures(data)

    ## Identify features that are separated on retention time by less than
    ## 2 minutes
    overlappingFeatures(data, expandRt = 60)

[Package xcms version 3.9.1 Index]