Compounding/feature grouping based on similar retention times

Group features based on similar retention time. This method is supposed to be used as an initial crude grouping of features based on the median retention time of all their chromatographic peaks. All features with a difference in their retention time which is <= parameter diffRt of the parameter object are grouped together. If a column "feature_group" is found in featureDefinitions() this is further sub-grouped by this method.

See MsFeatures::SimilarRtimeParam() in MsFeatures for more details.

Usage

# S4 method for class 'XcmsResult,SimilarRtimeParam'
groupFeatures(object, param, msLevel = 1L, ...)

Arguments

object

XcmsExperiment() or XCMSnExp() object containing also correspondence results.

param

SimilarRtimeParam object with the settings for the method. See MsFeatures::SimilarRtimeParam() for details and options.

msLevel

integer(1) defining the MS level on which the features should be grouped.

...

passed to the groupFeatures function on numeric values.

Value

the input object with feature groups added (i.e. in column "feature_group" of its featureDefinitions data frame.

See also

Other feature grouping methods: groupFeatures-abundance-correlation, groupFeatures-eic-similarity

Author

Johannes Rainer

Examples

library(MsFeatures)
library(MsExperiment)
## Load a test data set with detected peaks
faahko_sub <- loadXcmsData("faahko_sub2")

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

## Group chromatographic peaks across samples
xodg <- groupChromPeaks(faahko_sub, param = PeakDensityParam(sampleGroups = rep(1, 3)))

## Group features based on similar retention time (i.e. difference <= 2 seconds)
xodg_grp <- groupFeatures(xodg, param = SimilarRtimeParam(diffRt = 2))

## Feature grouping get added to the featureDefinitions in column "feature_group"
head(featureDefinitions(xodg_grp)$feature_group)
#> [1] "FG.004" "FG.009" "FG.003" "FG.004" "FG.010" "FG.011"

table(featureDefinitions(xodg_grp)$feature_group)
#> 
#> FG.001 FG.002 FG.003 FG.004 FG.005 FG.006 FG.007 FG.008 FG.009 FG.010 FG.011 
#>      2      2      3      3      3      2      2      2      1      1      1 
#> FG.012 FG.013 FG.014 FG.015 FG.016 FG.017 FG.018 FG.019 FG.020 FG.021 FG.022 
#>      1      1      1      1      1      1      1      1      1      1      1 
#> FG.023 FG.024 FG.025 FG.026 FG.027 FG.028 FG.029 FG.030 FG.031 FG.032 FG.033 
#>      1      1      1      1      1      1      1      1      1      1      1 
#> FG.034 FG.035 FG.036 
#>      1      1      1 
length(unique(featureDefinitions(xodg_grp)$feature_group))
#> [1] 36

## Using an alternative groupiing method that creates larger groups
xodg_grp <- groupFeatures(xodg,
    param = SimilarRtimeParam(diffRt = 2, groupFun = MsCoreUtils::group))

length(unique(featureDefinitions(xodg_grp)$feature_group))
#> [1] 35