Compounding/feature grouping based on similarity of abundances across samples

Features from the same originating compound are expected to have similar intensities across samples. This method thus groups features based on similarity of abundances (i.e. feature values) across samples in a data set. See also AbundanceSimilarityParam() for additional information and details.

This help page lists parameters specific for xcms result objects (i.e. XcmsExperiment() and XCMSnExp() objects). Documentation of the parameters for the similarity calculation is available in the AbundanceSimilarityParam() help page in the MsFeatures package.

Usage

# S4 method for class 'XcmsResult,AbundanceSimilarityParam'
groupFeatures(
  object,
  param,
  msLevel = 1L,
  method = c("medret", "maxint", "sum"),
  value = "into",
  intensity = "into",
  filled = TRUE,
  ...
)

Arguments

object

XcmsExperiment() or XCMSnExp() object containing LC-MS pre-processing results.

param

AbudanceSimilarityParam object with the settings for the method. See AbundanceSimilarityParam() for details on the grouping method and its parameters.

msLevel

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

method

character(1) passed to the featureValues call. See featureValues() for details. Defaults to method = "medret".

value

character(1) passed to the featureValues call. See featureValues() for details. Defaults to value = "into".

intensity

character(1) passed to the featureValues call. See featureValues() for details. Defaults to intensity = "into".

filled

logical(1) whether filled-in values should be included in the correlation analysis. Defaults to filled = TRUE.

...

additional parameters passed to the groupFeatures method for matrix.

Value

input object with feature group definitions added to (or updated in) a column "feature_group" in its featureDefinitions data frame.

See also

feature-grouping for a general overview.

Other feature grouping methods: groupFeatures-eic-similarity, groupFeatures-similar-rtime

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 correlation of feature values (integrated
## peak area) across samples. Note that there are many missing values
## in the feature value which influence grouping of features in the present
## data set.
xodg_grp <- groupFeatures(xodg,
    param = AbundanceSimilarityParam(threshold = 0.8))
table(featureDefinitions(xodg_grp)$feature_group)
#> 
#> FG.001 FG.002 FG.003 FG.004 FG.005 FG.006 FG.007 FG.008 
#>      8      8      8      6     12      3      1      1 

## Group based on the maximal peak intensity per feature
xodg_grp <- groupFeatures(xodg,
    param = AbundanceSimilarityParam(threshold = 0.8, value = "maxo"))
table(featureDefinitions(xodg_grp)$feature_group)
#> 
#> FG.001 FG.002 FG.003 FG.004 FG.005 FG.006 FG.007 FG.008 
#>      8      8      8      6     12      3      1      1