ggplot2 Version of plotFeatureGroups()

Visualizes feature groups by plotting features connected across retention time and m/z dimensions. This is a ggplot2 implementation of xcms's xcms::plotFeatureGroups() function, enabling modern visualization and interactive plotting capabilities.

Usage

gplotFeatureGroups(
  x,
  xlim = numeric(),
  ylim = numeric(),
  pch = 4,
  col = "#00000060",
  type = "o",
  featureGroups = character(),
  ...
)

# S4 method for class 'XCMSnExp'
gplotFeatureGroups(
  x,
  xlim = numeric(),
  ylim = numeric(),
  pch = 4,
  col = "#00000060",
  type = "o",
  featureGroups = character(),
  ...
)

# S4 method for class 'XcmsExperiment'
gplotFeatureGroups(
  x,
  xlim = numeric(),
  ylim = numeric(),
  pch = 4,
  col = "#00000060",
  type = "o",
  featureGroups = character(),
  ...
)

Arguments

x

An XCMSnExp or XcmsExperiment object with feature grouping results.

xlim

numeric(2) vector of length 2 specifying retention time range. Default: numeric() (auto-calculate from data).

ylim

numeric(2) vector of length 2 specifying m/z range. Default: numeric() (auto-calculate from data).

pch

Point character for feature markers (default: 4).

col

Color for feature points and connecting lines (default: "#00000060").

type

Plot type (default: "o" for overplotted points and lines).

featureGroups

character vector of feature group identifiers to plot. If empty (default), all feature groups are plotted.

...

Additional arguments passed to geom functions.

Value

A ggplot object showing features connected by lines within each feature group across retention time and m/z dimensions.

Details

The function:

• Extracts feature definitions and their grouping information

• Plots each feature as a point at (rtmed, mzmed)

• Connects features within the same group with lines

• Feature groups are created by groupFeatures() which identifies features that likely represent the same compound (isotopes, adducts, etc.)

Feature groups must be present in the object before calling this function. Run groupFeatures() first to create feature groups based on retention time, m/z relationships, or other criteria.

See also

xcms::plotFeatureGroups() for the original xcms implementation.

Author

Jan Stanstrup

Examples

library(xcmsVis)
library(xcms)
library(faahKO)
library(MsExperiment)
library(BiocParallel)
library(MsFeatures)

# Load example data
cdf_files <- dir(system.file("cdf", package = "faahKO"),
                 recursive = TRUE, full.names = TRUE)[1:3]

# Create XcmsExperiment and perform complete workflow
xdata <- readMsExperiment(spectraFiles = cdf_files, BPPARAM = SerialParam())
xdata <- findChromPeaks(xdata, param = CentWaveParam(), BPPARAM = SerialParam())
xdata <- groupChromPeaks(xdata, param = PeakDensityParam(
  sampleGroups = rep(1, 3), minFraction = 0.5))

# Disable parallel processing to avoid warnings
register(SerialParam())
xdata <- adjustRtime(xdata, param = ObiwarpParam())
#> value 5
xdata <- groupChromPeaks(xdata, param = PeakDensityParam(
  sampleGroups = rep(1, 3), minFraction = 0.5))

# Group features (identify related features like isotopes/adducts)
xdata <- groupFeatures(xdata, param = SimilarRtimeParam())

# Visualize feature groups
gplotFeatureGroups(xdata)
#> Warning: Removed 590 rows containing missing values or values outside the scale range
#> (`geom_path()`).


# Visualize specific feature groups only
gplotFeatureGroups(xdata, featureGroups = c("FG.0001", "FG.0002"))
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_path()`).