Components based on parent and transformation product (TP) linkage.

This class is derived from components and is used to store components that result from linking feature groups that are (predicted to be) parents with feature groups that (are predicted to be) transformation products. For more details, see generateComponentsTPs.

# S4 method for class 'componentsTPs'
as.data.table(x, candidates = FALSE)

# S4 method for class 'componentsTPs'
filter(
  obj,
  ...,
  retDirMatch = FALSE,
  minSpecSim = NULL,
  minSpecSimPrec = NULL,
  minSpecSimBoth = NULL,
  minTotFragMatches = NULL,
  minTotNLMatches = NULL,
  minFragMatches = NULL,
  minNLMatches = NULL,
  formulas = NULL,
  verbose = TRUE,
  negate = FALSE
)

# S4 method for class 'componentsTPs'
plotGraph(obj, onlyLinked = TRUE, width = NULL, height = NULL)

Arguments

x, obj: A componentsTPs object.
candidates: If TRUE then candidate specific tables are merged in the result table. See generateComponentsTPs (Result columns section) for more details.
..., verbose: Further arguments passed to the base filter method.
retDirMatch: If set to TRUE, only keep TPs for which the retention order direction from feature group data matches that of the expected values from TP data (retDir and TP_retDir columns). TPs will never be removed if either of the directions is 0 (i.e. unknown or not significantly different than the parent).
minSpecSim, minSpecSimPrec, minSpecSimBoth: The minimum spectral similarity of a TP compared to its parent (0-1). The minSpecSimPrec and minSpecSimBoth apply to binned data that is shifted with the "precursor" and "both" method, respectively (see MS spectral similarity parameters for more details). Set to NULL to ignore.
minTotFragMatches, minTotNLMatches, minFragMatches, minNLMatches: Minimum number of (total) parent/TP fragment and neutral loss matches. Set to NULL to ignore. See the Result columns section in generateComponentsTPs for more details.
formulas: A formulas object. The formula annotation data in this object is to verify if elemental additions/subtractions from metabolic logic reactions are possible (hence, it only works with data from generateTPsLogic). To verify elemental additions, only TPs with at least one candidate formula that has these elements are kept. Similarly, for elemental subtractions, any of the parent candidate formulae must contain the subtraction elements. Note that TPs are currently not filtered if either the parent or the TP has no formula annotations. Set to NULL to ignore.
negate: If TRUE then filters are applied in opposite manner.
onlyLinked: If TRUE then only components with links are plotted.
width, height: Passed to visNetwork.

Value

filter returns a filtered componentsTPs object.

plotGraph returns the result of visNetwork.

Methods (by generic)

as.data.table(componentsTPs): Returns all component data as a data.table.
filter(componentsTPs): Provides various rule based filtering options to clean and prioritize TP data.
plotGraph(componentsTPs): Plots an interactive network graph for linked components. Components are linked with each other if one or more transformation products overlap. The graph is constructed with the igraph package and rendered with visNetwork.

Slots

fromTPs: A logical that is TRUE when the componentization was performed with transformationProducts data (i.e. the TPs argument was not NULL).
parentsFromScreening: A logical that is TRUE when the parents were obtained from screening data.
TPsFromScreening: A logical that is TRUE when the TPs were obtained from screening data.

Note

The intensity values for components (used by plotSpectrum) are set to a dummy value (1) as no single intensity value exists for this kind of components.

S4 class hierarchy

components
- componentsTPs

References

Antonov M, Csárdi G, Horvát S, Müller K, Nepusz T, Noom D, Salmon M, Traag V, Welles BF, Zanini F (2023). “igraph enables fast and robust network analysis across programming languages.” arXiv preprint arXiv:2311.10260. doi:10.48550/arXiv.2311.10260 .

Csárdi G, Nepusz T (2006). “The igraph software package for complex network research.” InterJournal, Complex Systems, 1695. https://igraph.org.

Csárdi G, Nepusz T, Traag V, Horvát S, Zanini F, Noom D, Müller K, Schoch D, Salmon M (2026). igraph: Network Analysis and Visualization in R. doi:10.5281/zenodo.7682609 , R package version 2.2.3, https://CRAN.R-project.org/package=igraph.