Base feature annotations class

Source: R/feature_annotations.R

Holds information for all feature group annotations.

# S4 method for class 'featureAnnotations'
annotations(obj)

# S4 method for class 'featureAnnotations'
groupNames(obj)

# S4 method for class 'featureAnnotations'
length(x)

# S4 method for class 'featureAnnotations,ANY,missing,missing'
x[i, j, ..., drop = TRUE]

# S4 method for class 'featureAnnotations,ANY,missing'
x[[i, j]]

# S4 method for class 'featureAnnotations'
x$name

# S4 method for class 'featureAnnotations'
as.data.table(
  x,
  fGroups = NULL,
  fragments = FALSE,
  countElements = NULL,
  countFragElements = NULL,
  OM = FALSE,
  normalizeScores = "none",
  excludeNormScores = defaultExclNormScores(x)
)

# S4 method for class 'featureAnnotations'
delete(obj, i = NULL, j = NULL, ...)

# S4 method for class 'featureAnnotations'
filter(
  obj,
  minExplainedPeaks = NULL,
  scoreLimits = NULL,
  elements = NULL,
  fragElements = NULL,
  lossElements = NULL,
  topMost = NULL,
  OM = FALSE,
  negate = FALSE
)

# S4 method for class 'featureAnnotations'
plotVenn(obj, ..., labels = NULL, vennArgs = NULL)

# S4 method for class 'featureAnnotations'
plotUpSet(
  obj,
  ...,
  labels = NULL,
  nsets = length(list(...)) + 1,
  nintersects = NA,
  upsetArgs = NULL
)

Arguments

obj, x

featureAnnotations object to be accessed

i, j

For [/[[: A numeric or character value which is used to select feature groups by their index or name, respectively (for the order/names see groupNames()).

For [: Can also be logical to perform logical selection (similar to regular vectors). If missing all feature groups are selected.

For [[: should be a scalar value.

For delete: The data to remove from. i are the feature groups as numeric index, logical or character, j the candidates as numeric indices (rows). If either is NULL then data for all is removed. j may also be a function: it will be called for each feature group, with the annotation table (a data.table), the feature group name and any other arguments passed as ... to delete. The return value of this function specifies the candidate indices (rows) to be removed (specified as an integer or logical vector).

...

For the "[" operator: ignored.

For delete: passed to the function specified as j.

Others: Any further (and unique) featureAnnotations objects.

drop

ignored.

name

The feature group name (partially matched).

fGroups

The featureGroups object that was used to generate this object. If not NULL it is used to add feature group information (retention and m/z values).

fragments

If TRUE then information on annotated fragments will be included. Automatically set to TRUE if countFragElements is set.

countElements, countFragElements

A character vector with elements that should be counted for each candidate's formula. For instance, c("C", "H") adds columns for both carbon and hydrogen amounts of each formula. Note that the neutral formula (neutral_formula column) is used to count elements of non-fragmented formulae, whereas the charged formula of fragments (ion_formula column in fragInfo data) is used for fragments. Set to NULL to not count any elements.

OM

For as.data.table: if set to TRUE several columns with information relevant for organic matter (OM) characterization will be added (e.g. elemental ratios, classification). This will also make sure that countElements contains at least C, H, N, O, P and S.

For filter: If TRUE then several filters are applied to exclude unlikely formula candidates present in organic matter (OM). See Source section for details.

normalizeScores

A character that specifies how normalization of annotation scorings occurs. Either "none" (no normalization), "max" (normalize to max value) or "minmax" (perform min-max normalization). Note that normalization of negative scores (e.g. output by SIRIUS) is always performed as min-max. Furthermore, currently normalization for compounds takes the original min/max scoring values into account when candidates were generated. Thus, for compounds scoring, normalization is not affected when candidate results were removed after they were generated (e.g. by use of filter).

excludeNormScores

A character vector specifying any compound scoring names that should not be normalized. Set to NULL to normalize all scorings. Note that whether any normalization occurs is set by the excludeNormScores argument.

For compounds: By default score and individualMoNAScore are set to mimic the behavior of the MetFrag web interface.

minExplainedPeaks

Minimum number of explained peaks. Set to NULL to ignore.

scoreLimits

Filter results by their scores. Should be a named list that contains two-sized numeric vectors with the minimum/maximum value of a score (use -Inf/Inf for no limits). The names of each element should follow the name column of the table returned by formulaScorings$name and compoundScorings()$name. For instance, scoreLimits=list(numberPatents=c(10, Inf)) specifies that numberPatents should be at least 10. Note that a result without a specified scoring is never removed. If a score term exists multiple times, i.e. due to a consensus, then a candidate is kept if at least one of the terms falls within the range. Set to NULL to skip this filter.

elements

Only retain candidate formulae (neutral form) that match a given elemental restriction. The format of elements is a character string with elements that should be present where each element is followed by a valid amount or a range thereof. If no number is specified then 1 is assumed. For instance, elements="C1-10H2-20O0-2P", specifies that 1-10, 2-20, 0-2 and 1 carbon, hydrogen, oxygen and phosphorus atoms should be present, respectively. When length(elements)>1 formulas are tested to follow at least one of the given elemental restrictions. For instance, elements=c("P", "S") specifies that either one phosphorus or one sulfur atom should be present. Set to NULL to ignore this filter.

fragElements, lossElements

Specifies elemental restrictions for fragment or neutral loss formulae (charged form). Candidates are retained if at least one of the fragment formulae follow (or not follow if negate=TRUE) the given restrictions. See elements for the used format.

topMost

Only keep a maximum of topMost candidates with highest score (or least highest if negate=TRUE). Set to NULL to ignore.

negate

If TRUE then filters are applied in opposite manner.

labels

A character with names to use for labelling. If NULL labels are automatically generated.

vennArgs

A list with further arguments passed to VennDiagram plotting functions. Set to NULL to ignore.

nsets, nintersects

See upset.

upsetArgs

A list with any further arguments to be passed to upset. Set to NULL to ignore.

Value

as.data.table returns a data.table.

delete returns the object for which the specified data was removed.

filter returns a filtered featureAnnotations object.

plotVenn (invisibly) returns a list with the following fields:

  • gList the gList object that was returned by the utilized VennDiagram plotting function.

  • areas The total area for each plotted group.

  • intersectionCounts The number of intersections between groups.

The order for the areas and intersectionCounts fields is the same as the parameter order from the used plotting function (see e.g. draw.pairwise.venn and draw.triple.venn).

Details

This class stores annotation data for feature groups, such as molecular formulae, SMILES identifiers, compound names etc. The class of objects that are generated by formula and compound annotation (generateFormulas and generateCompounds) are based on this class.

Methods (by generic)

  • annotations(featureAnnotations): Accessor for the groupAnnotations slot.

  • groupNames(featureAnnotations): returns a character vector with the names of the feature groups for which data is present in this object.

  • length(featureAnnotations): Obtain total number of candidates.

  • x[i: Subset on feature groups.

  • x[[i: Extracts annotation data for a feature group.

  • $: Extracts annotation data for a feature group.

  • as.data.table(featureAnnotations): Generates a table with all annotation data for each feature group and other information such as element counts.

  • delete(featureAnnotations): Completely deletes specified annotations.

  • filter(featureAnnotations): Provides rule based filtering for feature group annotations. Useful to eliminate unlikely candidates and speed up further processing.

  • plotVenn(featureAnnotations): plots a Venn diagram (using VennDiagram) outlining unique and shared candidates of up to five different featureAnnotations objects.

  • plotUpSet(featureAnnotations): plots an UpSet diagram (using the upset function) outlining unique and shared candidates between different featureAnnotations objects.

Slots

groupAnnotations

A list with for each annotated feature group a data.table with annotation data. Use the annotations method for access.

scoreTypes

A character with all the score types present in this object.

scoreRanges

The minimum and maximum score values of all candidates for each feature group. Used for normalization.

Source

Calculation of the aromaticity index (AI) and related double bond equivalents (DBE_AI) is performed as described in Koch 2015. Formula classification is performed by the rules described in Abdulla 2013. Filtering of OM related molecules is performed as described in Koch 2006 and Kujawinski 2006. (see references).

References

Koch BP, Dittmar T (2015). “From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter.” Rapid Communications in Mass Spectrometry, 30(1), 250–250. doi:10.1002/rcm.7433 .

Abdulla HA, Sleighter RL, Hatcher PG (2013). “Two Dimensional Correlation Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectra of Dissolved Organic Matter: A New Graphical Analysis of Trends.” Analytical Chemistry, 85(8), 3895–3902. doi:10.1021/ac303221j .

Koch BP, Dittmar T (2006). “From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter.” Rapid Communications in Mass Spectrometry, 20(5), 926–932. doi:10.1002/rcm.2386 .

Kujawinski EB, Behn MD (2006). “Automated Analysis of Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectra of Natural Organic Matter.” Analytical Chemistry, 78(13), 4363–4373. doi:10.1021/ac0600306 .

Conway JR, Lex A, Gehlenborg N (2017). “UpSetR: an R package for the visualization of intersecting sets and their properties.” Bioinformatics, 33(18), 2938-2940. doi:10.1093/bioinformatics/btx364 , http://dx.doi.org/10.1093/bioinformatics/btx364.

Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H (2014). “UpSet: Visualization of Intersecting Sets.” IEEE Transactions on Visualization and Computer Graphics, 20(12), 1983–1992. doi:10.1109/tvcg.2014.2346248 .

See also

formulas-class and compounds-class

The derived formulas and compounds classes.