Compound annotations class

Contains data for compound annotations for feature groups.

addFormulaScoring(
  compounds,
  formulas,
  updateScore = FALSE,
  formulaScoreWeight = 1
)

# S4 method for class 'compounds'
defaultExclNormScores(obj)

# S4 method for class 'compounds'
show(object)

# S4 method for class 'compounds'
identifiers(compounds)

# S4 method for class 'compounds'
filter(
  obj,
  minExplainedPeaks = NULL,
  minScore = NULL,
  minFragScore = NULL,
  minFormulaScore = NULL,
  scoreLimits = NULL,
  ...
)

# S4 method for class 'compounds'
addFormulaScoring(
  compounds,
  formulas,
  updateScore = FALSE,
  formulaScoreWeight = 1
)

# S4 method for class 'compounds'
getMCS(obj, index, groupName)

# S4 method for class 'compounds'
plotStructure(obj, index, groupName, width = 500, height = 500)

# S4 method for class 'compounds'
plotScores(
  obj,
  index,
  groupName,
  normalizeScores = "max",
  excludeNormScores = defaultExclNormScores(obj),
  onlyUsed = TRUE
)

# S4 method for class 'compounds'
annotatedPeakList(
  obj,
  index,
  groupName,
  MSPeakLists,
  formulas = NULL,
  onlyAnnotated = FALSE
)

# S4 method for class 'compounds'
plotSpectrum(
  obj,
  index,
  groupName,
  MSPeakLists,
  formulas = NULL,
  plotStruct = FALSE,
  title = NULL,
  specSimParams = getDefSpecSimParams(),
  mincex = 0.9,
  xlim = NULL,
  ylim = NULL,
  maxMolSize = c(0.2, 0.4),
  molRes = c(100, 100),
  ...
)

# S4 method for class 'compounds'
consensus(
  obj,
  ...,
  absMinAbundance = NULL,
  relMinAbundance = NULL,
  uniqueFrom = NULL,
  uniqueOuter = FALSE,
  rankWeights = 1,
  labels = NULL
)

# S4 method for class 'compoundsSet'
show(object)

# S4 method for class 'compoundsSet'
delete(obj, i, j, ...)

# S4 method for class 'compoundsSet,ANY,missing,missing'
x[i, j, ..., sets = NULL, updateConsensus = FALSE, drop = TRUE]

# S4 method for class 'compoundsSet'
filter(obj, ..., sets = NULL, updateConsensus = FALSE, negate = FALSE)

# S4 method for class 'compoundsSet'
plotSpectrum(
  obj,
  index,
  groupName,
  MSPeakLists,
  formulas = NULL,
  plotStruct = FALSE,
  title = NULL,
  specSimParams = getDefSpecSimParams(),
  mincex = 0.9,
  xlim = NULL,
  ylim = NULL,
  maxMolSize = c(0.2, 0.4),
  molRes = c(100, 100),
  perSet = TRUE,
  mirror = TRUE,
  ...
)

# S4 method for class 'compoundsSet'
addFormulaScoring(
  compounds,
  formulas,
  updateScore = FALSE,
  formulaScoreWeight = 1
)

# S4 method for class 'compoundsSet'
annotatedPeakList(obj, index, groupName, MSPeakLists, formulas = NULL, ...)

# S4 method for class 'compoundsSet'
consensus(
  obj,
  ...,
  absMinAbundance = NULL,
  relMinAbundance = NULL,
  uniqueFrom = NULL,
  uniqueOuter = FALSE,
  rankWeights = 1,
  labels = NULL,
  filterSets = FALSE,
  setThreshold = 0,
  setThresholdAnn = 0,
  setAvgSpecificScores = FALSE
)

# S4 method for class 'compoundsSet'
unset(obj, set)

# S4 method for class 'compoundsConsensusSet'
unset(obj, set)

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

Arguments

formulas

The formulas object that should be used for scoring/annotation. For plotSpectrum and annotatedPeakList: set to NULL to ignore.

updateScore, formulaScoreWeight

If updateScore=TRUE then the annotation score column is updated by adding normalized values of the formula score (weighted by formulaScoreWeight). Currently, this only makes sense for annotations performed with MetFrag!

obj, object, compounds, x

The compound object.

minExplainedPeaks, scoreLimits

Passed to the featureAnnotations method.

minScore, minFragScore, minFormulaScore

Minimum overall score, in-silico fragmentation score and formula score, respectively. Set to NULL to ignore. The scoreLimits argument allows for more advanced score filtering.

...

For plotSpectrum: Further arguments passed to plot.

For delete: passed to the function specified as j.

for filter: passed to the featureAnnotations method.

For consensus: any further (and unique) compounds objects.

For sets workflow methods: further arguments passed to the base compounds method.

index

The numeric index of the candidate structure.

For plotStructure and getMCS: multiple indices (i.e. vector with length >=2) should be specified to plot/calculate the most common substructure (MCS). Alternatively, -1 may be specified to select all candidates.

For plotSpectrum: two indices can be specified to compare spectra. In this case groupName should specify values for the spectra to compare.

groupName

The name of the feature group (or feature groups when comparing spectra) to which the candidate belongs.

width, height

The dimensions (in pixels) of the raster image that should be plotted.

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.

onlyUsed

If TRUE then only scorings are plotted that actually have been used to rank data (see the scoreTypes argument to generateCompoundsMetFrag for more details).

MSPeakLists

The MSPeakLists object that was used to generate the candidate

onlyAnnotated

Set to TRUE to filter out any peaks that could not be annotated.

plotStruct

If TRUE then the candidate structure is drawn in the spectrum. Currently not supported when comparing spectra.

title

The title of the plot. If NULL a title will be automatically made.

specSimParams

A named list with parameters that influence the calculation of MS spectra similarities. See the spectral similarity parameters documentation for more details.

mincex

The formula annotation labels are automatically scaled. The mincex argument forces a minimum cex value for readability.

xlim, ylim

Sets the plot size limits used by plot. Set to NULL for automatic plot sizing.

maxMolSize

Numeric vector of size two with the maximum width/height of the candidate structure (relative to the plot size).

molRes

Numeric vector of size two with the resolution of the candidate structure (in pixels).

absMinAbundance, relMinAbundance

Minimum absolute or relative (0-1) abundance across objects for a result to be kept. For instance, relMinAbundance=0.5 means that a result should be present in at least half of the number of compared objects. Set to NULL to ignore and keep all results. Limits cannot be set when uniqueFrom is not NULL.

uniqueFrom

Set this argument to only retain compounds that are unique within one or more of the objects for which the consensus is made. Selection is done by setting the value of uniqueFrom to a logical (values are recycled), numeric (select by index) or a character (as obtained with algorithm(obj)). For logical and numeric values the order corresponds to the order of the objects given for the consensus. Set to NULL to ignore.

uniqueOuter

If uniqueFrom is not NULL and if uniqueOuter=TRUE: only retain data that are also unique between objects specified in uniqueFrom.

rankWeights

A numeric vector with weights of to calculate the mean ranking score for each candidate. The value will be re-cycled if necessary, hence, the default value of 1 means equal weights for all considered objects.

labels

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

i, j, drop

Passed to the featureAnnotations method.

sets

(sets workflow) A character with name(s) of the sets to keep (or remove if negate=TRUE). Note: if updateConsensus=FALSE then the setCoverage column of the annotation results is not updated.

updateConsensus

(sets workflow) If TRUE then the annonation consensus among set results is updated. See the Sets workflows section for more details.

negate

Passed to the featureAnnotations method.

perSet, mirror

(sets workflow) If perSet=TRUE then the set specific mass peaks are annotated separately. Furthermore, if mirror=TRUE (and there are two sets in the object) then a mirror plot is generated.

filterSets

(sets workflow) Controls how algorithms concensus abundance filters are applied. See the Sets workflows section below.

setThreshold, setThresholdAnn

(sets workflow) Thresholds used to create the annotation set consensus. See generateCompounds.

setAvgSpecificScores

(sets workflow) If TRUE then set specific annotation scores (e.g. MS/MS and isotopic pattern match scores) are averaged for the set consensus. See generateCompounds.

set

(sets workflow) The name of the set.

Value

addFormulaScoring returns a compounds object updated with formula scoring.

getMCS returns an rcdk molecule object (IAtomContainer).

consensus returns a compounds object that is produced by merging multiple specified compounds objects.

Details

compounds objects are obtained from compound generators. This class is derived from the featureAnnotations class, please see its documentation for more methods and other details.

Methods (by generic)

defaultExclNormScores(compounds): Returns default scorings that are excluded from normalization.
show(compounds): Show summary information for this object.
identifiers(compounds): Returns a list containing for each feature group a character vector with database identifiers for all candidate compounds. The list is named by feature group names, and is typically used with the identifiers option of generateCompoundsMetFrag.
filter(compounds): Provides rule based filtering for generated compounds. Useful to eliminate unlikely candidates and speed up further processing. Also see the featureAnnotations method.
addFormulaScoring(compounds): Adds formula ranking data from a formulas object as an extra compound candidate scoring (formulaScore column). The formula score for each compound candidate is between 0-1, where zero means no match with any formula candidates, and one means that the compound candidate's formula is the highest ranked.
getMCS(compounds): Calculates the maximum common substructure (MCS) for two or more candidate structures for a feature group. This method uses the get.mcs function from rcdk.
plotStructure(compounds): Plots a structure of a candidate compound using the rcdk package. If multiple candidates are specified (i.e. by specifying a vector for index) then the maximum common substructure (MCS) of the selected candidates is drawn.
plotScores(compounds): Plots a barplot with scoring of a candidate compound.
annotatedPeakList(compounds): Returns an MS/MS peak list annotated with data from a given candidate compound for a feature group.
plotSpectrum(compounds): Plots an annotated spectrum for a given candidate compound for a feature group. Two spectra can be compared by specifying a two-sized vector for the index and groupName arguments.
consensus(compounds): Generates a consensus of results from multiple objects. In order to rank the consensus candidates, first each of the candidates are scored based on their original ranking (the scores are normalized and the highest ranked candidate gets value 1). The (weighted) mean is then calculated for all scorings of each candidate to derive the final ranking (if an object lacks the candidate its score will be 0). The original rankings for each object is stored in the rank columns.

Slots

MS2QuantMeta: Metadata from MS2Quant filled in by predictRespFactors.
setThreshold,setThresholdAnn,setAvgSpecificScores: (sets workflow) A copy of the equally named arguments that were passed when this object was created by generateCompounds.
origFGNames: (sets workflow) The original (order of) names of the featureGroups object that was used to create this object.

Note

The values ranges in the scoreLimits slot, which are used for normalization of scores, are based on the original scorings when the compounds were generated (prior to employing the topMost filter to generateCompounds).

S4 class hierarchy

featureAnnotations
- compounds
  - compoundsConsensus
  - compoundsMF
  - compoundsSet
    - compoundsConsensusSet
  - compoundsUnset
  - compoundsSIRIUS

Source

Subscripting of formulae for plots generated by plotSpectrum is based on the chemistry2expression function from the ReSOLUTION package.

Sets workflows

The compoundsSet class is applicable for sets workflows. This class is derived from compounds and therefore largely follows the same user interface.

The following methods are specifically defined for sets workflows:

unset Converts the object data for a specified set into a 'non-set' object (compoundsUnset), which allows it to be used in 'regular' workflows. Only the annotation results that are present in the specified set are kept (based on the set consensus, see below for implications).

The following methods are changed or with new functionality:

filter and the subset operator ([) Can be used to select data that is only present for selected sets. Depending on the updateConsenus, both either operate on set consensus or original data (see below for implications).
annotatedPeakList Returns a combined annotation table with all sets.
plotSpectrum Is able to highlight set specific mass peaks (perSet and mirror arguments).
consensus Creates the algorithm consensus based on the original annotation data (see below for implications). Then, like the sets workflow method for generateCompounds, a consensus is made for all sets, which can be controlled with the setThreshold and setThresholdAnn arguments. The candidate coverage among the different algorithms is calculated for each set (e.g. coverage-positive column) and for all sets (coverage column), which is based on the presence of a candidate in all the algorithms from all sets data. The consensus method for sets workflow data supports the filterSets argument. This controls how the algorithm consensus abundance filters (absMinAbundance/relMinAbundance) are applied: if filterSets=TRUE then the minimum of all coverage set specific columns is used to obtain the algorithm abundance. Otherwise the overall coverage column is used. For instance, consider a consensus object to be generated from two objects generated by different algorithms (e.g. SIRIUS and MetFrag), which both have a positive and negative set. Then, if a candidate occurs with both algorithms for the positive mode set, but only with the first algorithm in the negative mode set, relMinAbundance=1 will remove the candidate if filterSets=TRUE (because the minimum relative algorithm abundance is 0.5), while filterSets=FALSE will not remove the candidate (because based on all sets data the candidate occurs in both algorithms).
addFormulaScoring Adds the formula scorings to the original data and re-creates the annotation set consensus (see below for implications).

Two types of annotation data are stored in a compoundsSet object:

Annotations that are produced from a consensus between set results (see generateCompounds).
The 'original' annotation data per set, prior to when the set consensus was made. This includes candidates that were filtered out because of the thresholds set by setThreshold and setThresholdAnn. However, when filter or subsetting ([) operations are performed, the original data is also updated.

In most cases the first data is used. However, in a few cases the original annotation data is used (as indicated above), for instance, to re-create the set consensus. It is important to realize that the original annotation data may have additional candidates, and a newly created set consensus may therefore have 'new' candidates. For instance, when the object consists of the sets "positive" and "negative" and setThreshold=1 was used to create it, then compounds[, sets = "positive", updateConsensus = TRUE] may now have additional candidates, i.e. those that were not present in the "negative" set and were previously removed due to the consensus threshold filter.

References

Guha R (2007). “Chemical Informatics Functionality in R.” Journal of Statistical Software, 18(6).

Arguments

Value

Details

Methods (by generic)

Slots

Note

S4 class hierarchy

Source

Sets workflows

References

See also