Componentization of adducts, isotopes etc. with cliqueMS

Uses cliqueMS to generate components using the cliqueMS::getCliques function.

Usage

generateComponentsCliqueMS(fGroups, ...)

# S4 method for class 'featureGroups'
generateComponentsCliqueMS(
  fGroups,
  ionization = NULL,
  maxCharge = 1,
  maxGrade = 2,
  ppm = 10,
  adductInfo = NULL,
  absMzDev = defaultLim("mz", "medium"),
  minSize = 2,
  relMinAdductAbundance = 0.75,
  adductConflictsUsePref = TRUE,
  NMConflicts = c("preferential", "mostAbundant", "mostIntense"),
  prefAdducts = c("[M+H]+", "[M-H]-"),
  extraOptsCli = NULL,
  extraOptsIso = NULL,
  extraOptsAnn = NULL,
  parallel = TRUE
)

# S4 method for class 'featureGroupsSet'
generateComponentsCliqueMS(fGroups, ionization = NULL, ...)

Arguments

fGroups

featureGroups object for which components should be generated.

...

(sets workflow) Further arguments passed to the non-sets workflow method.

ionization

Which ionization polarity was used to generate the data: should be "positive" or "negative". If the featureGroups object has adduct annotations, and ionization=NULL, the ionization will be detected automatically.

(sets workflow) This parameter is not supported for sets workflows, as the ionization will always be detected automatically.

maxCharge, maxGrade, ppm

Arguments passed to cliqueMS::getIsotopes and/or cliqueMS::getAnnotation.

adductInfo

Sets the adinfo argument to cliqueMS::getAnnotation. If NULL then the default adduct information from cliqueMS is used (i.e. the positive.adinfo/negative.adinfo package datasets).

absMzDev

Maximum absolute m/z deviation.

minSize

The minimum size of a component. Smaller components than this size will be removed. See note below.

relMinAdductAbundance

The minimum relative abundance (0-1) that an adduct should be assigned to features within the same feature group. See the Feature components section for more details.

adductConflictsUsePref

If set to TRUE, and not all adduct assigments to the features within a feature group are equal and at least one of those adducts is a preferential adduct (prefAdducts argument), then only the features with (the lowest ranked) preferential adduct are considered. In all other cases or when adductConflictsUsePref=FALSE only features with the most frequently assigned adduct is considered. See the Feature components section for more details.

NMConflicts

The strategies to employ when not all neutral masses within a component are equal. Valid options are: "preferential", "mostAbundant" and "mostIntense". Multiple strategies are possible, and will be executed in the given order until one succeeds. See the Feature components section for more details.

prefAdducts

A character vector with one or more preferential adducts. See the Feature components section for more details.

extraOptsCli, extraOptsIso, extraOptsAnn

Named list with further arguments to be passed to cliqueMS::getCliques, cliqueMS::getIsotopes and cliqueMS::getAnnotation, respectively. Set to NULL to ignore.

parallel

If set to TRUE then code is executed in parallel through the future package. Please see the parallelization section in the handbook for more details.

Value

A componentsFeatures derived object.

Details

This function uses cliqueMS to generate components. This function is called when calling generateComponents with algorithm="cliquems".

The grouping of features in each component ('clique') is based on high similarity of chromatographic elution profiles. All features in each component are then annotated with the cliqueMS::getIsotopes and cliqueMS::getAnnotation functions.

Feature components

The returned components are based on so called feature components. Unlike other algorithms, components are first made on a feature level (per analysis), instead of for complete feature groups. In the final step the feature components are converted to 'regular' components by employing a consensus approach with the following steps:

If an adduct assigned to a feature only occurs as a minority compared to other adduct assigments within the same feature group, it is considered as an outlier and removed accordingly (controlled by the relMinAdductAbundance argument).
For features within a feature group, only keep their adduct assignment if it occurs as the most frequent or is preferential (controlled by adductConflictsUsePref and prefAdducts arguments).
Components are made by combining the feature groups for which at least one of their features are jointly present in the same feature component.
Conflicts of neutral mass assignments within a component (i.e. not all are the same) are dealt with. Firstly, all feature groups with an unknown neutral mass are split in another component. Then, if conflicts still occur, the feature groups with similar neutral mass (determined by absMzDev argument) are grouped. Depending on the NMConflicts argument, the group with one or more preferential adduct(s) or that is the largest or most intense is selected, whereas others are removed from the component. In case multiple groups contain preferential adducts, and >1 preferential adducts are available, the group with the adduct that matches first in prefAdducts 'wins'. In case of ties, one of the next strategies in NMConflicts is tried.
If a feature group occurs in multiple components it will be removed completely.
the minSize filter is applied.

IMS workflows

The componentization algorithm is not aware of the IMS dimension. For this reason, no IMS feature groups will be considered for componentization, and direct IMS workflows (see assignMobilitities) are currently not supported.

Sets workflows

In a sets workflow the componentization is first performed for each set independently. The resulting components are then all combined in a componentsSet object. Note that the components themselves are never merged. The components are renamed to include the set name from which they were generated (e.g. "CMP1" becomes "CMP1-positive").

References

Senan O, Aguilar-Mogas A, Navarro M, Capellades J, Noon L, Burks D, Yanes O, Guimera R, Sales-Pardo M (2019). “CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network.” Bioinformatics, 35(20), 4089–4097. doi:10.1093/bioinformatics/btz207 .