Base class for grouped features.

This class holds all the information for grouped features.

Usage

groupTable(object, ...)

groupFeatIndex(fGroups)

groupInfo(fGroups)

unique(x, incomparables = FALSE, ...)

overlap(fGroups, which = NULL, aggregate = TRUE, exclusive = FALSE, ...)

selectIons(fGroups, components, prefAdduct, ...)

groupQualities(fGroups)

groupScores(fGroups)

internalStandards(fGroups)

internalStandardAssignments(fGroups, ...)

normInts(
  fGroups,
  featNorm = "none",
  groupNorm = FALSE,
  normFunc = max,
  standards = NULL,
  ISTDRTWindow = 120,
  ISTDMZWindow = 300,
  minISTDs = 3,
  ...
)

concentrations(fGroups, ...)

toxicities(fGroups, ...)

updateGroups(fGroups, ...)

# S4 method for class 'featureGroups'
names(x)

# S4 method for class 'featureGroups'
analyses(obj)

# S4 method for class 'featureGroups'
replicates(obj)

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

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

# S4 method for class 'featureGroups'
hasIMS(obj)

# S4 method for class 'featureGroups'
fromIMS(obj)

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

# S4 method for class 'featureGroups'
groupTable(object, areas = FALSE, normalized = FALSE)

# S4 method for class 'featureGroups'
analysisInfo(obj, df = FALSE)

# S4 method for class 'featureGroups'
analysisInfo(obj) <- value

# S4 method for class 'featureGroups'
groupInfo(fGroups)

# S4 method for class 'featureGroups'
featureTable(obj)

# S4 method for class 'featureGroups'
getFeatures(obj)

# S4 method for class 'featureGroups'
groupFeatIndex(fGroups)

# S4 method for class 'featureGroups'
groupQualities(fGroups)

# S4 method for class 'featureGroups'
groupScores(fGroups)

# S4 method for class 'featureGroups'
getFeatureQualityNames(
  obj,
  feat = TRUE,
  group = TRUE,
  scores = FALSE,
  totScore = TRUE
)

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

# S4 method for class 'featureGroups'
internalStandards(fGroups)

# S4 method for class 'featureGroups'
internalStandardAssignments(fGroups)

# S4 method for class 'featureGroups'
adducts(obj)

# S4 method for class 'featureGroups'
adducts(obj) <- value

# S4 method for class 'featureGroups'
concentrations(fGroups)

# S4 method for class 'featureGroups'
toxicities(fGroups)

# S4 method for class 'featureGroups,ANY,ANY,missing'
x[i, j, ..., ni, replicates, IMS, results, reorder = FALSE, drop = TRUE]

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

# S4 method for class 'featureGroups'
x$name

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

# S4 method for class 'featureGroups'
export(obj, type, out, IMS = FALSE)

# S4 method for class 'featureGroups'
unique(
  x,
  incomparables = FALSE,
  which,
  aggregate = TRUE,
  relativeTo = NULL,
  outer = FALSE
)

# S4 method for class 'featureGroups'
overlap(fGroups, which, aggregate, exclusive)

# S4 method for class 'featureGroups'
calculatePeakQualities(
  obj,
  weights,
  flatnessFactor,
  featureQualities = NULL,
  featureGroupQualities = NULL,
  avgFunc = mean,
  EICParams = getDefEICParams(window = 0),
  parallel = TRUE
)

# S4 method for class 'featureGroups'
selectIons(
  fGroups,
  components,
  prefAdduct,
  onlyMonoIso = TRUE,
  chargeMismatch = "adduct"
)

# S4 method for class 'featureGroups'
normInts(
  fGroups,
  featNorm = "none",
  groupNorm = FALSE,
  normFunc = max,
  standards = NULL,
  ISTDRTWindow = 120,
  ISTDMZWindow = 300,
  minISTDs = 3,
  ...
)

# S4 method for class 'featureGroups'
getTICs(obj, retentionRange = NULL, MSLevel = 1)

# S4 method for class 'featureGroups'
getBPCs(obj, retentionRange = NULL, MSLevel = 1)

# S4 method for class 'featureGroups'
updateGroups(
  fGroups,
  what = c("ret", "mz", "mobility", "CCS"),
  intWeight = FALSE
)

# S4 method for class 'featureGroupsSet'
sets(obj)

# S4 method for class 'featureGroupsSet'
internalStandardAssignments(fGroups, set = NULL)

# S4 method for class 'featureGroupsSet'
adducts(obj, set, ...)

# S4 method for class 'featureGroupsSet'
adducts(obj, set, reGroup = TRUE) <- value

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

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

# S4 method for class 'featureGroupsSet'
featureTable(obj)

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

# S4 method for class 'featureGroupsSet'
export(obj, type, out, ..., set)

# S4 method for class 'featureGroupsSet'
selectIons(fGroups, components, prefAdduct, ...)

# S4 method for class 'featureGroupsSet'
normInts(
  fGroups,
  featNorm = "none",
  groupNorm = FALSE,
  normFunc = max,
  standards = NULL,
  ISTDRTWindow = 120,
  ISTDMZWindow = 300,
  minISTDs = 3,
  ...
)

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

# S4 method for class 'featureGroupsKPIC2'
delete(obj, ...)

# S4 method for class 'featureGroupsXCMS'
analysisInfo(obj) <- value

# S4 method for class 'featureGroupsXCMS'
delete(obj, ...)

# S4 method for class 'featureGroupsXCMS3'
delete(obj, ...)

Arguments

...

For the "[" operator: ignored.

For delete: passed to the function specified as j.

For normInts: passed to screenSuspects if featNorm="istd".

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

fGroups, obj, x, object

featureGroups object to be accessed.

incomparables

Not used. Included for compatibility with the generic.

which

A character vector with the selection to compare (e.g. replicates, as set by the aggregate argument).

For overlap: can also be a NULL to compare all elements.

aggregate

Specifies how data should be aggregated prior to comparison. Set to FALSE to compare analyses, TRUE to compare replicates or to the name of a column in the analysis information to compare by a custom grouping of analyses.

exclusive

If TRUE then all feature groups are removed that are not unique to the given replicates.

components

The components object that was generated for the given featureGroups object. Obviously, the components must be created with algorithms that support adduct/isotope annotations, such as those from RAMClustR and cliqueMS.

prefAdduct

The 'preferred adduct' (see method description). This is often "[M+H]+" or "[M-H]-".

featNorm

The method applied for feature normalization: "istd", "tic", "conc" or "none". See the Feature intensity normalization section for details.

groupNorm

If TRUE then group normalization is performed. See the Feature intensity normalization section for details.

normFunc

A function to combine data for normalization. See the Feature intensity normalization section for details.

standards

A data.table (or data.frame) with all internal standards. Should follow the format of a suspect list. Only used if featNorm="istd". See the Feature intensity normalization section for details.

(sets workflow) Can also be a list with internal standard lists.

See the suspects argument to screenSuspects for more details.

ISTDRTWindow, ISTDMZWindow

The retention time and m/z windows for IS selection. Only used if featNorm="istd". See the Feature intensity normalization section for details.

minISTDs

The minimum number of IS that should be assigned to each feature (if possible). Only used if featNorm="istd". See the Feature intensity normalization section for details.

areas

If set to TRUE then areas are considered instead of peak intensities.

normalized

If TRUE then normalized intensity data is used (see the Feature intensity normalization section.

df

If TRUE then the returned value is a data.frame, otherwise a data.table.

value

For analysisInfo<-: A data.frame or data.table with updated analysis information.

For adducts<-: A character with adduct annotations assigned to each feature group. The length should equal the number of feature groups. Can be named with feature group names to customize the assignment order.

feat, group

If TRUE then names specific to features and feature groups are returned, respectively.

scores

If TRUE the score names are returned, otherwise the quality names.

totScore

If TRUE (and scores=TRUE) then the name of the total score is included.

i, j

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

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

For [[: should be a scalar value. If j is not specified, i selects by feature groups instead.

For delete: The data to remove from. i are the analyses as numeric index, logical or character, j the feature groups as numeric index, logical or character. 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 a vector of the group intensities, the group name and any other arguments passed as ... to delete. The return value of this function specifies the analyses of the features in the group to be removed (same format as i).

ni

Optional argument. An expression used for subsetting the analyses. The analysis information is first subset and the remaining rows are used to determine for which analyses the results should be kept. The unevaluated ni expression is used to set the i argument of the subset operator of data.table, which therefore brings the advanced subsetting capabilities of data.table (see the data.table documentation for more details). For instance, fGroups[replicate == "standard"] would subset all analyses assigned with the replicate "standard".

replicates

An optional character vector: if specified only keep results for the given replicates (equivalent to the replicates argument to filter).

IMS

(IMS workflow) Specifies which feature groups are considered to be kept ("[") or exported (export) in IMS workflows. The following options are valid:

• "both": Selects IMS and non-IMS features.

• "maybe": Selects non-IMS features and IMS features without assigned IMS precursor.

• FALSE: Selects only non-IMS features.

• TRUE: Selects only IMS features.

For "[": Leave unassigned to perform no IMS selection. For export: keep FALSE as the export formats currently do not support IMS data.

results

Optional argument. If specified only feature groups with results in the specified object are kept. The class of results should be featureAnnotations or components. Multiple objects can be specified in a list: in this case a feature group is kept if it has a result in any of the objects (equivalent to the results argument to filter).

reorder

If TRUE then the order of the analyses is changed to match the order of the i argument.

(sets workflow) If the sets argument is specified (and i is not) then the order of sets is changed instead.

drop

Ignored.

name

The feature group name (partially matched).

type

The export type: "brukerpa" (Bruker ProfileAnalysis), "brukertasq" (Bruker TASQ) or "mzmine" (MZmine).

out

The destination file for the exported data.

relativeTo

A character vector of groupings that should be used for unique comparison. The groupings (e.g. replicates) are configured by the aggregate argument. If NULL then all groupings are used for comparison. Data specified in which are ignored.

outer

If TRUE then only feature groups are kept which do not overlap among those present in the groupings specified by the which parameter.

weights

A named numeric vector that defines the weight for each score to calculate the totalScore. The names of the vector follow the score names. Unspecified weights are defaulted to 1. Example: weights=c(ApexBoundaryRatioScore=0.5, GaussianSimilarityScore=2).

flatnessFactor

Passed to MetaClean as the flatness.factor argument to calculateJaggedness and calculateModality.

featureQualities

Specifies which feature qualities to calculate. Can be NULL (default, calculates all qualities), a character vector with names of qualities to calculate (e.g., c("FWHM2Base", "Symmetry")), or a list of custom quality definitions. See the featureQualities function for more details.

featureGroupQualities

Analogous to featureQualities for feature groups metrics. See the featureGroupQualities function for more details.

avgFunc

The function used to average the peak qualities and scores for each feature group.

EICParams

A named list with parameters used for extracted ion chromatogram (EIC) creation. See the EIC parameters documentation for more details.

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.

onlyMonoIso

Set to TRUE to only keep feature groups that were annotated as monoisotopic. Feature groups are never removed by this setting if no isotope annotations are available.

chargeMismatch

Specifies how to deal with a mismatch in charge between adduct and isotope annotations. Valid values are: "adduct" (ignore isotope annotation), "isotope" (ignore adduct annotation), "none" (ignore both annotations) and "ignore" (don't check for charge mismatches). Important: when OpenMS is used to find features, it already removes any detected non-monoisotopic features by default. Hence, in such case setting chargeMismatch="adduct" is more appropriate.

retentionRange

Range of retention time (in seconds) to collect TIC traces. Should be a numeric vector with length of two containing the min/max values. Set to NULL to ignore.

MSLevel

Integer vector with the ms levels (i.e., 1 for MS1 and 2 for MS2) to obtain TIC traces.

what

A character vector specifying which group-wise values to update. Valid values are "ret" (retention time, in seconds), "mz" and "mobility". At least one must be specified. If "mobility" is specified an no IMS information is present, it will be ignored.

intWeight

If TRUE, calculate intensity-weighted means per group; otherwise use simple arithmetic means.

set

(sets workflow) The name of the set.

reGroup

(sets workflow) Set to TRUE to re-group the features after the adduct annotations are changed. See the Sets workflow section for more details.

sets

(sets workflow) A character with name(s) of the sets to keep.

Value

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

calculatePeakQualities returns a modified object amended with peak qualities and scores.

selectIons returns a featureGroups object with only the selected feature groups and amended with adduct annotations.

normInts returns a featureGroups object, amended with data in the ISTDs and ISTDAssignments slots if featNorm="istd".

Details

The featureGroup class is the workhorse of patRoon: almost all functionality operate on its instantiated objects. The class holds all information from grouped features (obtained from features). This class itself is virtual, hence, objects are not created directly from it. Instead, 'feature groupers' such as groupFeaturesXCMS return a featureGroups derived object after performing the actual grouping of features across analyses.

Methods (by generic)

• names(featureGroups): Obtain feature group names.

• analyses(featureGroups): returns a character vector with the names of the analyses for which data is present in this object.

• replicates(featureGroups): returns a character vector with the names of the replicates for which data is present in this object.

• groupNames(featureGroups): Same as names. Provided for consistency to other classes.

• length(featureGroups): Obtain number of feature groups.

• hasIMS(featureGroups): Returns TRUE if the feature groups object has ion mobility information.

• fromIMS(featureGroups): Returns TRUE if the features were directly generated from IMS data.

• show(featureGroups): Shows summary information for this object.

• groupTable(featureGroups): Accessor for groups slot.

• analysisInfo(featureGroups): Obtain analysisInfo (see analysisInfo slot in features).

• analysisInfo(featureGroups) <- value: Modifies the analysis information of this features object. This is primarily intended to change or add analysis metadata columns or can be used to re-order analysis. The removal or addition of analyses and changes to the "analysis" column are not supported. This function performs several internal updates after analysis information modifications. Hence, never attempt to change the analysisInfo slot directly.

• groupInfo(featureGroups): Accessor for groupInfo slot.

• featureTable(featureGroups): Obtain feature information (see features).

• getFeatures(featureGroups): Accessor for features slot.

• groupFeatIndex(featureGroups): Accessor for ftindex slot.

• groupQualities(featureGroups): Accessor for groupQualities slot.

• groupScores(featureGroups): Accessor for groupScores slot.

• getFeatureQualityNames(featureGroups): Returns feature quality names that were calculated for this object.

• annotations(featureGroups): Accessor for annotations slot.

• internalStandards(featureGroups): Accessor for ISTDs slot.

• internalStandardAssignments(featureGroups): Accessor for ISTDAssignments slot.

• adducts(featureGroups): Returns a named character with adduct annotations assigned to each feature group (if available).

• adducts(featureGroups) <- value: Sets adduct annotations for feature groups.

• concentrations(featureGroups): Accessor for concentrations slot.

• toxicities(featureGroups): Accessor for toxicities slot.

• x[i: Subset on analyses/feature groups.

• x[[i: Extract intensity values.

• $: Extract intensity values for a feature group.

• delete(featureGroups): Completely deletes specified feature groups.

• export(featureGroups): Exports feature groups to a .csv file that is readable to Bruker ProfileAnalysis (a 'bucket table'), Bruker TASQ (an analyte database) or that is suitable as input for the Targeted peak detection functionality of MZmine.

• unique(featureGroups): Obtain a subset with unique feature groups present in one or more analyses, replicates etc.

• overlap(featureGroups): Obtain a subset with feature groups that overlap between a set of specified replicate(s).

• calculatePeakQualities(featureGroups): Calculates peak and group qualities for all features and feature groups. The peak qualities (and scores) are calculated with the features method of this function, and subsequently averaged per feature group. Group metrics are then calculated and scored and scaled by normalizing qualities among all groups and scaling them from 0 (worst) to 1 (best). The totalScore for each group is then calculated as the weighted sum from all feature (group) scores. The getMCTrainData and predictCheckFeaturesSession functions can be used to train and apply Pass/Fail ML models from MetaClean.

• selectIons(featureGroups): uses componentization results to select feature groups with preferred adduct ion and/or isotope annotation. Typically, this means that only feature groups are kept if they are (de-)protonated adducts and are monoisotopic. The adduct annotation assignments for the selected feature groups are copied from the components to the annotations slot. If the adduct for a feature group is unknown, its annotation is defaulted to the 'preferred' adduct, and hence, the feature group will never be removed. Furthermore, if a component does not contain an annotation with the preferred adduct, the most intense feature group is selected instead. Similarly, if no isotope annotation is available, the feature group is assumed to be monoisotopic and thus not removed. An important advantage of selectIons is that it may considerably simplify your dataset. Furthermore, the adduct assignments allow formula/compound annotation steps later in the workflow to improve their annotation accuracy. On the other hand, it is important the componentization results are reliable. Hence, it is highly recommended that, prior to calling selectIons, the settings to generateComponents are optimized and its results are reviewed with checkComponents. Finally, the adducts<- method can be used to manually correct adduct assignments afterwards if necessary.

• normInts(featureGroups): Provides various methods to normalizes feature intensities for each sample analysis or of all features within a feature group. See the Feature intensity normalization section below.

• getTICs(featureGroups): Obtain the total ion chromatogram/s (TICs) of the analyses.

• getBPCs(featureGroups): Obtain the base peak chromatogram/s (BPCs) of the analyses.

• updateGroups(featureGroups): Recalculate group information from feature data.

Slots

groups

Matrix (data.table) with intensities for each feature group (columns) per analysis (rows). Access with groups method.

features

features class associated with this object. Access withfeatureTable methods.

groupInfo

data.table with retention time (ret column, in seconds) and m/z (mz column) for each feature group. Access with groupInfo method.

ftindex

Matrix (data.table) with feature indices for each feature group (columns) per analysis (rows). Each index corresponds to the row within the feature table of the analysis (see featureTable).

groupQualities,groupScores

A data.table with qualities/scores for each feature group (see the calculatePeakQualities method).

annotations

A data.table with adduct annotations for each group (see the selectIons method).

ISTDs

A data.table with screening results for internal standards (filled in by the normInts method).

ISTDAssignments

A list, where each item is named by a feature group and consists of a vector with feature group names of the internal standards assigned to it (filled in by the normInts method).

concentrations,toxicities

A data.table with predicted concentrations/toxicities for each feature group. Assigned by the calculateConcs/calculateTox methods. Use the concentratrions/toxicities methods for access.

groupAlgo,groupArgs,groupVerbose

(sets workflow) Grouping parameters that were used when this object was created. Used by adducts<- and selectIons when these methods perform a re-grouping of features.

annotations,ISTDAssignments

(sets workflow) As the featureGroups slots, but contains the data per set.

annotationsChanged

Set internally by adducts()<- and applied as soon as reGroup=TRUE.

Use of raw HRMS data

The raw data interface of patRoon is used by calculatePeakQualities to process HRMS (or IMS-HRMS) data. Please see its documentation for more information on the supported formats and available configuration options.

S4 class hierarchy

• workflowStep

  • featureGroups

    • featureGroupsSet

      • featureGroupsScreeningSet

    • featureGroupsUnset

    • featureGroupsScreening

      • featureGroupsSetScreeningUnset

    • featureGroupsBruker

    • featureGroupsConsensus

    • featureGroupsEnviMass

    • featureGroupsGreedy

    • featureGroupsIMS

    • featureGroupsKPIC2

    • featureGroupsOpenMS

    • featureGroupsSIRIUS

    • featureGroupsTable

    • featureGroupsBrukerTASQ

    • featureGroupsXCMS

    • featureGroupsXCMS3

Feature intensity normalization

The normInts method performs normalization of feature intensities (and areas). These values are amended in the features slot, while the original intensities/areas are kept. To use the normalized intensities set normalized=TRUE to methods such as plotInt, generateComponentsIntClust and as.data.table. Please see the normalized argument documentation for these methods for more details.

The normInts method supports several methods to normalize intensities/areas of features within the same analysis. Most methods are influenced by the normalization concentration (norm_conc in the analysis information) set for each sample analysis. For NA or zero values the output will be zero. If norm_conc is completely absent from the analysis information or all values are NA, the normalization concentration is defaulted to one.

The different normalization methods are:

1. featNorm="istd" Uses internal standards (IS) for normalization. The IS are screened internally by the screenSuspects function. Hence, the IS specified by the standards argument should follow the format of a suspect list. Note that labelled elements in IS formulae should be specified with the rcdk format, e.g. "[13]C" for 13C, "[2]H" for a deuterium etc. Example IS lists are provided with the patRoonData and patRoonDataIMS packages.

   The assignment of IS to features is automatically performed, using the following criteria:

   1. Only analyses are considered with a defined normalization concentration.

   2. The IS must be detected in all of the analyses in which the feature was detected.

   3. The retention time and m/z are reasonably close (ISTDRTWindow/ISTDMZWindow arguments). However, additional IS candidates outside these windows will be chosen if the number of candidates is less than the minISTDs argument. In this case the next close(st) candidate(s) will be chosen.

   Normalization of features within the same feature group always occur with the same IS. If multiple IS are assigned to a feature then normalization occurs with the combined intensity (area), which is calculated with the function defined by the normFunc argument. The (combined) IS intensity is then normalized by the normalization concentration, and finally used for feature normalization.

2. featNorm="tic" Uses the Total Ion Current (TIC) to normalize intensities. The TIC is calculated by combining all intensities with the function defined by the normFunc argument. For this reason, you may need to take care to perform normalization before e.g. suspect screening or other prioritization techniques. The TIC normalized intensities are finally divided by the normalization concentration.

3. featNorm="conc" Simply divides all intensities (areas) with the normalization concentration defined for the sample.

4. featNorm="none" Performs no normalization. The raw intensity values are simply copied. This is mainly useful if you only want to do group normalization (described below).

The meaning of the normalization concentration differs for each method: for "istd" it resembles the IS concentration of a sample analysis, whereas for "tic" and "conc" it is used to normalize different sample amounts (e.g. injection volume).

If groupNorm=TRUE then feature intensities (areas) will be normalized by the combined values for its feature group (again, combination occurs with normFunc). This group normalization always occurs after aforementioned normalization methods. Group normalization was the only method with patRoon <2.1, and still occurs automatically if normInts was not called when a method is executed that requests normalized data.

In IMS workflows with post mobility assignment (see assignMobilities), any IMS features are excluded for the assignment of internal standards (featNorm="istd") or calculation of TICs (featNorm="tic"). Furthermore, the normalized intensities and areas for IMS features are copied from their IMS precursors.

Sets workflows

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

The following methods are specifically defined for sets workflows:

• sets Returns the set names for this object.

• unset Converts the object data for a specified set into a 'non-set' object (featureGroupsUnset), which allows it to be used in 'regular' workflows. The adduct annotations for the selected set are used to convert all feature (group) masses to ionic m/z values. The annotations persist in the converted object.

The following methods are changed or with new functionality:

• adducts, adducts<- require the set argument. The order of the data that is returned/changed follows that of the annotations slot. Furthermore, adducts<- will perform a re-grouping of features when its reGroup parameter is set to TRUE. The implications for this are discussed below. Note that no adducts are changed until reGroup=TRUE.

• the subset operator ([) has specific arguments to choose (feature presence in) sets. See the argument descriptions.

• export Only allows to export data from one set. The unset method is used prior to exporting the data.

• overlap and unique allow to handle data per set. See the sets argument description.

• selectIons Will perform a re-grouping of features. The implications of this are discussed below.

• normInts Performs normalization for each set independently.

A re-grouping of features occurs if selectIons is called or adducts<- is used with reGroup=TRUE. Afterwards, it is very likely that feature group names are changed. Since data generated later in the workflow (e.g. annotation steps) rely on feature group names, these objects are not valid anymore, and must be re-generated.

References

Chetnik K, Petrick L, Pandey G (2020). “MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.” Metabolomics, 16(11). doi:10.1007/s11306-020-01738-3 .

See also

groupFeatures for generating feature groups, feature-filtering, feature-table and feature-plotting for more advanced featureGroups methods.

Author

Rick Helmus <r.helmus@uva.nl> and Ricardo Cunha <cunha@iuta.de> (getTICs and getBPCs functions)