Interactive GUI utilities to check workflow data

These functions provide interactive utilities to explore and review workflow data using a shiny graphical user interface (GUI). In addition, unsatisfactory data (e.g. noise identified as a feature and unrelated feature groups in a component) can easily be selected for removal.

checkFeatures(
  fGroups,
  session = "checked-features.yml",
  EICParams = getDefEICParams(),
  clearSession = FALSE
)

checkComponents(
  components,
  fGroups,
  session = "checked-components.yml",
  EICParams = getDefEICParams(),
  clearSession = FALSE
)

# S4 method for class 'components'
checkComponents(
  components,
  fGroups,
  session = "checked-components.yml",
  EICParams = getDefEICParams(),
  clearSession = FALSE
)

importCheckFeaturesSession(
  sessionIn,
  sessionOut,
  fGroups,
  rtWindow = 6,
  mzWindow = 0.002,
  overWrite = FALSE
)

# S4 method for class 'featureGroups'
checkFeatures(
  fGroups,
  session = "checked-features.yml",
  EICParams = getDefEICParams(),
  clearSession = FALSE
)

getMCTrainData(fGroups, session)

predictCheckFeaturesSession(fGroups, session, model = NULL, overWrite = FALSE)

Arguments

fGroups

A featureGroups object.

This should be the 'new' object for importCheckFeaturesSession for which the session needs to be imported.

session

The session file name.

EICParams

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

clearSession

If TRUE the session will be completely cleared before starting the GUI. This effectively removes all selections for data removal.

components

The components to be checked.

sessionIn, sessionOut

The file names for the input and output sessions.

rtWindow

The retention time window (seconds) used to relate 'old' with 'new' feature groups.

mzWindow

The m/z window (in Da) used to relate 'old' with 'new' feature groups.

overWrite

Set to TRUE to overwrite the output session file if it already exists. If FALSE, the function will stop with an error message.

model

The model that was created with MetaClean and that should be used to predict pass/fail data. If NULL, the example model of the MetaCleanData package is used.

Value

A dataframe with the class predictions as well as the associated probabilities for each EIC as returned by the MetaClean::getPredicitons function. The dataframe has the four columns: EIC, Pred_Class, Pred_Prob_Pass, Pred_Prob_Fail.

Details

The data selected for removal is stored in sessions. These are YAML files to allow easy external manipulation. The sessions can be used to restore the selections that were made for data removal when the GUI tool is executed again. Furthermore, functionality is provided to import and export sessions. To actually remove the data the filter method should be used with the session file as input.

checkComponents is used to review components and their feature groups contained within. A typical use case is to verify that peaks from features that were annotated as related adducts and/or isotopes are correctly aligned.

importCheckFeaturesSession is used to import a session file that was generated from a different featureGroups object. This is useful to avoid re-doing manual interpretation of chromatographic peaks when, for instance, feature group data is re-created with different parameters.

checkFeatures is used to review chromatographic information for feature groups. Its main purpose is to assist in reviewing the quality of detected feature (groups) and easily select unwanted data such as features with poor peak shapes or noise.

getMCTrainData converts a session created by checkFeatures to a data.frame that can be used by the MetaClean to train a new model. The output format is comparable to that from getPeakQualityMetrics.

predictCheckFeaturesSession Uses ML data from MetaClean to predict the quality (Pass/Fail) of feature group data, and converts this to a session which can be reviewed with checkFeatures and used to remove unwanted feature groups by filter.

Note

The topMost and topMostByRGroup EIC parameters (EICParams) are ignored.

checkComponents: Some componentization algorithms (e.g. generateComponentsNontarget and generateComponentsTPs) may output components where the same feature group in a component is present multiple times, for instance, when multiple TPs are matched to the same feature group. If such a feature group is selected for removal, then all of its result in the component will be marked for removal.

getMCTrainData only uses session data for selected feature groups. Selected features for removal are ignored, as this is not supported by MetaClean.

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 .