Predicting FingerPrint and compounds Identifications

This function creates an object of class predictParam that can be used to predict molecular fingerprints and compound identifications using CSI:FingerID and CANOPUS.

CSI:FIngerID identifies the structure of a molecule by predicting its molecular fingerprint and using this fingerprint to search in a molecular structure database.

CANOPUS (Dührkop et al.) predicts the presense/absense of more than 2500 compound classes. CANOPUS predicts these classes based solely on MS/MS data and without requiring database information. This means it can identify a class even if no molecular structure of that class exists in the molecular structure database.

Usage

predictParam(useScoreThreshold = TRUE, alwaysPredictHighRefMatches = FALSE)

Arguments

useScoreThreshold: logical whether to use a soft threshold to be applied to only compute FingerPrints for promising formula candidates. Enabling is highly recommended. Default is TRUE.
alwaysPredictHighRefMatches: logical wether to predict FingerPrint/Classes&Structure for formulas candidates with reference spectrum similarity > Sirius.minReferenceMatchScoreToInject no matter which score threshold rules apply. Default is FALSE.

Note

For more information, see the Sirius documentation.

References

reference

Examples

# Example of setting up the parameters for the prediction of molecular
# fingerprints and compound class
param <- predictParam()