Filter features based on the dispersion ratio

The `DratioFilter` class and method enable users to filter features from an `XcmsExperiment` or `SummarizedExperiment` object based on the D-ratio or *dispersion ratio*. This is defined as the standard deviation for QC samples divided by the standard deviation for biological test samples, for each feature of the object (Broadhurst et al.).

This `filter` is part of the possible dispatch of the generic function `filterFeatures`. Features *above* (`>`) the user-input threshold will be removed from the entire dataset.

Usage

DratioFilter(
  threshold = 0.5,
  qcIndex = integer(),
  studyIndex = integer(),
  na.rm = TRUE,
  mad = FALSE
)

# S4 method for class 'XcmsResult,DratioFilter'
filterFeatures(object, filter, ...)

# S4 method for class 'SummarizedExperiment,DratioFilter'
filterFeatures(object, filter, assay = 1)

Arguments

threshold: `numeric` value representing the threshold. Features with a D-ratio *strictly higher* (`>`) than this will be removed from the entire dataset.
qcIndex: `integer` (or `logical`) vector corresponding to the indices of QC samples.
studyIndex: `integer` (or `logical`) vector corresponding of the indices of study samples.
na.rm: `logical` Indicates whether missing values (`NA`) should be removed prior to the calculations.
mad: `logical` Indicates whether the *Median Absolute Deviation* (MAD) should be used instead of the standard deviation. This is suggested for non-gaussian distributed data.
object: XcmsExperiment or SummarizedExperiment. For an XcmsExperiment object, the featureValues(object) will be evaluated, and for Summarizedesxperiment the assay(object, assay). The object will be filtered.
filter: The parameter object selecting and configuring the type of filtering. It can be one of the following classes: RsdFilter, DratioFilter, PercentMissingFilter or BlankFlag.
...: Optional parameters. For object being an XcmsExperiment: parameters for the featureValues() call.
assay: For filtering of SummarizedExperiment objects only. Indicates which assay the filtering will be based on. Note that the features for the entire object will be removed, but the computations are performed on a single assay. Default is 1, which means the first assay of the object will be evaluated.

Value

For `DratioFilter`: a `DratioFilter` class. `filterFeatures` return the input object minus the features that did not met the user input threshold

References

Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018;14(6):72. doi: 10.1007/s11306-018-1367-3. Epub 2018 May 18. PMID: 29805336; PMCID: PMC5960010.

Author

Philippine Louail