Filter features based on their coefficient of variation

The `RsdFilter` class and methods enable users to filter features from an `XcmsExperiment` or `SummarizedExperiment` object based on their relative standard deviation (coefficient of variation) for a specified threshold.

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

RsdFilter(threshold = 0.3, qcIndex = integer(), na.rm = TRUE, mad = FALSE)

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

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

Arguments

threshold: `numeric` value representing the threshold. Features with a coefficient of variation *strictly higher* (`>`) than this will be removed from the entire dataset.
qcIndex: `integer` (or `logical`) vector corresponding to the indices of QC 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 `RsdFilter`: a `RsdFilter` class. `filterFeatures` return the input object minus the features that did not met the user input threshold.

Note

It is assumed that the abundance values are in natural scale. Abundances in log scale should be first transformed to natural scale before calculating the RSD.

Author

Philippine Louail