fsEdger: Selects Features by Exact Test

In exprso: Rapid Deployment of Machine Learning Algorithms

Description

fsEdger selects features using the exactTest function from the edgeR package. This function does not normalize the data, but does estimate dispersion using the estimateCommonDisp and estimateTagwiseDisp functions.

Usage

fsEdger(object, top = 0, keep = 0, ...)

Arguments

object	An ExprsArray object to undergo feature selection.
top	A numeric scalar or character vector. A numeric scalar indicates the number of top features that should undergo feature selection. A character vector indicates specifically which features by name should undergo feature selection. Set top = 0 to include all features. A numeric vector can also be used to indicate specific features by location, similar to a character vector.
keep	A numeric scalar. Specifies the number of top features that should get returned by the feature selection method. Use of keep is generally not recommended, but can speed up analyses of large data.
...	Arguments passed to the detailed function.

Details

The user can normalize the data before feature selection using the modTMM function. Note that applying edgeR to already normalized counts differs slightly from applying edgeR with normalization.

Value

Returns an ExprsArray object.

exprso documentation built on May 1, 2019, 7:11 p.m.