fsEbayes

An <code>ExprsArray</code> object to undergo feature selection.

object

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 <code>top = 0</code> to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.

A numeric scalar. Specifies the number of top features that should get
returned by the feature selection method. Use of <code>keep</code> is generally not
recommended, but can speed up analyses of large data.

keep

Arguments passed to the detailed function.

<code>fsEbayes</code> selects features using the <code>lmFit</code> and
 <code>eBayes</code> functions from the <code>limma</code> package. Features
 ranked by the <code>topTableF</code> function.

Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly building and deploying
supervised machine learning in a high-throughput manner. This package provides a
user-friendly interface that empowers investigators to execute state-of-the-art
binary and multi-class classification, as well as regression, with minimal
programming experience necessary.

fsEbayes: Select Features by Moderated t-test

Description

Usage

Arguments

Value