modFilter

modFilter,ExprsArray-method

Specifies the <code>ExprsArray</code> object to undergo pre-processing.

object

A numeric scalar. The value below which to assign this value.

threshold

A numeric scalar. The value above which to assign this value.

maximum

A numeric scalar. The <code>max - min</code> range above which to
include the feature. Inclusive with <code>beta2</code>.

beta1

A numeric scalar. The <code>max / min</code> ratio above which to
include the feature. Inclusive with <code>beta1</code>.

beta2

A logical scalar. Toggles whether to produce side-by-side plots
of the before and after <code>ExprsArray</code> summary.

plotSkip


<code>modFilter</code> imposes a hard filter for (gene expression) feature data.


Supervised machine learning has an increasingly important role in biological
studies. However, the sheer complexity of classification pipelines poses a significant
barrier to the expert biologist unfamiliar with machine learning. Moreover,
many biologists lack the time or technical skills necessary to establish their own
pipelines. This package introduces a framework for the rapid implementation of
high-throughput supervised machine learning built with the biologist user in mind.
Written by biologists, for biologists, this package provides a user-friendly interface
that empowers investigators to execute state-of-the-art binary and multi-class
classification, including deep learning, with minimal programming
experience necessary.

Thomas Quinn

exprso

Rapid Implementation of Machine Learning Algorithms for Genomic
Data

modFilter function

<ul>
<li> <code>ExprsArray</code>: Method to filter an <code>ExprsArray</code> object.
</li>
</ul>

modFilter: Hard Filter Data

Description

Usage

Arguments

Value

Methods (by class)

Details

See Also