modCluster

modCluster,ExprsArray-method

An <code>ExprsArray</code> object. The object containing the subject
data to cluster.

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 character string. The name of the function used to cluster.
Select from "hclust", "kmeans", "agnes", "clara", "diana", "fanny", or
"pam".

A logical sclar. Toggles whether to return a processed
cluster object or an updated <code>ExprsArray</code> object.

onlyCluster

Additional arguments to the cluster function and/or
other functions used for clustering (e.g., <code>dist</code> and
<code>cutree</code>).


This method clusters subjects based on feature data using any one of
 seven available clustering algorithms. See Arguments below.


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

modCluster function

<ul>
<li> <code>ExprsArray</code>: Method to compare <code>ExprsArray</code> objects.
</li>
</ul>

modCluster: Cluster Subjects

Description

Usage

Arguments

Value

Methods (by class)

Details