cc_cluster_count

Count the number of clusters based on stability score.

Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.

Behnam Yousefi

ConsensusClustering

Consensus Clustering

cc_cluster_count function

<dl><dt>CM</dt>
<dd>list of consensus matrices each for a specific number of clusters.
It can be the output of <code>consensus_matrix()</code> and <code>multiview_consensus_matrix()</code> functions.</dd>
<dt>plot.cdf</dt>
<dd>binary value to plot the cumulative distribution functions of <code>CM</code> (default <code>TRUE</code>).</dd>
<dt>plot.logit</dt>
<dd>binary value to plot the logit model of cumulative distribution functions of <code>CM</code> (default <code>FALSE</code>).</dd></dl>

Arguments

Count the number of clusters based on stability score. — cc_cluster_count

<dl>

<dt>CM</dt>
<dd>list of consensus matrices each for a specific number of clusters.
It can be the output of <code>consensus_matrix()</code> and <code>multiview_consensus_matrix()</code> functions.</dd>


<dt>plot.cdf</dt>
<dd>binary value to plot the cumulative distribution functions of <code>CM</code> (default <code>TRUE</code>).</dd>


<dt>plot.logit</dt>
<dd>binary value to plot the logit model of cumulative distribution functions of <code>CM</code> (default <code>FALSE</code>).</dd>

</dl>

cc_cluster_count: Count the number of clusters based on stability score.

Description

Usage

Value

Arguments

Details

Examples