spectralClusteringAffinity

Perform spectral clustering algorithms for an affinity matrix, using SNFtool::spectralClustering.

Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly
used in clinical data. Current consensus clustering methods tend to ensemble a number of different
clusters from mathematical replicates with similar sample coverage. As the fact of common variety
of sample coverage in the real-world data, a new consensus clustering strategy dealing with
such biological replicates is required. This is a two-step consensus clustering package, which
is used to input multiple predictive labels with different sample coverage (missing labels).

Chuanxing Li

ccml

Consensus Clustering for Different Sample Coverage Data

Meng Zhou

spectralClusteringAffinity function

<dl><dt>affi_matrix</dt>
<dd>A numerical similarity or affinity matrix.</dd>
<dt>k</dt>
<dd>A number value of clusters</dd>
<dt>type</dt>
<dd>The variants of spectral clustering to use. See <code>SNFtool::spectralClustering</code></dd></dl>

Arguments

Perform spectral clustering algorithms for an affinity matrix, using SNFtool::spectralClustering. — spectralClusteringAffinity

<dl>

<dt>affi_matrix</dt>
<dd>A numerical similarity or affinity matrix.</dd>


<dt>k</dt>
<dd>A number value of clusters</dd>


<dt>type</dt>
<dd>The variants of spectral clustering to use. See <code>SNFtool::spectralClustering</code></dd>

</dl>

spectralClusteringAffinity: Perform spectral clustering algorithms for an affinity matrix, using SNFtool::spectralClustering.

Description

Usage

Value

Arguments