calc_aris

This function constructs an <code>ari_matrix</code> class object from a <code>solutions_df</code>
class object. The ARI matrix stores pairwise adjusted Rand indices for all
cluster solutions as well as a numeric order for the solutions data frame
based on the hierarchical clustering of the ARI matrix.

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

Prashanth S Velayudhan

metasnf

Meta Clustering with Similarity Network Fusion

Xiaoqiao Xu

Prajkta Kallurkar

Ana Patricia Balbon

Maria T Secara

Adam Taback

Denise Sabac

Nicholas Chan

Shihao Ma

Bo Wang

Daniel Felsky

Stephanie H Ameis

Brian Cox

Colin Hawco

Lauren Erdman

Anne L Wheeler

calc_aris function

<dl><dt>sol_df</dt>
<dd>Solutions data frame containing cluster solutions to calculate
pairwise ARIs for.</dd>
<dt>processes</dt>
<dd>Specify number of processes used to complete calculations<ul>
<li><code>1</code> (default) Sequential processing</li>
<li><code>2</code> or higher: Parallel processing will use the
<code><a href="https://future.apply.futureverse.org/reference/future_apply.html">future.apply::future_apply</a></code> to distribute the calculations across
the specified number of CPU cores. If higher than the number of
available cores, a warning will be raised and the maximum number of
cores will be used.</li>
<li><code>max</code>: All available cores will be used.
Note that no progress indicator is available during multi-core processing.</li>
</ul></dd>
<dt>verbose</dt>
<dd>If TRUE, output progress to console.</dd>
<dt>dist_method</dt>
<dd>Distance method to use when calculating sorting order to
of the matrix. Argument is directly passed into stats::dist. Options
include "euclidean", "maximum", "manhattan", "canberra", "binary", or
"minkowski".</dd>
<dt>hclust_method</dt>
<dd>Agglomerative method to use when calculating sorting
order by <code><a href="/link/stats%3A%3Ahclust?package=metasnf&version=2.1.2" data-mini-rdoc="metasnf::stats::hclust">stats::hclust</a></code>. Options include "ward.D", "ward.D2", "single",
"complete", "average", "mcquitty", "median", or "centroid".</dd></dl>

Arguments

Construct an ARI matrix storing inter-solution similarities — calc_aris

<dl>

<dt>sol_df</dt>
<dd>Solutions data frame containing cluster solutions to calculate
pairwise ARIs for.</dd>


<dt>processes</dt>
<dd>Specify number of processes used to complete calculations<ul>
<li><code>1</code> (default) Sequential processing</li>
<li><code>2</code> or higher: Parallel processing will use the
<code><a href='https://future.apply.futureverse.org/reference/future_apply.html'>future.apply::future_apply</a></code> to distribute the calculations across
the specified number of CPU cores. If higher than the number of
available cores, a warning will be raised and the maximum number of
cores will be used.</li>
<li><code>max</code>: All available cores will be used.
Note that no progress indicator is available during multi-core processing.</li>
</ul></dd>


<dt>verbose</dt>
<dd>If TRUE, output progress to console.</dd>


<dt>dist_method</dt>
<dd>Distance method to use when calculating sorting order to
of the matrix. Argument is directly passed into stats::dist. Options
include "euclidean", "maximum", "manhattan", "canberra", "binary", or
"minkowski".</dd>


<dt>hclust_method</dt>
<dd>Agglomerative method to use when calculating sorting
order by <code><a href='https://rdrr.io/r/stats/hclust.html'>stats::hclust</a></code>. Options include "ward.D", "ward.D2", "single",
"complete", "average", "mcquitty", "median", or "centroid".</dd>

</dl>

Construct an ARI matrix storing inter-solution similarities

calc_aris: Construct an ARI matrix storing inter-solution similarities

Description

Usage

Value

Arguments

Examples