batch_snf

This is the core function of the <code>metasnf</code> package. Using the information
stored in a settings_df (see ?settings_df) and a data list
(see ?data_list), run repeated complete SNF pipelines to generate
a broad space of post-SNF cluster solutions.

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

batch_snf function

<dl><dt>dl</dt>
<dd>A nested list of input data from <code>data_list()</code>.</dd>
<dt>sc</dt>
<dd>An <code>snf_config</code> class object which stores all sets of
hyperparameters used to transform data in dl into a cluster solutions. See
<code>?settings_df</code> or
https://branchlab.github.io/metasnf/articles/settings_df.html for more
details.</dd>
<dt>processes</dt>
<dd>Specify number of processes used to complete SNF iterations<ul>
<li><code>1</code> (default) Sequential processing: function will iterate through the
<code>settings_df</code> one row at a time with a for loop. This option will
not make use of multiple CPU cores, but will show a progress bar.</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 SNF iterations 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.</li>
</ul></dd>
<dt>return_sim_mats</dt>
<dd>If TRUE, function will return a list where
the first element is the solutions data frame and the second element is a list
of similarity matrices for each row in the sol_df. Default FALSE.</dd>
<dt>sim_mats_dir</dt>
<dd>If specified, this directory will be used to
save all generated similarity matrices.</dd></dl>

Arguments

Run variations of SNF — batch_snf

<dl>

<dt>dl</dt>
<dd>A nested list of input data from <code>data_list()</code>.</dd>


<dt>sc</dt>
<dd>An <code>snf_config</code> class object which stores all sets of
hyperparameters used to transform data in dl into a cluster solutions. See
<code>?settings_df</code> or
https://branchlab.github.io/metasnf/articles/settings_df.html for more
details.</dd>


<dt>processes</dt>
<dd>Specify number of processes used to complete SNF iterations<ul>
<li><code>1</code> (default) Sequential processing: function will iterate through the
<code>settings_df</code> one row at a time with a for loop. This option will
not make use of multiple CPU cores, but will show a progress bar.</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 SNF iterations 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.</li>
</ul></dd>


<dt>return_sim_mats</dt>
<dd>If TRUE, function will return a list where
the first element is the solutions data frame and the second element is a list
of similarity matrices for each row in the sol_df. Default FALSE.</dd>


<dt>sim_mats_dir</dt>
<dd>If specified, this directory will be used to
save all generated similarity matrices.</dd>

</dl>

Run variations of SNF

batch_snf: Run variations of SNF

Description

Usage

Value

Arguments

Examples