batch_row_closure

Generate closure function to run batch_snf in an apply-friendly format

internal

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_row_closure function

<dl><dt>dl</dt>
<dd>A nested list of input data from <code>data_list()</code>.</dd>
<dt>dfl</dt>
<dd>An optional nested list containing which
distance metric function should be used for the various feature types
(continuous, discrete, ordinal, categorical, and mixed). See
?dist_fns_list for details on how to build this.</dd>
<dt>cfl</dt>
<dd>List of custom clustering algorithms to apply
to the final fused network. See ?clust_fns_list.</dd>
<dt>sdf</dt>
<dd>matrix indicating parameters to iterate SNF through.</dd>
<dt>wm</dt>
<dd>A matrix containing feature weights to use during
distance matrix calculation. See ?weights_matrix for details on
how to build this.</dd>
<dt>similarity_matrix_dir</dt>
<dd>If specified, this directory will be used to
save all generated similarity matrices.</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>prog</dt>
<dd>Progressr function to update parallel processing progress</dd></dl>

Arguments

Generate closure function to run batch_snf in an apply-friendly format — batch_row_closure

<dl>

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


<dt>dfl</dt>
<dd>An optional nested list containing which
distance metric function should be used for the various feature types
(continuous, discrete, ordinal, categorical, and mixed). See
?dist_fns_list for details on how to build this.</dd>


<dt>cfl</dt>
<dd>List of custom clustering algorithms to apply
to the final fused network. See ?clust_fns_list.</dd>


<dt>sdf</dt>
<dd>matrix indicating parameters to iterate SNF through.</dd>


<dt>wm</dt>
<dd>A matrix containing feature weights to use during
distance matrix calculation. See ?weights_matrix for details on
how to build this.</dd>


<dt>similarity_matrix_dir</dt>
<dd>If specified, this directory will be used to
save all generated similarity matrices.</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>prog</dt>
<dd>Progressr function to update parallel processing progress</dd>

</dl>

Generate closure function to run batch_snf in an apply-friendly format

batch_row_closure: Generate closure function to run batch_snf in an apply-friendly format

Description

Usage

Value

Arguments