weights_matrix

Function for building a weights matrix independently of an SNF config. The
weights matrix contains one row corresponding to each row of the settings
data frame in an SNF config (one row for each resulting cluster solution)
and one column for each feature in the data list used for clustering. Values
of the weights matrix are passed to distance metrics functions during the
conversion of input data frames to distance matrices. Typically, there is no
need to use this function directly. Instead, users should provide weights
matrix-building parameters to the <code>snf_config()</code> function.

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

weights_matrix function

<dl><dt>dl</dt>
<dd>A nested list of input data from <code>data_list()</code>.</dd>
<dt>n_solutions</dt>
<dd>Number of rows to generate the template weights matrix
for.</dd>
<dt>weights_fill</dt>
<dd>String indicating what to populate generate rows with.
Can be "ones" (default; fill matrix with 1), "uniform" (fill matrix with
uniformly distributed random values), or "exponential" (fill matrix with
exponentially distributed random values).</dd></dl>

Arguments

Generate a matrix to store feature weights — weights_matrix

<dl>

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


<dt>n_solutions</dt>
<dd>Number of rows to generate the template weights matrix
for.</dd>


<dt>weights_fill</dt>
<dd>String indicating what to populate generate rows with.
Can be "ones" (default; fill matrix with 1), "uniform" (fill matrix with
uniformly distributed random values), or "exponential" (fill matrix with
exponentially distributed random values).</dd>

</dl>

Generate a matrix to store feature weights

weights_matrix: Generate a matrix to store feature weights

Description

Usage

Value

Arguments

Examples