get_cluster_solutions

<a href="https://lifecycle.r-lib.org/articles/stages.html#deprecated"><img src="figures/lifecycle-deprecated.svg?package=metasnf&version=2.1.2" alt="[Deprecated]"></a>
Deprecated function for building extracting cluster solutions from a
solutions data frame. Please use <code><a href="/link/t()?package=metasnf&version=2.1.2" data-mini-rdoc="metasnf::t()">t()</a></code> instead.
This function takes in a solutions data frame and returns a data frame containing
the cluster assignments for each uid. It is similar to
'<code>get_clusters()</code>, which takes one solutions data frame row and returns a vector
of cluster assignments' and <code>get_cluster_df()</code>, which takes a solutions
matrix with only one row and returns a data frame with two columns: "cluster"
and "uid" (the UID of the observation).

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

get_cluster_solutions function

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#deprecated'><img src='figures/lifecycle-deprecated.svg' alt='[Deprecated]' /></a>
Deprecated function for building extracting cluster solutions from a
solutions data frame. Please use <code><a href='https://rdrr.io/r/base/t.html'>t()</a></code> instead.
This function takes in a solutions data frame and returns a data frame containing
the cluster assignments for each uid. It is similar to
'<code>get_clusters()</code>, which takes one solutions data frame row and returns a vector
of cluster assignments' and <code>get_cluster_df()</code>, which takes a solutions
matrix with only one row and returns a data frame with two columns: "cluster"
and "uid" (the UID of the observation).

Extract cluster membership information from a sol_df — get_cluster_solutions

Extract cluster membership information from a sol_df

get_cluster_solutions: Extract cluster membership information from a sol_df

Description

Usage

Value

Arguments