Package: metasnf
Title: Meta Clustering with Similarity Network Fusion
Version: 2.3.1
Authors@R: 
    c(
        person(
            "Prashanth S",
            "Velayudhan",
            email = "psvelayu@gmail.com",
            role = c("aut", "cre")
        ),
        person("Xiaoqiao", "Xu", role = c("aut")),
        person("Prajkta", "Kallurkar", role = c("aut")),
        person("Ana Patricia", "Balbon", role = c("aut")),
        person("Maria T", "Secara", role = c("aut")),
        person("Adam", "Taback", role = c("aut")),
        person("Denise", "Sabac", role = c("aut")),
        person("Nicholas", "Chan", role = c("aut")),
        person("Shihao", "Ma", role = c("aut")),
        person("Bo", "Wang", role = c("aut")),
        person("Daniel", "Felsky", role = c("aut")),
        person("Stephanie H", "Ameis", role = c("aut")),
        person("Brian", "Cox", role = c("aut")),
        person("Colin", "Hawco", role = c("aut")),
        person(given = "Lauren", family = "Erdman", role = c("aut")),
        person(
            given = "Anne L",
            family = "Wheeler",
            email = "anne.wheeler@sickkids.ca",
            role = c("aut", "ths")
        )
    )
Description: 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.
License: GPL (>= 3)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Imports: cli, cluster, data.table, digest, dplyr, ggplot2, grDevices,
        MASS, mclust, methods, progressr, purrr, RColorBrewer, rlang,
        SNFtool, stats, tibble, tidyr, utils
Suggests: circlize, ComplexHeatmap, InteractiveComplexHeatmap, future,
        future.apply, knitr, rmarkdown, testthat (>= 3.0.0),
        ggalluvial, lifecycle, dbscan
Config/testthat/edition: 3
Depends: R (>= 4.1.0)
LazyData: true
VignetteBuilder: knitr
URL: https://branchlab.github.io/metasnf/,
        https://github.com/BRANCHlab/metasnf/
BugReports: https://github.com/BRANCHlab/metasnf/issues
Config/pak/sysreqs: libicu-dev
Repository: https://branchlab.r-universe.dev
Date/Publication: 2026-06-19 21:08:41 UTC
RemoteUrl: https://github.com/branchlab/metasnf
RemoteRef: HEAD
RemoteSha: 07a0e486d3f77566818b58e9bc89960012117b7e
NeedsCompilation: no
Packaged: 2026-06-19 23:10:45 UTC; root
Author: Prashanth S Velayudhan [aut, cre],
  Xiaoqiao Xu [aut],
  Prajkta Kallurkar [aut],
  Ana Patricia Balbon [aut],
  Maria T Secara [aut],
  Adam Taback [aut],
  Denise Sabac [aut],
  Nicholas Chan [aut],
  Shihao Ma [aut],
  Bo Wang [aut],
  Daniel Felsky [aut],
  Stephanie H Ameis [aut],
  Brian Cox [aut],
  Colin Hawco [aut],
  Lauren Erdman [aut],
  Anne L Wheeler [aut, ths]
Maintainer: Prashanth S Velayudhan <psvelayu@gmail.com>