Package: kgraph
Title: Knowledge Graphs Constructions and Visualizations
Version: 1.2.0
Authors@R: c(person("Thomas", "Charlon", role = c("aut", "cre"),
                    email = "charlon@protonmail.com",
                    comment = c(ORCID = "0000-0001-7497-0470")),
             person("Hongyi", "Yuan", role = "ctb", comment = c(ORCID = "0000-0003-2597-1973")),
             person("CELEHS", role = "aut", comment = "<https://celehs.hms.harvard.edu>"),
             person("PARSE Health", role = "aut", comment = "<https://parse-health.org>"))
Description: Knowledge graphs enable to efficiently visualize and gain
        insights into large-scale data analysis results, as p-values
        from multiple studies or embedding data matrices. The usual
        workflow is a user providing a data frame of association
        studies results and specifying target nodes, e.g. phenotypes,
        to visualize. The knowledge graph then shows all the features
        which are significantly associated with the phenotype, with the
        edges being proportional to the association scores. As the user
        adds several target nodes and grouping information about the
        nodes such as biological pathways, the construction of such
        graphs soon becomes complex. The 'kgraph' package aims to
        enable users to easily build such knowledge graphs, and
        provides two main features: first, to enable building a
        knowledge graph based on a data frame of concepts
        relationships, be it p-values or cosine similarities; second,
        to enable determining an appropriate cut-off on cosine
        similarities from a complete embedding matrix, to enable the
        building of a knowledge graph directly from an embedding
        matrix. The 'kgraph' package provides several display, layout
        and cut-off options, and has already proven useful to
        researchers to enable them to visualize large sets of p-value
        associations with various phenotypes, and to quickly be able to
        visualize embedding results. Two example datasets are provided
        to demonstrate these behaviors, and several live 'shiny'
        applications are hosted by the CELEHS laboratory and Parse
        Health, as the KESER Mental Health application
        <https://keser-mental-health.parse-health.org/> based on Hong
        C. (2021) <doi:10.1038/s41746-021-00519-z>.
Imports: dplyr, htmltools, igraph, magrittr, opticskxi, plyr, pROC,
        RColorBrewer, reshape2, sgraph, shiny
Depends: R (>= 3.5.0)
Suggests: bslib, data.table, DT, grid, knitr, nlpembeds, rmarkdown,
        testthat
VignetteBuilder: knitr
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
URL: https://gitlab.com/thomaschln/kgraph
BugReports: https://gitlab.com/thomaschln/kgraph/-/issues
Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libuv1-dev
        libxml2-dev zlib1g-dev
Repository: https://thomaschln.r-universe.dev
Date/Publication: 2025-04-04 19:00:52 UTC
RemoteUrl: https://gitlab.com/thomaschln/kgraph
RemoteRef: HEAD
RemoteSha: 90cf6f20bb782928e099db774ce96b283b50d6b1
NeedsCompilation: no
Packaged: 2026-06-04 08:39:10 UTC; root
Author: Thomas Charlon [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-7497-0470>),
  Hongyi Yuan [ctb] (ORCID: <https://orcid.org/0000-0003-2597-1973>),
  CELEHS [aut] (<https://celehs.hms.harvard.edu>),
  PARSE Health [aut] (<https://parse-health.org>)
Maintainer: Thomas Charlon <charlon@protonmail.com>