Package: rScudo
Type: Package
Title: Signature-based Clustering for Diagnostic Purposes
Version: 1.19.0
Authors@R: c(person("Matteo", "Ciciani", email = "matteo.ciciani@gmail.com",
    role = c("aut", "cre")), person("Thomas", "Cantore",
    email = "cantorethomas@gmail.com", role = "aut"), person("Enrica",
    "Colasurdo", email = "enrica.colasurdo@gmail.com", role = "ctb"), person(
    "Mario", "Lauria", email = "mario.lauria@unitn.it", role = c("ctb")))
Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a 
    rank-based method for the analysis of gene expression profiles for
    diagnostic and classification purposes. It is based on the identification of
    sample-specific gene signatures composed of the most up- and down-regulated
    genes for that sample. Starting from gene expression data, functions in this
    package identify sample-specific gene signatures and use them to build a
    graph of samples. In this graph samples are joined by edges if they have a
    similar expression profile, according to a pre-computed similarity matrix.
    The similarity between the expression profiles of two samples is computed
    using a method similar to GSEA. The graph of samples can then be used to
    perform community clustering or to perform supervised classification of
    samples in a testing set.
License: GPL-3
Encoding: UTF-8
LazyData: true
URL: https://github.com/Matteo-Ciciani/scudo
BugReports: https://github.com/Matteo-Ciciani/scudo/issues
Collate:
    'class.R'
    'accessors.R'
    'packageDoc.R'
    'utilities.R'
    'scudoClassifyUtilities.R'
    'scudoClassify.R'
    'scudoTrain.R'
    'scudoCytoscape.R'
    'scudoModel.R'
    'scudoNetwork.R'
    'scudoPlot.R'
    'scudoTest.R'
    'show.R'
RoxygenNote: 6.1.1
Depends: R (>= 3.6)
Imports: methods,
    stats,
    igraph,
    stringr,
    grDevices,
    Biobase,
    S4Vectors,
    SummarizedExperiment,
    BiocGenerics
Suggests: testthat,
    BiocStyle,
    knitr,
    rmarkdown,
    ALL,
    RCy3,
    caret,
    e1071,
    parallel,
    doParallel
VignetteBuilder: knitr
biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics,
    Classification, Clustering, GraphAndNetwork, Network, Proteomics,
    Transcriptomics, SystemsBiology, FeatureExtraction