DESCRIPTION

Package: MoonlightR
Type: Package
Title: Identify oncogenes and tumor suppressor genes from omics data
Version: 1.31.0
Date: 07-08-2020
Authors@R:
  c(person("Antonio", "Colaprico",
           role="aut"),
    person("Catharina", "Olsen",
           role="aut"),
    person("Matthew H.", "Bailey",
           role="aut"),
    person("Gabriel J.", "Odom",
           role="aut"),
    person("Thilde", "Terkelsen",
           role="aut"),
    person("Mona", "Nourbakhsh",
           role="aut"),
    person("Astrid", "Saksager",
           role="aut"),
    person("Tiago C.", "Silva",
           role="aut"),
    person("André V.", "Olsen",
           role="aut"),
    person("Laura", "Cantini",
           role="aut"),
    person("Andrei", "Zinovyev",
           role="aut"),
    person("Emmanuel", "Barillot",
           role="aut"),
    person("Houtan", "Noushmehr",
           role="aut"),
    person("Gloria", "Bertoli",
           role="aut"),
    person("Isabella", "Castiglioni",
           role="aut"),
    person("Claudia","Cava",
           role="aut"),
    person("Gianluca", "Bontempi",
           role="aut"),
    person("Xi Steven", "Chen",
           role="aut"),
    person("Elena", "Papaleo",
           role="aut"),
    person("Matteo", "Tiberti",
           role=c("cre", "aut"),
           email="tiberti@cancer.dk",
           ))
Depends: R (>= 3.5), doParallel, foreach
Imports: parmigene, randomForest, SummarizedExperiment, gplots,
        circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase,
        limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats,
        RISmed, grid, utils
Description: Motivation: The understanding of cancer mechanism requires
        the identification of genes playing a role in the development
        of the pathology and the characterization of their role
        (notably oncogenes and tumor suppressors). Results: We present
        an R/bioconductor package called MoonlightR which returns a
        list of candidate driver genes for specific cancer types on the
        basis of TCGA expression data. The method first infers gene
        regulatory networks and then carries out a functional
        enrichment analysis (FEA) (implementing an upstream regulator
        analysis, URA) to score the importance of well-known biological
        processes with respect to the studied cancer type. Eventually,
        by means of random forests, MoonlightR predicts two specific
        roles for the candidate driver genes: i) tumor suppressor genes
        (TSGs) and ii) oncogenes (OCGs). As a consequence, this
        methodology does not only identify genes playing a dual role
        (e.g. TSG in one cancer type and OCG in another) but also helps
        in elucidating the biological processes underlying their
        specific roles. In particular, MoonlightR can be used to
        discover OCGs and TSGs in the same cancer type. This may help
        in answering the question whether some genes change role
        between early stages (I, II) and late stages (III, IV) in
        breast cancer. In the future, this analysis could be useful to
        determine the causes of different resistances to
        chemotherapeutic treatments.
License: GPL (>= 3)
biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation,
        GeneExpression, MethylationArray, DifferentialExpression,
        Pathways, Network, Survival, GeneSetEnrichment,
        NetworkEnrichment
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2,
        png, edgeR
VignetteBuilder: knitr
LazyData: true
URL: https://github.com/ELELAB/MoonlightR
BugReports: https://github.com/ELELAB/MoonlightR/issues
RoxygenNote: 7.2.3
Encoding: UTF-8