Package: PDATK
Type: Package
Title: Pancreatic Ductal Adenocarcinoma Tool-Kit
Version: 1.19.0
Date: `r Sys.Date()`
Authors@R: c(
    person('Vandana', 'Sandhu', role=c('aut')),
    person('Heewon', 'Seo', role=c('aut')),
    person('Christopher', 'Eeles', role=c('aut')),
    person('Neha', 'Rohatgi', role=c('ctb')),
    person('Benjamin', 'Haibe-Kains', role=c('aut', 'cre'),
        email="benjamin.haibe.kains@utoronto.ca"))
Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively
        poor prognosis and is one of the most lethal cancers. Molecular
        classification of gene expression profiles holds the potential
        to identify meaningful subtypes which can inform therapeutic
        strategy in the clinical setting. The Pancreatic Cancer
        Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based
        interface for performing unsupervised subtype discovery,
        cross-cohort meta-clustering, gene-expression-based
        classification, and subsequent survival analysis to identify
        prognostically useful subtypes in pancreatic cancer and beyond.
        Two novel methods, Consensus Subtypes in Pancreatic Cancer
        (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP)
        are included for consensus-based meta-clustering and
        overall-survival prediction, respectively. Additionally, four
        published subtype classifiers and three published prognostic
        gene signatures are included to allow users to easily recreate
        published results, apply existing classifiers to new data, and
        benchmark the relative performance of new methods. The use of
        existing Bioconductor classes as input to all PDATK classes and
        methods enables integration with existing Bioconductor
        datasets, including the 21 pancreatic cancer patient cohorts
        available in the MetaGxPancreas data package. PDATK has been
        used to replicate results from Sandhu et al (2019)
        [https://doi.org/10.1200/cci.18.00102] and an additional paper
        is in the works using CSPC to validate subtypes from the
        included published classifiers, both of which use the data
        available in MetaGxPancreas. The inclusion of subtype centroids
        and prognostic gene signatures from these and other
        publications will enable researchers and clinicians to classify
        novel patient gene expression data, allowing the direct
        clinical application of the classifiers included in PDATK.
        Overall, PDATK provides a rich set of tools to identify and
        validate useful prognostic and molecular subtypes based on
        gene-expression data, benchmark new classifiers against
        existing ones, and apply discovered classifiers on novel
        patient data to inform clinical decision making.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1), SummarizedExperiment
Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus,
        igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro,
        CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics,
        survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel,
        rlang, piano, scales, survcomp, genefu, ggplot2, switchBox,
        reportROC, pROC, verification, utils
Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr,
        HDF5Array
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE, r6=FALSE)
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics,
        Software, Classification, Survival, Clustering, GenePrediction
BugReports: https://github.com/bhklab/PDATK/issues
RoxygenNote: 7.1.2
Collate: 'class-S4Model.R' 'class-CohortList.R'
        'class-SurvivalExperiment.R' 'class-SurvivalModel.R'
        'class-ClinicalModel.R' 'class-ConsensusMetaclusteringModel.R'
        'class-CoxModel.R' 'class-GeneFuModel.R'
        'class-ModelComparison.R' 'class-NCSModel.R' 'class-PCOSP.R'
        'class-RGAModel.R' 'class-RLSModel.R' 'classUnions.R' 'data.R'
        'globals.R' 'methods-assignSubtypes.R'
        'methods-barPlotModelComparison.R' 'methods-coerce.R'
        'methods-compareModels.R'
        'methods-densityPlotModelComparison.R'
        'methods-dropNotCensored.R' 'methods-findCommonGenes.R'
        'methods-findCommonSamples.R' 'methods-forestPlot.R'
        'methods-getTopFeatures.R' 'methods-merge.R'
        'methods-normalize.R' 'methods-plotNetworkGraph.R'
        'methods-plotROC.R' 'methods-plotSurvivalCurves.R'
        'methods-predictClasses.R' 'methods-rankFeatures.R'
        'methods-runGSEA.R' 'methods-subset.R' 'methods-trainModel.R'
        'methods-validateModel.R' 'utilities.R'
Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libjpeg-dev
        libpng-dev libxml2-dev libssl-dev zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2025-10-29 15:06:36 UTC
RemoteUrl: https://github.com/bioc/PDATK
RemoteRef: HEAD
RemoteSha: 16dbbfebaa511cfd1b60f5075819bfcb31a89196
NeedsCompilation: no
Packaged: 2025-11-02 15:22:12 UTC; root
Author: Vandana Sandhu [aut],
  Heewon Seo [aut],
  Christopher Eeles [aut],
  Neha Rohatgi [ctb],
  Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
Built: R 4.6.0; ; 2025-11-02 15:25:35 UTC; windows
