Package: PDATK
Type: Package
Title: Pancreatic Ductal Adenocarcinoma Tool-Kit
Version: 1.12.0
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'
git_url: https://git.bioconductor.org/packages/PDATK
git_branch: RELEASE_3_19
git_last_commit: 857690d
git_last_commit_date: 2024-04-30
Repository: Bioconductor 3.19
Date/Publication: 2024-05-01
NeedsCompilation: no
Packaged: 2024-05-02 01:04:28 UTC; biocbuild
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 4.4.0; ; 2024-05-02 13:15:56 UTC; windows
