{ "_id": "6a1ac0841d7bb097a09d6ece", "Package": "PDATK", "Type": "Package", "Title": "Pancreatic Ductal Adenocarcinoma Tool-Kit", "Version": "1.21.0", "Date": "`r Sys.Date()`", "Authors@R": "c(\nperson('Vandana', 'Sandhu', role=c('aut')),\nperson('Heewon', 'Seo', role=c('aut')),\nperson('Christopher', 'Eeles', role=c('aut')),\nperson('Neha', 'Rohatgi', role=c('ctb')),\nperson('Benjamin', 'Haibe-Kains', role=c('aut', 'cre'),\nemail=\"benjamin.haibe.kains@utoronto.ca\"))", "Description": "Pancreatic ductal adenocarcinoma (PDA) has a relatively\npoor prognosis and is one of the most lethal cancers. Molecular\nclassification of gene expression profiles holds the potential\nto identify meaningful subtypes which can inform therapeutic\nstrategy in the clinical setting. The Pancreatic Cancer\nAdenocarcinoma Tool-Kit (PDATK) provides an S4 class-based\ninterface for performing unsupervised subtype discovery,\ncross-cohort meta-clustering, gene-expression-based\nclassification, and subsequent survival analysis to identify\nprognostically useful subtypes in pancreatic cancer and beyond.\nTwo novel methods, Consensus Subtypes in Pancreatic Cancer\n(CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP)\nare included for consensus-based meta-clustering and\noverall-survival prediction, respectively. Additionally, four\npublished subtype classifiers and three published prognostic\ngene signatures are included to allow users to easily recreate\npublished results, apply existing classifiers to new data, and\nbenchmark the relative performance of new methods. The use of\nexisting Bioconductor classes as input to all PDATK classes and\nmethods enables integration with existing Bioconductor\ndatasets, including the 21 pancreatic cancer patient cohorts\navailable in the MetaGxPancreas data package. PDATK has been\nused to replicate results from Sandhu et al (2019)\n[https://doi.org/10.1200/cci.18.00102] and an additional paper\nis in the works using CSPC to validate subtypes from the\nincluded published classifiers, both of which use the data\navailable in MetaGxPancreas. The inclusion of subtype centroids\nand prognostic gene signatures from these and other\npublications will enable researchers and clinicians to classify\nnovel patient gene expression data, allowing the direct\nclinical application of the classifiers included in PDATK.\nOverall, PDATK provides a rich set of tools to identify and\nvalidate useful prognostic and molecular subtypes based on\ngene-expression data, benchmark new classifiers against\nexisting ones, and apply discovered classifiers on novel\npatient data to inform clinical decision making.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "Roxygen": "list(markdown = TRUE, r6=FALSE)", "biocViews": "GeneExpression, Pharmacogenetics, Pharmacogenomics,\nSoftware, Classification, Survival, Clustering, GenePrediction", "BugReports": "https://github.com/bhklab/PDATK/issues", "RoxygenNote": "7.1.2", "Collate": "'class-S4Model.R' 'class-CohortList.R'\n'class-SurvivalExperiment.R' 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ECDF of clustering consensus at two points in a subinterval.", "topics": [ "optimalKMinimizeAmbiguity" ] }, { "page": "PCOSP", "title": "Pancreatic Cancer Overall Survival Predictor (PCOSP) Constructor", "topics": [ "PCOSP" ] }, { "page": "PCOSP_or_ClinicalModel-class", "title": "Class Union for PCOSP and ClinicalModel Types", "topics": [ "PCOSP_or_ClinicalModel-class" ] }, { "page": "PCOSP_or_RLS_or_RGA-class", "title": "Class Union for PCOSP, RLSModel and RGAModel Types", "topics": [ "PCOSP_or_RLS_or_RGA-class" ] }, { "page": "PCOSP-class", "title": "Pancreatic Cancer Overall Survival Predictor (PCOSP) Class", "topics": [ ".PCOSP", "PCOSP-class" ] }, { "page": "plotNetworkGraph", "title": "A Generic for Plotting a Network Graph From an S4 Object", "topics": [ "plotNetworkGraph" ] }, { "page": "plotNetworkGraph-NCSModel-method", "title": "Plot a Network Graph for a Classified NCSModel Object", "topics": [ "plotNetworkGraph,NCSModel-method" ] }, { "page": "plotROC", "title": "Plot ROC curves for an 'S4' object", "topics": [ "plotROC" ] }, { "page": "plotROC-PCOSP-method", "title": "Plot ROC curves for a 'PCOSP' model object.", "topics": [ "plotROC,PCOSP-method" ] }, { "page": "plotSurvivalCurves", "title": "Generic for Plotting Survival Curves from an 'S4' Object", "topics": [ "plotSurvivalCurves" ] }, { "page": "plotSurvivalCurves-CoxModel-method", "title": "Plot Survival Curves from a Fit 'CoxModel' Object", "topics": [ "plotSurvivalCurves,CoxModel-method" ] }, { "page": "predictClasses", "title": "Predict Classes for New Data Based on a Train Classifier Model", "topics": [ "predictClasses" ] }, { "page": "predictClasses-CohortList-ClinicalModel-method", "title": "Use a Clinical GLM to Predict Classes for a 'CohortList' of 'SurvivalExperment' Objects.", "topics": [ "predictClasses,CohortList,ClinicalModel-method" ] }, { "page": "predictClasses-CohortList-GeneFuModel-method", "title": "Use a Gene Signature Based Prediciton Model from the 'genefu' Package to Predict Signature Scores for Each Sample", "topics": [ "predictClasses,CohortList,GeneFuModel-method" ] }, { "page": "predictClasses-CohortList-PCOSP_or_RLS_or_RGA-method", "title": "Predict Survival Prognosis Classes and Risk Scores for A 'CohortList' Using a 'PCOSP', 'RLSModel' or 'RGAModel' object.", "topics": [ "predictClasses,CohortList,PCOSP_or_RLS_or_RGA-method" ] }, { "page": "predictClasses-ConsensusMetaclusteringModel-ANY-method", "title": "Compute the Optimal Clustering Solution for a Trained ConsensusMetaclusteringModel", "topics": [ "predictClasses,ConsensusMetaclusteringModel,ANY-method" ] }, { "page": "predictClasses-NCSModel-ANY-method", "title": "Predict Metacluster Labels for a NetworkCommunitySearchModel", "topics": [ "predictClasses,NCSModel,ANY-method" ] }, { "page": "predictClasses-SurvivalExperiment-ClinicalModel-method", "title": "Predict Survival Prognosis Classes and Risk Scores for A 'SurvivalModel' Using a 'ClinicalModel' Object.", "topics": [ 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micro-array cohort from 'MetaGxPancreas' as training data.", "topics": [ "sampleRLSmodel" ] }, { "page": "sampleTrainedPCOSPmodel", "title": "A Sample Trained PCOSP Model Containing the ICGC micro-array cohort from 'MetaGxPancreas' as training data.", "topics": [ "sampleTrainedPCOSPmodel" ] }, { "page": "sampleValPCOSPmodel", "title": "Sample Validated PCOSP Model for Plotting Examples", "topics": [ "sampleValPCOSPmodel" ] }, { "page": "show-S4Model-method", "title": "Show method for Classes Inheriting from 'S4Model'", "topics": [ "show,S4Model-method" ] }, { "page": "subset-CohortList-method", "title": "Subset method for a 'CohortList'", "topics": [ "subset,CohortList-method" ] }, { "page": "SurvivalExperiment", "title": "Constructor for 'SurvivalExperiment' Class Builds a 'SurvivalExperiment' object, which is just a wrapper for a 'SummarizedExperiment' with mandatory survival metadata numeric columns 'survival_time' and 'event_occurred'.", "topics": [ "SurvivalExperiment" ] }, { 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