PDATK

DOI: 10.18129/B9.bioc.PDATK  

This package is for version 3.16 of Bioconductor; for the stable, up-to-date release version, see PDATK.

Pancreatic Ductal Adenocarcinoma Tool-Kit

Bioconductor version: 3.16

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.

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 at utoronto.ca>

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("PDATK")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("PDATK")

 

HTML	R Script	PCOSP: Pancreatic Cancer Overall Survival Predictor
HTML	R Script	PDATK_introduction.html
PDF		Reference Manual
Text		LICENSE

Details

biocViews	Classification, Clustering, GeneExpression, GenePrediction, Pharmacogenetics, Pharmacogenomics, Software, Survival
Version	1.6.0
In Bioconductor since	BioC 3.13 (R-4.1) (2 years)
License	MIT + file LICENSE
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
LinkingTo
Suggests	testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr, HDF5Array
SystemRequirements
Enhances
URL
BugReports	https://github.com/bhklab/PDATK/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	PDATK_1.6.0.tar.gz
Windows Binary	PDATK_1.6.0.zip
macOS Binary (x86_64)	PDATK_1.6.0.tgz
macOS Binary (arm64)	PDATK_1.6.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/PDATK
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/PDATK
Bioc Package Browser	https://code.bioconductor.org/browse/PDATK/
Package Short Url	https://bioconductor.org/packages/PDATK/
Package Downloads Report	Download Stats