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PDATK

This is the development version of PDATK; for the stable release version, see PDATK.

Pancreatic Ductal Adenocarcinoma Tool-Kit


Bioconductor version: Development (3.19)

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>

Citation (from within R, enter citation("PDATK")):

Installation

To install this package, start R (version "4.4") and enter:


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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

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

Details

biocViews Classification, Clustering, GeneExpression, GenePrediction, Pharmacogenetics, Pharmacogenomics, Software, Survival
Version 1.11.0
In Bioconductor since BioC 3.13 (R-4.1) (3 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
System Requirements
URL
Bug Reports https://github.com/bhklab/PDATK/issues
See More
Suggests testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr, HDF5Array
Linking To
Enhances
Depends On Me
Imports Me
Suggests Me
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Build Report Build Report

Package Archives

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

Source Package PDATK_1.11.0.tar.gz
Windows Binary PDATK_1.11.0.zip (64-bit only)
macOS Binary (x86_64) PDATK_1.11.0.tgz
macOS Binary (arm64) PDATK_1.11.0.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