PharmacoGx - Analysis of Large-Scale Pharmacogenomic Data

Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line.

Last updated 24 days ago

geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationdatasetspharmacogenomicpharmacogx

11.60 score 65 stars 3 packages 381 scripts 554 downloads

mRMRe - Parallelized Minimum Redundancy, Maximum Relevance (mRMR)

Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique. Published in De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.

Last updated 3 years ago

8.67 score 19 stars 2 packages 104 scripts 1.1k downloads

consensusOV - Gene expression-based subtype classification for high-grade serous ovarian cancer

This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.

Last updated 24 days ago

classificationclusteringdifferentialexpressiongeneexpressionmicroarraytranscriptomicscancer-datacancer-genomicscancer-researchexpression-databaseovarian-cancer

5.98 score 2 stars 1 packages 15 scripts 265 downloads

Xeva - Analysis of patient-derived xenograft (PDX) data

The Xeva package provides efficient and powerful functions for patient-drived xenograft (PDX) based pharmacogenomic data analysis. This package contains a set of functions to perform analysis of patient-derived xenograft data. This package was developed by the BHKLab, for further information please see our documentation.

Last updated 24 days ago

geneexpressionpharmacogeneticspharmacogenomicssoftwareclassification

5.73 score 9 stars 20 scripts 324 downloads

CREAM - Clustering of Genomic Regions Analysis Method

Provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.

Last updated 4 years ago

peakdetectionfunctionalpredictionbiomedicalinformaticsclusteringbiomedical-informaticsfunctional-predictionpeak-detection

5.43 score 12 stars 15 scripts 178 downloads

PDATK - Pancreatic Ductal Adenocarcinoma Tool-Kit

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.

Last updated 24 days ago

geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction

4.31 score 1 stars 17 scripts 372 downloads