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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.

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geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationdatasetspharmacogenomicpharmacogxcpp

11.15 score 70 stars 3 dependents 468 scripts 670 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>.

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cppopenmp

9.52 score 21 stars 2 dependents 142 scripts 5.3k downloads

survcomp - Performance Assessment and Comparison for Survival Analysis

Assessment and Comparison for Performance of Risk Prediction (Survival) Models.

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geneexpressiondifferentialexpressionvisualizationcpp

9.01 score 14 dependents 508 scripts 2.4k downloads

genefu - Computation of Gene Expression-Based Signatures in Breast Cancer

This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis.

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differentialexpressiongeneexpressionvisualizationclusteringclassification

7.40 score 3 dependents 281 scripts 848 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.

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geneexpressionpharmacogeneticspharmacogenomicssoftwareclassification

6.59 score 11 stars 35 scripts 344 downloads

CoreGx - Classes and Functions to Serve as the Basis for Other 'Gx' Packages

A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) <doi:10.1093/bioinformatics/btv723>. Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) <doi:10.1101/449793>.

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softwarepharmacogenomicsclassificationsurvival

6.52 score 7 dependents 79 scripts 766 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.

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classificationclusteringdifferentialexpressiongeneexpressionmicroarraytranscriptomicscancer-datacancer-genomicscancer-researchexpression-databaseovarian-cancer

6.18 score 3 stars 1 dependents 24 scripts 416 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.

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peakdetectionfunctionalpredictionbiomedicalinformaticsclusteringbiomedical-informaticsfunctional-predictionpeak-detection

5.50 score 14 stars 15 scripts 174 downloads

ToxicoGx - Analysis of Large-Scale Toxico-Genomic Data

Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data.

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geneexpressionpharmacogeneticspharmacogenomicssoftware

4.46 score 29 scripts 366 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.

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geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction

4.31 score 1 stars 17 scripts 341 downloads

RadioGx - Analysis of Large-Scale Radio-Genomic Data

Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) <doi:10.1101/449793>.

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softwarepharmacogeneticsqualitycontrolsurvivalpharmacogenomicsclassification

4.18 score 15 scripts 450 downloads

SIGN - Similarity Identification in Gene Expression

Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.

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geneexpressionclassificationclusteringsurvival

2.70 score 6 scripts 188 downloads

MM2Sdata - Gene Expression Datasets for the 'MM2S' Package

Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

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mm2sexperimentdataexpressiondatamicroarraydata

2.11 score 13 scripts 163 downloads