Showing 23 of total 23 results (show query)
bioc
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.
Maintained by Benjamin Haibe-Kains. Last updated 2 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationdatasetspharmacogenomicpharmacogxcpp
32.2 match 68 stars 11.39 score 442 scripts 3 dependentsbioc
ChemmineR:Cheminformatics Toolkit for R
ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures.
Maintained by Thomas Girke. Last updated 5 months ago.
cheminformaticsbiomedicalinformaticspharmacogeneticspharmacogenomicsmicrotitreplateassaycellbasedassaysvisualizationinfrastructuredataimportclusteringproteomicsmetabolomicscpp
10.0 match 14 stars 9.42 score 253 scripts 12 dependentsbioc
pogos:PharmacOGenomics Ontology Support
Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies.
Maintained by VJ Carey. Last updated 2 months ago.
pharmacogenomicspooledscreensimmunooncology
20.6 match 4.30 score 10 scriptsbioc
ChemmineOB:R interface to a subset of OpenBabel functionalities
ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly.
Maintained by Thomas Girke. Last updated 5 months ago.
cheminformaticsbiomedicalinformaticspharmacogeneticspharmacogenomicsmicrotitreplateassaycellbasedassaysvisualizationinfrastructuredataimportclusteringproteomicsmetabolomicsopenbabelcpp
10.0 match 10 stars 7.87 score 77 scripts 1 dependentsbioc
fmcsR:Mismatch Tolerant Maximum Common Substructure Searching
The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering.
Maintained by Thomas Girke. Last updated 5 months ago.
cheminformaticsbiomedicalinformaticspharmacogeneticspharmacogenomicsmicrotitreplateassaycellbasedassaysvisualizationinfrastructuredataimportclusteringproteomicsmetabolomicscpp
10.0 match 5 stars 7.03 score 60 scripts 1 dependentsbioc
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.
Maintained by Benjamin Haibe-Kains. Last updated 4 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassification
10.5 match 11 stars 6.35 score 17 scriptsbioc
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>.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
softwarepharmacogenomicsclassificationsurvival
10.0 match 6.53 score 63 scripts 6 dependentsbioc
gwasurvivr:gwasurvivr: an R package for genome wide survival analysis
gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data.
Maintained by Abbas Rizvi. Last updated 5 months ago.
genomewideassociationsurvivalregressiongeneticssnpgeneticvariabilitypharmacogenomicsbiomedicalinformatics
10.0 match 12 stars 6.43 score 75 scriptsbioc
faers:R interface for FDA Adverse Event Reporting System
The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis.
Maintained by Yun Peng. Last updated 5 months ago.
softwaredataimportbiomedicalinformaticspharmacogenomicsadverse-eventsdrug-safetyfaersfaers-procedurepharmacovigilancesignal-detection
10.0 match 20 stars 5.95 score 5 scripts
bhklab
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>.
Maintained by Benjamin Haibe-Kains. Last updated 4 years ago.
6.5 match 19 stars 8.95 score 105 scripts 2 dependentsbioc
rcellminer:rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines
The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data.
Maintained by Augustin Luna. Last updated 5 months ago.
acghcellbasedassayscopynumbervariationgeneexpressionpharmacogenomicspharmacogeneticsmirnacheminformaticsvisualizationsoftwaresystemsbiology
10.0 match 5.71 score 113 scriptsbioc
eiR:Accelerated similarity searching of small molecules
The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach.
Maintained by Thomas Girke. Last updated 1 months ago.
cheminformaticsbiomedicalinformaticspharmacogeneticspharmacogenomicsmicrotitreplateassaycellbasedassaysvisualizationinfrastructuredataimportclusteringproteomicsmetabolomics
10.0 match 3 stars 5.51 score 12 scriptsbioc
gCrisprTools:Suite of Functions for Pooled Crispr Screen QC and Analysis
Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity.
Maintained by Russell Bainer. Last updated 5 months ago.
immunooncologycrisprpooledscreensexperimentaldesignbiomedicalinformaticscellbiologyfunctionalgenomicspharmacogenomicspharmacogeneticssystemsbiologydifferentialexpressiongenesetenrichmentgeneticsmultiplecomparisonnormalizationpreprocessingqualitycontrolrnaseqregressionsoftwarevisualization
10.0 match 4.78 score 8 scriptsbioc
octad:Open Cancer TherApeutic Discovery (OCTAD)
OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds.
Maintained by E. Chekalin. Last updated 5 months ago.
classificationgeneexpressionpharmacogeneticspharmacogenomicssoftwaregenesetenrichment
11.2 match 4.00 score 4 scriptsbioc
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.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftware
10.0 match 4.36 score 23 scriptsbioc
DepInfeR:Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling
DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864.
Maintained by Junyan Lu. Last updated 5 months ago.
softwareregressionpharmacogeneticspharmacogenomicsfunctionalgenomics
10.0 match 1 stars 4.36 score 23 scriptsbioc
drugTargetInteractions:Drug-Target Interactions
Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.
Maintained by Thomas Girke. Last updated 5 months ago.
cheminformaticsbiomedicalinformaticspharmacogeneticspharmacogenomicsproteomicsmetabolomics
10.0 match 1 stars 4.34 score 11 scriptsbioc
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.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction
10.0 match 1 stars 4.31 score 17 scriptsbioc
mslp:Predict synthetic lethal partners of tumour mutations
An integrated pipeline to predict the potential synthetic lethality partners (SLPs) of tumour mutations, based on gene expression, mutation profiling and cell line genetic screens data. It has builtd-in support for data from cBioPortal. The primary SLPs correlating with muations in WT and compensating for the loss of function of mutations are predicted by random forest based methods (GENIE3) and Rank Products, respectively. Genetic screens are employed to identfy consensus SLPs leads to reduced cell viability when perturbed.
Maintained by Chunxuan Shao. Last updated 5 months ago.
pharmacogeneticspharmacogenomics
10.0 match 3.30 score 1 scripts
mbant
BayesSUR:Bayesian Seemingly Unrelated Regression Models in High-Dimensional Settings
Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.
Maintained by Zhi Zhao. Last updated 7 months ago.
3.4 match 8 stars 6.05 score 3 scripts
zsviolet
PRSPGx:Construct PGx PRS
Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj (unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL (penalized regression), PRS-PGx-Bayes (Bayesian regression). Package is based on ''Pharmacogenomics Polyenic Risk Score for Drug Response Prediction Using PRS-PGx Methods'' by Zhai, S., Zhang, H., Mehrotra, D.V., and Shen, J., 2021 (submitted).
Maintained by Song Zhai. Last updated 3 years ago.
3.2 match 2.00 score 3 scripts
huanglabumn
oncoPredict:Drug Response Modeling and Biomarker Discovery
Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.
Maintained by Robert Gruener. Last updated 12 months ago.
svapreprocesscorestringrbiomartgenefilterorg.hs.eg.dbgenomicfeaturestxdb.hsapiens.ucsc.hg19.knowngenetcgabiolinksbiocgenericsgenomicrangesirangess4vectors
0.5 match 18 stars 6.47 score 41 scripts
calvintchi
hierBipartite:Bipartite Graph-Based Hierarchical Clustering
Bipartite graph-based hierarchical clustering performs hierarchical clustering of groups of samples based on association patterns between two sets of variables. It is developed for pharmacogenomic datasets and datasets sharing the same data structure. In the context of pharmacogenomic datasets, the samples are cell lines, and the two sets of variables are typically expression levels and drug sensitivity values. For this method, sparse canonical correlation analysis from Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> is first applied to extract association patterns for each group of samples. Then, a nuclear norm-based dissimilarity measure is used to construct a dissimilarity matrix between groups based on the extracted associations. Finally, hierarchical clustering is applied.
Maintained by Calvin Chi. Last updated 4 years ago.
0.8 match 1 stars 3.70 score 4 scripts