Package: oncoPredict Type: Package Title: Drug Response Modeling and Biomarker Discovery Version: 1.3 URL: https://github.com/HuangLabUMN/oncoPredict BugReports: https://github.com/HuangLabUMN/oncoPredict/issues Authors@R: c( person(given = 'Robert', family = 'Gruener', role = c("aut", "cre"), email = 'rgruener@umn.edu', comment = c(ORCID = "0000-0002-8166-2772")), person(given = "Danielle", family = "Maeser", role = "aut", email = "maese005@umn.edu", comment = c(ORCID = "0000-0002-3890-887X"))) Maintainer: Robert Gruener Description: 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. License: GPL-2 Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.2 Depends: R (>= 4.1.0) biocViews: sva, limma, stringr, biomaRt, genefilter, GenomicFeatures, genefilter, TCGAbiolinks, BiocGenerics, GenomicRanges, IRanges, S4Vectors Imports: parallel, ridge, car, glmnet, pls, sva, limma, GenomicFeatures, BiocGenerics, GenomicRanges, IRanges, S4Vectors Suggests: knitr, rmarkdown, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TCGAbiolinks, testthat (>= 3.0.0) Additional_repositories: https://bioconductor.org/packages/release/data/annotation VignetteBuilder: knitr Config/testthat/edition: 3 Config/pak/sysreqs: cmake make libgsl0-dev libbz2-dev libicu-dev liblzma-dev libpng-dev libxml2-dev libssl-dev xz-utils zlib1g-dev Repository: https://huanglabumn.r-universe.dev Date/Publication: 2026-06-13 15:58:48 UTC RemoteUrl: https://github.com/huanglabumn/oncopredict RemoteRef: HEAD RemoteSha: c16c1ffaffdd855f4ada4c83a3948e5c16b618c5 NeedsCompilation: no Packaged: 2026-06-30 09:27:40 UTC; root Author: Robert Gruener [aut, cre] (ORCID: ), Danielle Maeser [aut] (ORCID: )