Package: oncoPredict
Type: Package
Title: Drug Response Modeling and Biomarker Discovery
Version: 1.3.1
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 <rgruener@umn.edu>
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.
    The package is described in Maeser et al. (2021) "oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data" <doi:10.1093/bib/bbab260>.
    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
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
NeedsCompilation: no
Packaged: 2026-06-23 15:17:48 UTC; rfgruener
Author: Robert Gruener [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8166-2772>),
  Danielle Maeser [aut] (ORCID: <https://orcid.org/0000-0002-3890-887X>)
Repository: CRAN
Date/Publication: 2026-06-29 14:50:02 UTC
Built: R 4.5.3; ; 2026-07-17 13:41:35 UTC; windows
