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.

Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data.

Construct and evaluate directed tree structures that
   model the process of occurrence of genetic alterations during carcinogenesis
   as described in Szabo, A. and Boucher, K (2002)  <doi:10.1016/S0025-5564(02)00086-X>.

Single-cell datasets are growing in size, posing challenges
    as well as opportunities for genomics researchers. 'ondisc' is an R
    package that facilitates analysis of large-scale single-cell data
    out-of-core on a laptop or distributed across tens to hundreds of
    processors on a cluster or cloud. In both of these settings, 'ondisc'
    requires only a few gigabytes of memory, even if the input data are tens
    of gigabytes in size. 'ondisc' mainly is oriented toward single-cell
    CRISPR screen analysis, but also can be used for single-cell differential
    expression and single-cell co-expression analyses. 'ondisc' is powered by
    several new, efficient algorithms for manipulating and querying large,
    sparse expression matrices.