calcPhenotype           This function predicts a phenotype (drug
                        sensitivity score) when provided with
                        microarray or bulk RNAseq gene expression data
                        of different platforms. The imputations are
                        performed using ridge regression, training on a
                        gene expression matrix where phenotype is
                        already known. This function integrates
                        training and testing datasets via a
                        user-defined procedure, and power transforming
                        the known phenotype.
completeMatrix          This function performs an iterative matrix
                        completion algorithm to predict drug response
                        for pre-clinical data when there are missing
                        ('NA') values.
doVariableSelection     This function performs variable selection on
                        gene expression matrices. It can, for instance,
                        remove genes with low variation.
glds                    This function determines drug-gene associations
                        for pre-clinical data.
homogenizeData          This function takes two gene expression
                        matrices (like trainExprMat and testExprMat)
                        and returns homogenized versions of the
                        matrices by employing the homogenization method
                        specified. By default, the Combat method from
                        the sva library is used. In both matrices,
                        genes are row names and samples are column
                        names. It will deal with duplicated gene names,
                        as it subsets and orders the matrices
                        correctly.
idwas                   This function will test every drug against
                        every CNV or somatic mutation for your cancer
                        type.
map_cnv                 This function maps cnv data to genes. The
                        output of this function is a .RData file called
                        map.RData; this file contains
                        theCnvQuantVecList_mat (rows are genes, and
                        columns are samples) and tumorSamps (indicates
                        which samples are primary tumor samples, 01A).
summarizeGenesByMean    This function takes a gene expression matrix
                        and if duplicate genes are measured, summarizes
                        them by their means.
