Help for 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

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:

NeedsCompilation:

Packaged:

2026-06-23 15:17:48 UTC; rfgruener

Author:

Robert Gruener

[aut, cre], Danielle Maeser

[aut]

Repository:

CRAN

Date/Publication:

2026-06-29 14:50:02 UTC

Generate predicted drug sensitivity scores

Description

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.

Usage

calcPhenotype(
  trainingExprData,
  trainingPtype,
  testExprData,
  batchCorrect,
  powerTransformPhenotype = TRUE,
  removeLowVaryingGenes = 0.2,
  minNumSamples,
  selection = 1,
  printOutput,
  pcr = FALSE,
  removeLowVaringGenesFrom,
  report_pc = FALSE,
  cc = FALSE,
  percent = 80,
  rsq = FALSE,
  folder = FALSE,
  parallel = FALSE,
  cores = 1
)

Arguments

trainingExprData

The training data. A matrix of expression levels. rownames() are genes, colnames() are samples (cell line names or cosmic ides, etc.). rownames() must be specified and must contain the same type of gene ids as "testExprData"

trainingPtype

The known phenotype for "trainingExprData". This data must be a matrix with training samples as rows and drugs or phenotypes as columns. This matrix can contain NA values, that is ok (they are removed in the calcPhenotype() function).

testExprData

The test data where the phenotype will be estimated. It is a matrix of expression levels, rows contain genes and columns contain samples, "rownames()" must be specified and must contain the same type of gene ids as "trainingExprData".

batchCorrect

How should training and test data matrices be homogenized. Choices are "eb" (default) for ComBat, "qn" for quantile normalization, "standardize" for within-dataset z-score standardization, "rank", "rank_then_eb", or "none" for no homogenization.

powerTransformPhenotype

Should the phenotype be power transformed before we fit the regression model? Default to TRUE, set to FALSE if the phenotype is already known to be highly normal.

removeLowVaryingGenes

What proportion of low varying genes should be removed? 20 percent be default

minNumSamples

How many training and test samples are required. Print an error if below this threshold

selection

How should duplicate gene ids be handled. Default is -1 which asks the user. 1 to summarize by their or 2 to disguard all duplicates.

printOutput

Set to FALSE to supress output.

pcr

Indicates whether or not you'd like to use pcr for feature (gene) reduction. Options are 'TRUE' and 'FALSE'. If you indicate 'report_pc=TRUE' you need to also indicate 'pcr=TRUE'

removeLowVaringGenesFrom

Determine method to remove low varying genes. Options are 'homogenizeData' and 'rawData'.

report_pc

Indicates whether you want to output the training principal components. Options are 'TRUE' and 'FALSE'.

cc

Indicate if you want correlation coefficients for biomarker discovery.

percent

Indicate percent variability (of the training data) you'd like principal components to reflect if pcr=TRUE. Default is 80 for 80% These are the correlations between a given gene of interest across all samples vs. a given drug response across samples. These correlations can be ranked to obtain a ranked correlation to determine highly correlated drug-gene associations.

rsq

Indicate whether or not you want to output the R^2 values for the data you train on from true and predicted values. These values represent the percentage in which the optimal model accounts for the variance in the training data. Options are 'TRUE' and 'FALSE'.

folder

If TRUE, write calcPhenotype outputs to calcPhenotype_Output in the current working directory. The default is FALSE.

parallel

If TRUE, fit drug models in parallel after the shared homogenization and gene-filtering steps are complete. The default is FALSE.

cores

The number of cores to use when parallel is TRUE. Parallel execution uses forked processes via parallel::mclapply, which is not available for multicore execution on Windows PCs; on Windows, calcPhenotype will warn and run serially.

Value

A matrix of predicted drug response values. If rsq, cc, or report_pc is TRUE, returns a list containing the predictions and requested optional outputs. If folder is TRUE, the same object is returned invisibly after files are written.

Examples

set.seed(1)
genes <- paste0("gene", 1:30)
trainingExprData <- matrix(rnorm(30 * 8), nrow=30,
                           dimnames=list(genes, paste0("train", 1:8)))
testExprData <- matrix(rnorm(30 * 3), nrow=30,
                       dimnames=list(genes, paste0("test", 1:3)))
trainingPtype <- matrix(rnorm(8), ncol=1,
                        dimnames=list(colnames(trainingExprData), "drug1"))
predictions <- calcPhenotype(trainingExprData, trainingPtype, testExprData,
                             batchCorrect="none",
                             powerTransformPhenotype=FALSE,
                             removeLowVaryingGenes=0,
                             minNumSamples=0,
                             selection=1,
                             printOutput=FALSE,
                             pcr=FALSE,
                             removeLowVaringGenesFrom="rawData")
head(predictions)

This function performs an iterative matrix completion algorithm to predict drug response for pre-clinical data when there are missing ('NA') values.

Description

This function performs an iterative matrix completion algorithm to predict drug response for pre-clinical data when there are missing ('NA') values.

Usage

completeMatrix(senMat, nPerms = 50, folder = FALSE)

Arguments

senMat

A matrix of drug sensitivity data with missing ('NA') values. rownames() are samples (e.g. cell lines), and colnames() are drugs.

nPerms

The number of iterations that the EM-algorithm (expectation maximization approach) run. The default is 50, as previous findings recommend 50 iterations (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1050-9)

folder

If TRUE, write the completed matrix to complete_matrix_output.txt in the current working directory. The default is FALSE.

Value

A matrix of drug sensitivity scores without missing values. rownames() are samples, and colnames are drugs.

Examples

set.seed(1)
senMat <- matrix(rnorm(80 * 4), nrow=80,
                 dimnames=list(paste0("sample", 1:80), paste0("drug", 1:4)))
senMat[1, 1] <- NA
completed <- completeMatrix(senMat, nPerms=1)
dim(completed)

Remove genes with low variation.

Description

This function performs variable selection by removing genes with the lowest variance in the datasets.

Usage

doVariableSelection(exprMat, removeLowVaryingGenes = 0.2)

Arguments

exprMat

A matrix of gene expression levels. rownames() are genes, and colnames() are samples.

removeLowVaryingGenes

The proportion of low varying genes to be removed.The default is .2

Value

A vector of row/genes to keep.

Examples

exprMat <- matrix(seq_len(20), nrow=5,
                  dimnames=list(paste0("gene", 1:5), paste0("sample", 1:4)))
doVariableSelection(exprMat, removeLowVaryingGenes=0.2)

This function determines drug-gene associations for pre-clinical data.

Description

This function determines drug-gene associations for pre-clinical data.

Usage

glds(
  drugMat,
  drugRelatedness,
  markerMat,
  minMuts = 5,
  additionalCovariateMatrix = NULL,
  expression = NULL,
  threshold = 0.7,
  folder = FALSE
)

Arguments

drugMat

A matrix of drug sensitivity data. rownames() are pre-clinical samples, and colnames() are drug names.

drugRelatedness

A matrix in which column 1 contains a list of compounds, and column 2 contains a list of their corresponding target pathways. Given the subjective nature of drug classification, please ensure these pathways are as specific as possible for accurate results.

markerMat

A matrix containing the data for which you are looking for an association with drug sensitivity (e.g. a matrix of somatic mutation data). rownames() are marker names (e.g. gene names), and colnames() are samples.

minMuts

The minimum number of non-zero entries required so that a p-value can be calculated (e.g. how many somatic mutations must be present). The default is 5.

additionalCovariateMatrix

A matrix containing covariates to be fit in the drug biomarker association models. This could be, for example, tissue of origin or cancer type. Rows are sample names. The default is NULL.

expression

A matrix of expression data. rownames() are genes, and colnames() are the same pre-clinical samples as those in the drugMat (also in the same order). The default is NULL. If expression data is provided, a gene signature will be obtained.

threshold

Determine the correlation coefficient. Drugs with a correlation coefficient greater than or equal to this number with the drug under scrutiny will be removed from the negative control group. The default is 0.7

folder

If TRUE, write GLDS p-value and beta outputs to CSV files in the current working directory. The default is FALSE.

Value

A list containing GLDS-adjusted p-values and beta values, plus naive p-values and beta values.

Examples

set.seed(1)
sampleNames <- paste0("cell", 1:14)
drugNames <- paste0("drug", 1:6)
drugMat <- matrix(rnorm(14 * 6), nrow=14,
                  dimnames=list(sampleNames, drugNames))
drugRelatedness <- cbind(drug=drugNames,
                         pathway=paste0("pathway", 1:6))
markerMat <- matrix(c(rep(c(0, 1), 7), rep(c(1, 0), 7)),
                    nrow=2, byrow=TRUE,
                    dimnames=list(c("marker1", "marker2"), sampleNames))
gldsResult <- glds(drugMat, drugRelatedness, markerMat,
                   minMuts=1, threshold=1)
names(gldsResult)

Homogenizes two expression matrices

Description

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.

Usage

homogenizeData(
  testExprMat,
  trainExprMat,
  batchCorrect = "eb",
  selection = -1,
  printOutput = TRUE
)

Arguments

testExprMat

A gene expression matrix for samples on which we wish to predict a phenotype.Genes are rows, samples are columns.

trainExprMat

A gene expression matrix for samples for which the phenotype is already known.Genes are rows, samples are columns.

batchCorrect

The type of batch correction to be used. Options are 'eb' for ComBat, 'qn' for quantile normalization, 'standardize' for within-dataset z-score standardization, 'rank', 'rank_then_eb', or 'none'. The 'standardize' option can be useful when using microarray training data to build models on RNA-seq testing data. #The default is 'eb'.

selection

This parameter can be used to specify how duplicates are handled. The default value of -1 means to ask the user. #Other options include '1' to summarize duplicates by their mean, and '2'to discard all duplicated genes.

printOutput

To suppress output, set to false. Default is TRUE.

Value

A list containing two entries $train and $test, which are the homogenized input matrices.

Examples

trainExpr <- matrix(rnorm(20), nrow=5,
                    dimnames=list(paste0("gene", 1:5), paste0("train", 1:4)))
testExpr <- matrix(rnorm(10), nrow=5,
                   dimnames=list(paste0("gene", 1:5), paste0("test", 1:2)))
homData <- homogenizeData(testExpr, trainExpr, batchCorrect="none",
                          selection=1, printOutput=FALSE)
names(homData)

This function will test every drug against CNA amplifications or somatic mutations for your cancer type.

Description

This function will test every drug against CNA amplifications or somatic mutations for your cancer type.

Usage

idwas(drug_prediction, data, n = 10, cnv, folder = FALSE)

Arguments

drug_prediction

The drug prediction data. Must be a data frame. rownames are samples, colnames are drugs. Make sure sample names are of the same form as the sample names in your cnv or mutation data. e.g. if the rownames() are TCGA barcodes of the form TCGA-##-####-###, make sure your cnv/mutation data also uses samples in the form TCGA-##-####-###

data

The CNA amplification or mutation data. Must be a data frame. If you wish to use CNA data, use the output from map_cnv(), where rows are genes and columns are samples, or use data of similar form. If you wish to use mutation data, use the method for downloading mutation data outlined in the vignette, and make sure the TCGA barcodes use '-' instead of '.'; if you use another dataset (and don't download data from TCGA), make sure your data file includes the following columns: 'Variant_Classification', 'Hugo_Symbol', 'Tumor_Sample_Barcode'.

n

The minimum number of samples you want CNA amplifications or mutations to occur in. The default is 10 (arbitrarily chosen).

cnv

TRUE or FALSE. Indicate whether or not you would like to test CNA amplification data. If TRUE, you will test CNA amplifications. If FALSE, you will test mutation data.

folder

If TRUE, write IDWAS results to CSV files in the current working directory. The default is FALSE.

Value

Raw p-value and beta-values for CNA amplifications or somatic mutations.

Examples

drugPrediction <- data.frame(drug1=c(1, 2, 3),
                             drug2=c(2, 1, 3),
                             row.names=c("S1", "S2", "S3"))
mutationData <- data.frame(Variant_Classification=c("Missense_Mutation",
                                                    "Missense_Mutation",
                                                    "Missense_Mutation"),
                           Tumor_Sample_Barcode=c("S1", "S2", "S3"),
                           Hugo_Symbol=c("GENE1", "GENE1", "GENE2"))
idwas(drugPrediction, mutationData, n=1, cnv=FALSE)

This function maps cnv data to genes.

Description

This function maps cnv data to genes.

Usage

map_cnv(Cnvs, folder = FALSE)

Arguments

Cnvs

The cnv data. A table with the following colnames: Sample (named using the TCGA patient barcode), Chromosome, Start, End, Num_Probes, and Segment_Mean.

folder

If TRUE, write a map.RData file containing the mapped CNV matrix and tumor sample indices. The default is FALSE.

Value

A list containing theCnvQuantVecList_mat (rows are genes, columns are samples) and tumorSamps (primary tumor/01A sample column indices).

Examples

if (requireNamespace("org.Hs.eg.db", quietly=TRUE) &&
    requireNamespace("TxDb.Hsapiens.UCSC.hg19.knownGene", quietly=TRUE)) {
  cnvData <- data.frame(Sample="TCGA-01-0001-01A-01D-0000-01",
                        Chromosome=7,
                        Start=55000000,
                        End=55200000,
                        Num_Probes=10,
                        Segment_Mean=1.5)
  mapped <- map_cnv(cnvData)
  names(mapped)
}

Calculate Cross-Validation Scores using OncoPredict

Description

Usage

predictionAccuracybyCV(
  trainingExprData,
  trainingPtype,
  testExprData,
  batchCorrect,
  powerTransformPhenotype = TRUE,
  removeLowVaryingGenes = 0.2,
  minNumSamples,
  selection = 1,
  printOutput,
  pcr = FALSE,
  removeLowVaringGenesFrom,
  report_pc = FALSE,
  cc = FALSE,
  percent = 80,
  rsq = FALSE,
  folder = FALSE,
  cvFold = -1,
  parallel = FALSE,
  cores = 1
)

Arguments

trainingExprData

trainingPtype

The known phenotype for "trainingExprData". This can be a one-drug vector with one value per training sample or a matrix with training samples as rows and drugs or phenotypes as columns. This matrix can contain NA values, that is ok (they are removed in the calcPhenotype() function).

testExprData

batchCorrect

powerTransformPhenotype

Should the phenotype be power transformed before we fit the regression model? Default to TRUE, set to FALSE if the phenotype is already known to be highly normal.

removeLowVaryingGenes

What proportion of low varying genes should be removed? 20 percent be default

minNumSamples

How many training and test samples are required. Print an error if below this threshold

selection

How should duplicate gene ids be handled. Default is -1 which asks the user. 1 to summarize by their or 2 to disguard all duplicates.

printOutput

Set to FALSE to supress output.

pcr

Indicates whether or not you'd like to use pcr for feature (gene) reduction. Options are 'TRUE' and 'FALSE'. If you indicate 'report_pc=TRUE' you need to also indicate 'pcr=TRUE'

removeLowVaringGenesFrom

Determine method to remove low varying genes. Options are 'homogenizeData' and 'rawData'.

report_pc

Indicates whether you want to output the training principal components. Options are 'TRUE' and 'FALSE'.

cc

Indicate if you want correlation coefficients for biomarker discovery.

percent

rsq

folder

Retained for compatibility with calcPhenotype arguments. Cross-validation results are returned as an object.

cvFold

Indicate the number of k-folds wanted in the CV calculation. -1 indicates a leave-one-out cross validation

parallel

If TRUE, run cross-validation folds in parallel. The default is FALSE.

cores

Value

A list containing cross-validated predicted phenotype values and real phenotype values.

Examples

set.seed(1)
genes <- paste0("gene", 1:30)
trainingExprData <- matrix(rnorm(30 * 8), nrow=30,
                           dimnames=list(genes, paste0("train", 1:8)))
trainingPtype <- matrix(rnorm(8), ncol=1,
                        dimnames=list(colnames(trainingExprData), "drug1"))
cvResult <- predictionAccuracybyCV(trainingExprData, trainingPtype,
                                   testExprData=NULL,
                                   batchCorrect="none",
                                   powerTransformPhenotype=FALSE,
                                   removeLowVaryingGenes=0,
                                   minNumSamples=0,
                                   selection=1,
                                   printOutput=FALSE,
                                   pcr=FALSE,
                                   removeLowVaringGenesFrom="rawData",
                                   cvFold=2)
names(cvResult)

Average over duplicate gene values

Description

This function takes a gene expression matrix and if duplicate genes are measured, summarizes them by their means.

Usage

summarizeGenesByMean(exprMat)

Arguments

exprMat

A gene expression matrix with genes as rownames() and samples as colnames().

Value

A gene expression matrix that does not contain duplicate genes.

Examples

exprMat <- matrix(seq_len(12), nrow=3,
                  dimnames=list(paste0("gene", 1:3), paste0("sample", 1:4)))
summarizeGenesByMean(exprMat)

Package {oncoPredict}

Generate predicted drug sensitivity scores

Description

Usage

Arguments

Value

Examples

This function performs an iterative matrix completion algorithm to predict drug response for pre-clinical data when there are missing ('NA') values.

Description

Usage

Arguments

Value

Examples

Remove genes with low variation.

Description

Usage

Arguments

Value

Examples

This function determines drug-gene associations for pre-clinical data.

Description

Usage

Arguments

Value

Examples

Homogenizes two expression matrices

Description

Usage

Arguments

Value

Examples

This function will test every drug against CNA amplifications or somatic mutations for your cancer type.

Description

Usage

Arguments

Value

Examples

This function maps cnv data to genes.

Description

Usage

Arguments

Value

Examples

Calculate Cross-Validation Scores using OncoPredict

Description

Usage

Arguments

Value

Examples

Average over duplicate gene values

Description

Usage

Arguments

Value

Examples