CellResponseProcessor: Transform Drug Response Data

In JRC-COMBINE/FORESEE: A Tool for the Systematic Comparison of Translational Drug Response Modeling Pipelines

Description

The CellResponseProcessor transforms the response data of the TrainObject for prediction.

Usage

CellResponseProcessor(TrainObject, DrugName, CellResponseType,
  CellResponseTransformation)

Arguments

TrainObject	Object that contains all data needed to train a model, including molecular data (such as gene expression, mutation, copy number variation, methylation, cancer type, etc. ) and drug response data
DrugName	Name of the drug whose efficacy is supposed to be predicted with the model
CellResponseType	Format of the drug response data of the TrainObject, such as LN_IC50, AUC, GI50, etc., that is included in the TrainObject and to be used for prediction
CellResponseTransformation	Method that is to be used to transform the drug response data of the TrainObject: the function 'powertransform' power transforms the drug response data, the function 'logarithm' returns the natural logarithm of the drug response data, the function 'binarization_kmeans' returns a binarized drug response vector based on 2 kmeans clusters, the function 'binarization_cutoff' returns a binarized drug response vector based on a cutoff at the median, the function 'none' returns the unchanged drug response data. The function 'listInputOptions("CellResponseProcessor")' returns a list of the possible options. Instead of choosing one of the implemented options, a user-defined function can be used as an input.

Value

TrainObject

The TrainObject with preprocessed drug response data.

Examples

CellResponseProcessor(GDSC, "Docetaxel", "LN_IC50", "binarization_cutoff")

JRC-COMBINE/FORESEE documentation built on Jan. 24, 2020, 1:19 a.m.