FeaturePreprocessor: Preprocess the Gene Expression Inputs of both TrainObject and...

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

Description

The FeaturePreprocessor converts the original gene expression features into predictive features with a function defined by FeaturePreprocessing.

Usage

FeaturePreprocessor(TrainObject, TestObject, FeaturePreprocessing)

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
TestObject	Object that contains all data that the model is to be tested on, including molecular data (such as gene expression, mutation, copy number variation, methylation, cancer type, etc. ) and drug response data
FeaturePreprocessing	Method for preprocessing the inputs of the model: The function 'zscore_genewise' calculates the zscore normalizing each gene over all samples, The function 'zscore_samplewise' calculates the zscore normalizing each sample over all genes, The function 'pca' does principal component analysis, The function 'physio' does physiospace analysis with the samples using cell line gene expression of the gdsc data base as physiological references, The function 'none' keeps the gene expression values unchanged, The function 'listInputOptions("FeaturePreprocessor")' returns a list of the possible options. Instead of chosing one of the implemented options, a user-defined function can be used as an input.

Value

TrainObject	The TrainObject with preprocessed features.
TestObject	The TestObject with preprocessed features.

Examples

FeaturePreprocessor(GDSC,GSE6434,"zscore_genewise")

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