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

Description Usage Arguments Value Examples

View source: R/FeaturePreprocessor.R

Description

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

Usage

1
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

1
FeaturePreprocessor(GDSC,GSE6434,"zscore_genewise")

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