ODT (version 1.0.0)

predictTree: Predict Treatment Outcomes with a Trained Decision Tree

Description

This function utilizes a trained decision tree model (ODT) to predict treatment outcomes for test data based on patient sensitivity data and features, such as mutations or gene expression profiles.

Usage

predictTree(tree, PatientData, PatientSensitivityTrain)

Value

A factor representing the assigned treatment for each node in the decision tree based on the provided patient data and sensitivity.

Arguments

tree

A trained decision tree object created by the `trainTree` function.

PatientData

A matrix representing patient features, where rows correspond to patients/samples and columns correspond to genes/features. This matrix can contain:

• Binary mutation data (e.g., presence/absence of mutations).

• Continuous data from gene expression profiles (e.g., expression levels).

PatientSensitivityTrain

A matrix containing the drug response values of the **training dataset**. In this matrix, rows correspond to patients, and columns correspond to drugs. This matrix is used solely for extracting treatment names and is not used in the prediction process itself.

Examples

# \donttest{
  # Example 1: Prediction using mutation data
  data("mutations_w12")
  data("mutations_w34")
  data("drug_response_w12")
  ODTmut <- trainTree(PatientData = mutations_w12, 
                      PatientSensitivity = drug_response_w12, 
                      minbucket = 10)
  ODTmut
  ODT_mutpred <- predictTree(tree = ODTmut, 
                              PatientSensitivityTrain = drug_response_w12, 
                              PatientData = mutations_w34)

  # Example 2: Prediction using gene expression data
  data("expression_w34")
  data("expression_w12")
  data("drug_response_w34")
  ODTExp <- trainTree(PatientData = expression_w34, 
                       PatientSensitivity = drug_response_w34, 
                       minbucket = 20)
  ODTExp
  ODT_EXPpred <- predictTree(tree = ODTExp, 
                              PatientSensitivityTrain = drug_response_w34, 
                              PatientData = expression_w12)
# }