Run Random Forest Classification on Cytokine Data,

This function trains and evaluates a Random Forest classification model on cytokine data. It includes feature importance visualization, cross- validation for feature selection, and performance metrics such as accuracy, sensitivity, and specificity. Optionally, for binary classification, the function also plots the ROC curve and computes the AUC.

Usage

cyt_rf(
  data,
  group_col,
  ntree = 500,
  mtry = 5,
  train_fraction = 0.7,
  plot_roc = FALSE,
  k_folds = 5,
  step = 0.5,
  run_rfcv = TRUE,
  verbose = FALSE,
  seed = 123
)

Arguments

data

A data frame containing the cytokine data, with one column as the grouping variable and the rest as numerical features.

group_col

A string representing the name of the column with the grouping variable (the target variable for classification).

ntree

An integer specifying the number of trees to grow in the forest (default is 500).

mtry

An integer specifying the number of variables randomly selected at each split (default is 5).

train_fraction

A numeric value between 0 and 1 representing the proportion of data to use for training (default is 0.7).

plot_roc

A logical value indicating whether to plot the ROC curve and compute the AUC for binary classification (default is FALSE).

k_folds

An integer specifying the number of folds for cross-validation (default is 5).

step

A numeric value specifying the fraction of variables to remove at each step during cross-validation for feature selection (default is 0.5).

run_rfcv

A logical value indicating whether to run Random Forest cross-validation for feature selection (default is TRUE).

verbose

A logical value indicating whether to print additional informational output to the console. When TRUE, the function will display progress messages, and intermediate results when FALSE (the default), it runs quietly.

seed

An integer specifying the seed for reproducibility (default is 123).

Value

A list containing:

model

The trained Random Forest model.

confusion_matrix

The confusion matrix of the test set predictions.

importance_plot

A ggplot object showing the variable importance plot based on Mean Decrease Gini.

rfcv_result

Results from Random Forest cross-validation for feature selection (if run_rfcv is TRUE).

importance_data

A data frame containing the variable importance based on the Gini index.

Details

The function fits a Random Forest model to the provided data by splitting it into training and test sets. It calculates performance metrics such as accuracy, sensitivity, and specificity for both sets. For binary classification, it can also plot the ROC curve and compute the AUC. If run_rfcv is TRUE, cross-validation is performed to select the optimal number of features. If verbose is TRUE, the function prints additional information to the console, including training results, test results, and plots.

Examples

data.df0 <- ExampleData1
data.df <- data.frame(data.df0[, 1:3], log2(data.df0[, -c(1:3)]))
data.df <- data.df[, -c(2:3)]
data.df <- dplyr::filter(data.df, Group != "ND")

cyt_rf(
  data = data.df, group_col = "Group", k_folds = 5, ntree = 1000,
  mtry = 4, run_rfcv = TRUE, plot_roc = TRUE, verbose = FALSE
)