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Run Random Forest Classification on Cytokine Data

Source: R/cyt_rf.R
cyt_rf.Rd

This function trains and evaluates a Random Forest classification model on cytokine data. It includes variable importance visualization, cross-validation for feature selection, and performance metrics such as accuracy, sensitivity, and specificity. For binary classification the function can also plot the ROC curve and compute 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,
  cv = FALSE,
  cv_folds = 5,
  scale = c("none", "log2", "log10", "zscore", "custom"),
  custom_fn = NULL,
  output_file = NULL,
  font_settings = NULL,
  progress = NULL
)

Arguments

data

A data frame containing the cytokine measurements. One column should correspond to the grouping variable (the outcome) and the remaining columns should be numeric predictors.

group_col

A string naming the column in data that contains the grouping variable.

ntree

Integer specifying the number of trees to grow. Default is 500.

mtry

Integer specifying the number of variables randomly sampled at each split. Default is 5.

train_fraction

Numeric between 0 and 1 giving the proportion of data used for training. Default is 0.7.

plot_roc

Logical. If TRUE and the problem is binary, an ROC curve and AUC are computed and returned. Default is FALSE.

k_folds

Integer specifying the number of folds for rfcv when run_rfcv = TRUE. Default is 5.

step

Numeric specifying the fraction of variables removed at each step during rfcv. Default is 0.5.

run_rfcv

Logical indicating whether to run Random Forest cross-validation for feature selection. Default is TRUE.

verbose

Logical. When TRUE, training and test performance metrics, confusion matrices, and cross-validation details are printed. Default is FALSE.

seed

Optional integer seed for reproducibility. Default is 123.

cv

Logical indicating whether to perform a separate k-fold classification cross-validation using caret. Default is FALSE.

cv_folds

Integer specifying the number of folds for classification cross-validation when cv = TRUE. Default is 5.

scale

Character string specifying a transformation to apply to numeric predictor columns prior to model fitting. One of "none" (default), "log2", "log10", "zscore", or "custom".

custom_fn

A custom transformation function used when scale = "custom".

output_file

Optional. A file path to save outputs as a PDF. If NULL (default), a named list is returned for interactive display.

font_settings

Optional named list of font sizes for supported plot text elements.

progress

Optional. A Shiny Progress object for reporting progress updates.

Value

When output_file is NULL, an invisible named list with elements summary_text, vip_plot, roc_plot, rfcv_plot, rfcv_data, importance_data, and cv_results. When output_file is provided, a PDF is written and the function returns NULL invisibly.

Author

Shubh Saraswat and Xiaohua Douglas Zhang

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)