cyt_xgb: Run XGBoost Classification on Cytokine Data.

Description

This function trains and evaluates an XGBoost classification model on cytokine data. It allows for hyperparameter tuning, cross-validation, and visualizes feature importance.

Usage

cyt_xgb(
  data,
  group_col,
  train_fraction = 0.7,
  nrounds = 500,
  max_depth = 6,
  eta = lifecycle::deprecated(),
  learning_rate = 0.1,
  nfold = 5,
  cv = FALSE,
  objective = "multi:softprob",
  early_stopping_rounds = NULL,
  eval_metric = "mlogloss",
  gamma = lifecycle::deprecated(),
  min_split_loss = 0,
  colsample_bytree = 1,
  subsample = 1,
  min_child_weight = 1,
  top_n_features = 10,
  verbose = 1,
  plot_roc = FALSE,
  print_results = FALSE,
  seed = 123
)

Value

A list containing:

model: The trained XGBoost model.
confusion_matrix: The confusion matrix of the test set predictions.
importance: The feature importance matrix for the top features.
class_mapping: A named vector showing the mapping from class labels to numeric values used for training.
cv_results: Cross-validation results, if cross-validation was performed (otherwise NULL).
plot: A ggplot object showing the feature importance plot.

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 (i.e., the target variable for classification).
train_fraction: A numeric value between 0 and 1 representing the proportion of data to use for training (default is 0.7).
nrounds: An integer specifying the number of boosting rounds (default is 500).
max_depth: An integer specifying the maximum depth of the trees (default is 6).
eta: Deprecated; use learning_rate instead.
learning_rate: A numeric value representing the learning rate (default is 0.1). This replaces the deprecated eta argument.
nfold: An integer specifying the number of folds for cross-validation (default is 5).
cv: A logical value indicating whether to perform cross-validation (default is FALSE).
objective: A string specifying the XGBoost objective function (default is "multi:softprob" for multi-class classification).
early_stopping_rounds: An integer specifying the number of rounds with no improvement to stop training early (default is NULL).
eval_metric: A string specifying the evaluation metric (default is "mlogloss").
gamma: Deprecated; use min_split_loss instead.
min_split_loss: A numeric value for the minimum loss reduction required to make a further partition (default is 0). This replaces the deprecated gamma argument.
colsample_bytree: A numeric value specifying the subsample ratio of columns when constructing each tree (default is 1).
subsample: A numeric value specifying the subsample ratio of the training instances (default is 1).
min_child_weight: A numeric value specifying the minimum sum of instance weight needed in a child (default is 1).
top_n_features: An integer specifying the number of top features to display in the importance plot (default is 10).
verbose: An integer specifying the verbosity of the training process (default is 1).
plot_roc: A logical value indicating whether to plot the ROC curve and calculate the AUC for binary classification (default is FALSE).
print_results: A logical value indicating whether to print the results of the model training and evaluation (default is FALSE). If set to TRUE, it will print the confusion matrix, and feature importance.
seed: An integer specifying the seed for reproducibility (default is 123).

Author

Shubh Saraswat

Details

The function allows for training an XGBoost model on cytokine data, splitting the data into training and test sets. If cross-validation is enabled (cv = TRUE), it performs k-fold cross-validation and reports the confusion matrix and accuracy. The function also visualizes the top N important features using xgb.ggplot.importance().

Examples

Run this code

# Example usage:
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_xgb(
 data = data_df,
 group_col = "Group",
 nrounds = 500,
 max_depth = 4,
 min_split_loss = 0,
 learning_rate = 0.05,
 nfold = 5,
 cv = FALSE,
 objective = "multi:softprob",
 eval_metric = "auc",
 early_stopping_rounds = NULL,
 top_n_features = 10,
 verbose = 0,
 plot_roc = TRUE,
 print_results = FALSE)

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