cyt_xgb: Run XGBoost Classification on Cytokine Data.
In CytoProfile: Cytokine Profiling Analysis Tool

cyt_xgb

R Documentation

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,
  learning_rate = 0.1,
  nfold = 5,
  cv = FALSE,
  objective = "multi:softprob",
  early_stopping_rounds = NULL,
  eval_metric = "mlogloss",
  min_split_loss = 0,
  colsample_bytree = 1,
  subsample = 1,
  min_child_weight = 1,
  top_n_features = 10,
  verbose = 0,
  plot_roc = FALSE,
  print_results = FALSE,
  seed = 123,
  scale = c("none", "log2", "log10", "zscore", "custom"),
  custom_fn = NULL
)

Arguments

`data`	A data frame containing numeric predictor variables and one grouping column. Non-numeric predictor columns will be coerced to numeric if possible.
`group_col`	Character string naming the column that contains the class labels (target variable). Required.
`train_fraction`	Numeric between 0 and 1 specifying the proportion of samples used for model training. The remainder is reserved for testing. Default is 0.7.
`nrounds`	Integer specifying the number of boosting rounds. Default is 500.
`max_depth`	Integer specifying the maximum depth of each tree. Default is 6.
`learning_rate`	Numeric specifying the learning rate. Default is 0.1.
`nfold`	Integer specifying the number of folds for cross-validation when `cv = TRUE`. Default is 5.
`cv`	Logical indicating whether to perform cross-validation using `xgb.cv`. Default is `FALSE`.
`objective`	Character string specifying the objective function. For multi-class classification use "multi:softprob"; for binary classification use "binary:logistic". Default is "multi:softprob".
`early_stopping_rounds`	Integer specifying the number of rounds without improvement to trigger early stopping. Default is NULL (no early stopping).
`eval_metric`	Character specifying the evaluation metric used during training. Default is "mlogloss".
`min_split_loss`	Numeric specifying the minimum loss reduction required to make a further partition. Default is 0.
`colsample_bytree`	Numeric specifying the subsample ratio of columns when constructing each tree. Default is 1.
`subsample`	Numeric specifying the subsample ratio of the training instances. Default is 1.
`min_child_weight`	Numeric specifying the minimum sum of instance weight needed in a child. Default is 1.
`top_n_features`	Integer specifying the number of top features to display in the importance plot. Default is 10.
`verbose`	Integer (0, 1 or 2) controlling the verbosity of `xgb.train`. Default is 0. Larger values print more information.
`plot_roc`	Logical indicating whether to plot the ROC curve and compute the AUC for binary classification. Default is `FALSE`.
`print_results`	Logical. If `TRUE`, prints the confusion matrix, top features and cross-validation metrics to the console. Default is `FALSE`.
`seed`	Optional integer seed for reproducibility. Default is 123.
`scale`	Character string specifying a transformation to apply to numeric predictors prior to model fitting. Possible values are "none", "log2", "log10", "zscore" or "custom". When set to "custom" a user defined function must be supplied via `custom_fn`. Defaults to "none".
`custom_fn`	A custom transformation function used when `scale = "custom"`. Ignored otherwise. Should take a numeric vector and return a numeric vector of the same length.

Value

An invisible list with elements: model (the trained xgboost object), confusion_matrix (test set confusion matrix), importance (variable importance matrix), class_mapping (mapping from class names to numeric labels), cv_results (cross-validation results when cv=TRUE), importance_plot (a ggplot2 object of the top feature importance), and roc_plot (a ROC curve for binary classification when plot_roc=TRUE). All plots are printed automatically.

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_xgb(
 data = data_df,
 group_col = "Group",
 nrounds = 250,
 max_depth = 4,
 min_split_loss = 0,
 learning_rate = 0.05,
 nfold = 5,
 cv = FALSE,
 objective = "multi:softprob",
 eval_metric = "auc",
 plot_roc = TRUE,
 print_results = FALSE)

CytoProfile documentation built on Feb. 27, 2026, 5:06 p.m.