ROC Analysis for Glycomics and Glycoproteomics Data

Perform Receiver Operating Characteristic (ROC) analysis for binary classification of glycomics or glycoproteomics data. The function calculates ROC curves and Area Under the Curve (AUC) values for each variable to assess their discriminatory power between two groups.

Usage

gly_roc(exp, group_col = "group", pos_class = NULL, add_info = TRUE)

Arguments

exp: A glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object, or another SummarizedExperiment containing an expression matrix and sample information.
group_col: A character string specifying the column name of the grouping variable in the sample information. Default is "group". The grouping variable must have exactly 2 levels for binary classification.
pos_class: A character string specifying which group level should be treated as the positive class. If NULL (default), the second level (alphabetically) will be used as the positive class.
add_info: A logical value. If TRUE (default), variable information from the experiment will be added to the result tibbles. If FALSE, only the ROC analysis results are returned.

Value

A list with three elements:

tidy_result: A list containing two tibbles:
- auc: A tibble containing AUC values for each variable with the following columns:
  - variable: Variable name
  - auc: Area Under the Curve value
  - auc_ci_low: Lower bound of 95% confidence interval for AUC
  - auc_ci_high: Upper bound of 95% confidence interval for AUC
- coords: A tibble containing ROC curve coordinates with the following columns:
  - variable: Variable name
  - threshold: Threshold value for classification
  - specificity: Specificity (True Negative Rate)
  - sensitivity: Sensitivity (True Positive Rate)
raw_result: A list of pROC objects
meta_data: A list containing metadata from the input experiment The list has classes glystats_roc_res and glystats_res.

Details

For each variable, a ROC curve is computed using the expression values as predictor and the binary group labels as response.

The function requires exactly 2 groups in the specified grouping variable. If more than 2 groups are present, an error will be thrown.

Underlying Function:

ROC analysis is performed using pROC::roc()
Coordinates are extracted using pROC::coords()

Required packages

This function requires the pROC package to be installed for ROC curve computation.