cGVHD diagnostic classifier. (A) Samples were first divided into training and test sets. Bootstrap univariate marker selection was then performed by applying t test to the training samples of each marker and estimating the percentage of bootstraps over which a given marker has P < .05, referred to as selection frequency (f). A set of markers (S) with selection frequency >0.99 were used for classifier training. Labels of test samples (I_p) were then predicted using the trained classifier weights (w) and compared against the ground truth labels (l_g) to evaluate the classifier’s performance. This procedure was repeated 1000 times with random sample splits to assess variability in performance. (B) Selection frequency of cellular and plasma markers based on all samples plotted. Markers with selection frequency >0.99 indicated in yellow. (C) The classifier achieved an average ROC AUC of 0.89 over the 1000 random sample splits. FPR, false-positive rate.