Fig. 1.

Neutrophil RNA expression differences between patients with intracranial aneurysms (IA) and IA-free controls, feature selection for classification model creation, and model training. a Transcriptome profiling demonstrated 95 differently expressed transcripts (q-value < 0.05) between patients with IA and controls. Of these, 26 had a false discovery rate (FDR) < 0.05 and an absolute fold change ≥ 1.5 (in red). b Principal component analysis (PCA) using these 26 transcripts demonstrated general separation between samples from patients with IA (60%, circled in red) and those from controls (80%, circled in blue). c Estimation of model performance during leave-one-out (LOO) cross-validation in the training cohort demonstrated that most models performed with an accuracy of 0.50–0.73. Among the classification models, diagonal linear discriminant analysis (DLDA) had the highest combination of sensitivity, specificity, and accuracy (0.67, 0.80, 0.73 respectively). d Receiver operating characteristic (ROC) analysis using classifications in the training dataset showed that the models had areas under the curve of 0.54 (support vector machines [SVM]) to 0.73 (DLDA). (F-C: fold-change; ABS: absolute value; Cosine NN: cosine nearest neighbors; NSC: nearest shrunken centroids)