Personalized Analysis by Validation of Monte Carlo for Application of Pathways in Cardioembolic Stroke

Abstract

BACKGROUND Cardioembolic stroke (CES), which causes 20% cause of all ischemic strokes, is associated with high mortality. Previous studies suggest that pathways play a critical role in the identification and pathogenesis of diseases. We aimed to develop an integrated approach that is able to construct individual networks of pathway cross-talk to quantify differences between patients with CES and controls. MATERIAL AND METHODS One biological data set E-GEOD-58294 was used, including 23 normal controls and 59 CES samples. We used individualized pathway aberrance score (iPAS) to assess pathway statistics of 589 Ingenuity Pathways Analysis (IPA) pathways. Random Forest (RF) classification was implemented to calculate the AUC of every network. These procedures were tested by Monte Carlo Cross-Validation for 50 bootstraps. RESULTS A total of 28 networks with AUC >0.9 were found between CES and controls. Among them, 3 networks with AUC=1.0 had the best performance for classification in 50 bootstraps. The 3 pathway networks were able to significantly identify CES versus controls, which showed as biomarkers in the regulation and development of CES. CONCLUSIONS This novel approach could identify 3 networks able to accurately classify CES and normal samples in individuals. This integrated application needs to be validated in other diseases.

Figure 1

Heatmap of pathway pairs in each bootstrap. Bootstraps were clustered with the abscissa and pairs of pathways were clustered with the ordinate.

Figure 2

The best individual differential networks repeated 50 bootstraps. (A) The individual network in 10 bootstraps. (B) The individual network in 10 bootstraps. (C) The individual network in 23 bootstraps.