Gene expression ranking change based single sample pre-disease state detection.

Introduction: To prevent disease, it is of great importance to detect the critical point (pre-disease state) when the biological system abruptly transforms from normal to disease state. However, rapid and accurate pre-disease state detection is still a challenge when there is only a single sample available. The state transition of the biological system is driven by the variation in regulations between genes. Methods: In this study, we propose a rapid single-sample pre-disease state-identifying method based on the change in gene expression ranking, which can reflect the coordinated shifts between genes, that is, S-PCR. The R codes of S-PCR can be accessed at https://github.com/ZhenshenBao/S-PCR. Results: This model-free method is validated by the successful identification of pre-disease state for both simulated and five real datasets. The functional analyses of the pre-disease state-related genes identified by S-PCR also demonstrate the effectiveness of this computational approach. Furthermore, the time efficiency of S-PCR is much better than that of its peers. Discussion: Hence, the proposed S-PCR approach holds immense potential for clinical applications in personalized disease diagnosis.