Zhenshen Bao ^1* Xianbin Li ² Peng Xu ³ Xiangzheng Zan ^4*

• ¹ Taizhou University, Taizhou, Zhejiang Province, China
• ² Jiujiang University, Jiujiang, Jiangxi Province, China
• ³ Guangzhou University, Guangzhou, Guangdong Province, China
• ⁴ Shenzhen Pengcheng Technician College, Shenzhen, China

To prevent disease, it is of great importance to detect the critical point (pre-disease state) when biological system abruptly transforming from normal to disease. However, rapid and accurate predisease state detection is still a challenge when there is only a single sample available. The state transition of biological system is actually driven by the variation of regulations between genes. In this study, we propose a rapid Single Sample Pre-disease state identified method based on the Change of gene expression Ranking, which can reflect the coordinated shifts between genes, i.e. S-PCR. This model-free method is validated by the successful identification of pre-disease state for both a 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 its peers. Hence, the proposed S-PCR approach holds immense potential for clinical applications in personalized disease diagnosis. The R codes of S-PCR can be accessed at https://github.com/ZhenshenBao/S-PCR.