Abstract
Complex diseases may not always progress in a gradual manner. In the early stages of complex diseases, obvious symptoms are usually not observable, but there is a commonality: there is a brief state of predisease between the progression from normal state to disease state, which usually includes three stages: normal state, critical state of predisease, and disease state. Identifying this critical state, especially with a single sample from an individual, remains a difficult task. In this study, we applied three methods, i.e., single-sample Jensen-Shannon Divergence (sJSD), network information gain (NIG), and temporal network flow entropy (TNFE) method, to a simulated dataset and type 2 diabetes (GSE13268 and GSE13269). Three different methods were utilized to create indexes, including the Inconsistency Index (ICI), NIG, and TNFE, to measure the overall disruption caused by individual samples compared to a set of reference samples. Changes in these indexes were used to identify critical states during the progression of the disease. Results from the numerical simulations show the effectiveness of the three methods. All the methods can detect two critical states based on a single sample, which are respectively at 8 weeks and 16 weeks for GSE13268 and at 4 weeks and 16 weeks for GSE13269, indicating the critical states before deterioration can be detected and the dynamic network biomarkers (DNBs) can be identified successfully. But there are differences in the sensitivity of predictive indicators based on the three methods. The identified dynamic network biomarkers are also significantly different. In addition, the computational principles of the three methods are compared. The proposed three methods can effectively detect the critical state and identify the DNB, solely based on a single sample. The three methods are data-driven and model-free on an individual basis. sJSD method is more sensitive to the critical state, while NIG and TNFE methods are more robust and effective. They can therefore not only help future studies of personalized disease diagnosis but also provide a better insight into clinical practice.