Quantifying critical states of complex diseases using single-sample dynamic network biomarkers
Fig 1
Disease progression and dynamic network biomarkers.
(A) Three states during a disease progression. Clearly, there are significant differences between normal and disease states in terms of molecular expressions, and that is why traditional biomarkers can identify the disease state based on the differential information between them. But generally there is no significant difference between normal and pre-disease states, and thus traditional biomarkers may fail to detect the critical state for correctly predicting the disease. (B) Flowchart for calculating the composite index of single-sample dynamic network biomarkers (sDNB), which can detect the pre-disease state based on the three statistical conditions, rather than the differential expressions. Reference samples are required to produce the reference data. The distribution of every gene in terms of expression can be obtained from the reference samples, and the absolute value of the difference between a gene’s expression in an individual sample d and the average value of the gene’s expression in the reference samples is defined as the single-sample expression deviation (sED) of the gene for sample d. The Pearson correlation coefficient (PCC) between two genes in the reference samples is defined as PCCn. After the expression profile of sample d is added to the reference samples, the new correlation coefficient between the two genes can be obtained as PCCn+1. The difference between PCCn and PCCn+1 can be regarded as the single-sample PCC (sPCC) between the two genes for sample d. The detail computation procedure of the sDNB score Is is described in Fig 2.