Figure 1.

Flowchart of the individual-level pathway score (iPS) approach. (a) For a group of $n$ normal samples, compute the correlations, i.e., the $PC{C}_n$ (the Pearson correlation coefficient ($PCC$) of an edge in each pathway with n samples) of each edge in the pathway based on expression data. Then, a new individual sample d is added to the group, and the new correlation of each edge in the pathways is calculated in the same way by the $PC{C}_{n+1}$ of the combined data. (b) The pathway-based differential correlation is calculated by the difference of the corresponding edge in terms of $PCC$, i.e., $∆PCC$ = $PC{C}_{n+1}$ − $PC{C}_n$ for each edge. The sum of all edges, the $∆PCC$ value, is obtained for each pathway. (c) iPS is defined as an individual pathway-based score that can quantify the correlation perturbation of each pathway in each sample. Finally, analyses of distribution, classification, survival, and prognosis are performed.