PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data
Fig 2
(A) Representation of gene-gene interaction effects (GIE) in cancer progression. Nodes denote genes and edges denote regulatory interactions, with statistical variability in interactions contributing to genetic heterogeneity. Five categories of genes are considered, with their interaction effects differing by type. (B) Illustration of GIE strength: oncogenes (OGs) and tumor suppressor genes (TSGs) are expected to exert stronger effects on network dynamics compared with other gene classes. (C) Computational formulation of PICDGI. The model links observed temporal gene expression data to hidden variables at two levels: (i) local hidden variables (e.g., gene-specific mutations and expression fluctuations) and (ii) global hidden variables capturing the overall GIE structure across the network. (D) Inference procedure. The effect of a gene on driving mutations in other genes is quantified through the highest density interval (HDI) of the posterior distribution over gene expression dynamics, integrating both temporal patterns and estimated gene–gene interactions.