PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data
Fig 5
Predicted vs. observed gene expression levels in epithelial cells.
(A-C) Scatterplots illustrating the performance of the PICDGI framework in predicting epithelial cell gene expression across the Early, Mid, and Late stages of LUAD progression for Patients 1, 2, and 3, respectively. Each plot shows the relationship between true gene expression (TGE) and predicted gene expression (PGE), with Pearson’s correlation coefficient (ρ), coefficient of determination (R²), and corresponding p-value computed using a two-sided t-test. (D) Summary of predictive accuracy across stages. Barplots display the mean Pearson correlation coefficients (ρ) ± SEM (Standard Error of the Mean) for the comparison between TGE and PGE at each of the three time points; Early, Mid, Late for each patient. These summary statistics complement the scatterplots by providing an aggregated view of model performance across genes. From top to bottom, the panels correspond to Patient 1, Patient 2, and Patient 3.