PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data
Fig 7
Comparison of the PICDG framework with the existing Moran’s I test algorithm for predicting driver genes’ inference in immune cells.
The driver genes identified through Moran’s I test display a lower average expression level compared to the expression level of driver genes presented by the PICDGI computational framework. The genes are ranked from the highest to the lowest immune-suppressive role (1 to 10): (A) Single-cell atlas map the trajectory and time values of cells progression; (B) Mast cell; (C) Natural Killer; (D) T cell; (E) B cell; (F) Dendritic cell.