PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data
Fig 6
Cancer driver genes with the highest driver coefficient.
(A) Barplot showing the driver coefficients of epithelial cell genes derived from patient 1 gene expression data using the PICDGI framework. Data are presented as mean + /- SEM (Standard Error of the Mean). Black cross marks indicate genes previously identified as oncogenes (OGs) or tumor suppressor genes (TSGs). (B) Heatmap showing PICDGI-derived DrCoef values for the top 30 epithelial driver genes (selected based on panel A) recalculated independently within each annotated immune cell type from patient 1 single-cell data. DrCoef values in this panel are computed using cell-type specific models, enabling assessment of the regulatory influence of epithelial-identified driver genes across immune compartment. (C) Boxplots comparing transcription factor (TF) expression and TF activity between normal epithelial and cancer cells for two representative TFs showing discordance between differential activity and differential expression. P-values for differential TF activity and expression were calculated using a t-test and Wilcoxon rank-sum test, respectively. Boxplot elements indicate the median (horizontal line), interquartile range (box), and whiskers extending to 1.5 × interquartile range.