Abstract
Understanding gene expression levels is crucial for comprehending gene functions, gene-gene interactions and disease mechanisms. Differential gene expression (DGE) analysis is a widely used statistical approach that offers insights by comparing gene expression across various conditions. However, traditional DGE methods focus on what are known as marginal associations, which refer to correlations observed between gene expression and a trait of interest, even if that association is indirect or not causal. To address this limitation, we introduce conditional differential gene expression (CDGE) analysis, a framework designed to identify direct effect genes. Direct effect genes are those whose changes in expression causally and directly impact downstream biological processes of interest. In applications to three RNA sequencing datasets (including one genome-scale perturb-seq dataset), CDGE analysis identifies that only a small fraction of differentially expressed genes has direct effects and mediate most other gene actions. These direct effect genes offer greater biological insight in enrichment analyses involving protein interactions and pathways. This suggests that CDGE yields more informative conclusions on causal gene effects and could become a key tool for studying biological pathways.