COMPASS: Component-Wise Inference of Shared and Gene-Specific Perturbation Response

Predicting how a genetic perturbation reshapes a cell's transcriptome is a central goal of computational biology. Previous studies report that the mean response across training perturbations rivals specialized models on standard accuracy metrics, even though it cannot distinguish which perturbation occurred. Across 2,270 CRISPRi perturbations measured in each of six cell lines, we show that this apparent paradox reflects a conserved organization of perturbation responses. Perturbations span a continuum from responses strongly aligned with the mean to more targeted responses that depart from it. Crucially, a perturbation's position along this continuum is conserved across cell lines (Kendall's W=0.59) and predictable from STRING protein-interaction embeddings (R^2=0.35). We formalize this structure with COMPASS, an interpretable linear model that decomposes each response into shared and gene-specific components and estimates them separately. The shared-response component is modeled as a cell-line-wide response scaled by a perturbation-specific coefficient. This coefficient is strongly conserved across cell lines. The residual gene-specific component---which is moderately conserved across cell lines---recovers pathway-level programs. COMPASS outperforms scGPT, CPA, GEARS, GenePert, and State in both response accuracy (de-biased Pearson delta 0.34 vs. <=0.32) and perturbation discrimination (cosine PDS gain 0.23 vs. <=0.08). These results recast perturbation prediction across cellular contexts as component-wise inference, with each component estimated from the evidence best suited to it.