scPathCF: Intervention-Based Path Decomposition for Perturbation-Aware Protein Prediction

A central goal in functional genomics and drug discovery is to predict how protein levels change after a genetic perturbation, especially for surface proteins that serve as immune checkpoints and therapeutic targets. A protein change can follow a corresponding RNA change or arise from regulation that is not explained by the measured RNA. Existing methods typically predict protein directly from RNA or predict the perturbed transcriptional state, without separating these two sources of protein change. We present \method, a perturbation-aware predictor that decomposes protein abundance into a control baseline, an RNA-mediated increment, and a perturbation-dependent residual increment. The mediated head reads RNA-related representations but not perturbation identity, preventing a direct identity-to-protein shortcut. The residual head reads the perturbation embedding and an optional offline screening prior, but not factual RNA or cell context. Hard branch switches assemble a control, a mediator-blocked, and a factual state; their differences define the two increments as executable interventions. Across three public CRISPR–CITE-seq screens, the decomposition retains competitive predictive accuracy with a strict-zero prior on Papalexi, a full-data screen on Frangieh, and curated annotations on CaRPool, and the branch interventions reproduce contrasts consistent with known post-transcriptional and RNA-coupled regulation. The state differences provide an explicit account of how the fitted predictor allocates each response between its branches. Code is available at https://osf.io/xhzes/?view_only=d03a9e76234b425ead29d60f35586c85.