CMAPS: Causal Mediation Analysis of Perturbation Screens with Application to Genome-scale Perturb-seq Data

CRISPR-Cas9 perturbation screens coupled with single-cell multi-omic profiling enable dissection of gene regulatory mechanisms, yet existing analyses largely quantify total perturbation effects and offer limited insight into the molecular intermediates that transmit these effects. We introduce CMAPS (Causal Mediation Analysis for Perturbation Screens), a semiparametric framework for robust mediation analysis that accommodates unmeasured mediator-outcome confounding and incorporates an adaptive bootstrap test with false discovery rate control. Simulations and data-driven computational experiments show that CMAPS yields accurate, calibrated mediation estimates and robust mediator identification, as confirmed through negative controls and permutation-based validation. Applied to K562 Perturb-seq, CMAPS recapitulates transcriptional cascades downstream of GATA1. In BT16 MultiPerturb-seq data, CMAPS identifies promoter-centric, enhancer-distributed, and mixed cis-regulatory programs linking chromatin remodeling factors to transcriptional responses. CMAPS provides a rigorous and interpretable framework for mechanistic inference in single-cell perturbation screens. CMAPS is implemented in R and is available at https://github.com/keleslab/CMAPS.