AI Insight
CMAPS is a semiparametric statistical framework designed to perform causal mediation analysis on CRISPR perturbation screen data combined with single-cell multi-omic profiling, going beyond measuring total perturbation effects to identify the molecular intermediates that transmit those effects. The method incorporates controls for unmeasured confounding between mediators and outcomes, and uses an adaptive bootstrap procedure with false discovery rate control to reliably identify mediators. When applied to real datasets, CMAPS successfully reconstructed transcriptional cascades downstream of GATA1 in K562 cells and identified distinct cis-regulatory programs linking chromatin remodeling factors to transcriptional responses in BT16 cells.
Why it matters
Understanding the intermediate molecular steps through which genetic perturbations exert their effects is critical for deciphering gene regulatory networks and identifying potential therapeutic targets. CMAPS provides a principled computational tool that could broadly improve mechanistic interpretation of large-scale functional genomics experiments.
⚠️ Preprint – Noch nicht peer-reviewed
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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.