Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Recent advancements in single-cell technologies allow characterization of
experimental perturbations at single-cell resolution. While methods have
been developed to analyze such experiments, the application of a strict
causal framework has not yet been explored for the inference of treatment
effects at the single-cell level. In this work, we present a causal
inference-based approach to single-cell perturbation analysis, termed
CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal
Transport). CINEMA-OT separates confounding sources of variation from
perturbation effects to obtain an optimal transport matching that reflects
counterfactual cell pairs. These cell pairs represent causal perturbation
responses permitting a number of novel analyses, such as individual
treatment effect analysis, response clustering, attribution analysis, and
synergy analysis. We benchmark CINEMA-OT on an array of treatment effect
estimation tasks for several simulated and real datasets and show that it
outperforms other single-cell perturbation analysis methods. Finally, we
perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus
and cigarette smoke-exposed airway organoids, and (2) combinatorial
cytokine stimulation of immune cells. In these experiments, CINEMA-OT
reveals potential mechanisms by which cigarette smoke exposure dulls the
airway antiviral response, as well as the logic that governs chemokine
secretion and peripheral immune cell recruitment.