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. 2023 Nov 2;20(11):1769–1779. doi: 10.1038/s41592-023-02040-5

Fig. 2. Key functionalities of CINEMA-OT.

Fig. 2

a, CINEMA-OT takes scRNA-seq data labeled by treatment condition as input. CINEMA-OT learns a confounder embedding that is mixed across batches and matches counterfactual cell pairs across conditions to compute causal perturbation effects. b, The single-cell-level treatment-effect matrices can be further clustered, and gene set enrichment analysis can be conducted on the output. GO, Gene Ontology. c, Single-cell-level synergy in combinatorial perturbations can be obtained as the dissimilarity of extrapolated phenotypes and true combinatorially perturbed phenotypes. d, CINEMA-OT can attribute divergent treatment effects to either explicit confounders or latent confounders by analysis of cluster-wise response matching matrices.