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

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a, scRNA-seq count data is first decomposed into confounder variation and treatment-associated variation using ICA. Cells are then matched across treatment conditions by entropy-regularized optimal transport in the confounder space to generate a causal matching plan. The smooth matching map can then be used to estimate individual treatment effects. b, Illustration of the properties of CINEMA-OT compared with other potential matching schemes, including global matching (minimizing the average difference) and local matching (finding nearest neighbors for each cell). c, Illustration of the differential abundance issue in the unweighted CINEMA-OT method, and the resampling procedure used in CINEMA-OT-W.