ArticleNature methods2023
Causal identification of single-cell experimental perturbation effects with CINEMA-OT.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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52 citing papers in PubMed, 70 citations in OpenAlex.
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- Disentangling factors affecting bacterial transcriptional regulatory network inference.iScience · 2026Article
- Single-cell analysis of microglia and monocyte dynamics uncovers distinct TNF-driven neuroimmune signatures in humans after intracerebral hemorrhage.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Article
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- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Benchmarking the prediction of responding cells to perturbations affecting both gene expression and cellular abundance using scRNA sequencing.Scientific reports · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Single-Cell Analysis of Microglia and Monocyte Dynamics Uncover Distinct TNF-α-driven Neuroimmune Signatures after Intracerebral Hemorrhage.bioRxiv : the preprint server for biology · 2026Article
- pertTF: context-aware AI modeling for genome-scale and cross-system perturbation prediction.bioRxiv : the preprint server for biology · 2026Article
- Uncovering causal relationships in single-cell omic studies with causarray.Briefings in bioinformatics · 2026Article
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- Single-cell disentangled representations for perturbation modeling and treatment effect estimation.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
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. Here 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.