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Causal Machine Learning for Single-Cell Genomics

Definition
AI-generated

Causal machine learning for single-cell genomics aims to estimate perturbation effects, counterfactual cell states, or regulatory structure from high-dimensional single-cell data while addressing confounding, batch structure, and missing interventions.

Topics
Single-cell analysis
Related
Disentangled Representation Learning In Silico Perturbation Prediction Optimal Transport in Single-Cell Omics

Why it matters in GWAS

Observational sc-eQTL and atlas data are associative; causal ML frames when predicted responses to a gene knockdown or drug—in silico or measured—can support mechanistic claims about GWAS candidate genes without over-interpreting correlation.

Example usage

"Causal machine learning for single-cell genomics prioritized cell states most consistent with putative intervention targets."

Disentangled Representation Learning In Silico Perturbation Prediction Optimal Transport in Single-Cell Omics Perturb-seq Single-Cell Perturbation Genomics
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Tejada-Lapuerta A, et al. (2025). Causal machine learning for single-cell genomics. Nat Genet.
  • Dong M, et al. (2023). Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nat Methods.
  • Dimitrov D, Schrod S, Rohbeck M, et al. (2026). Interpretation, extrapolation and perturbation of single cells. Nat Rev Genet. https://doi.org/10.1038/s41576-025-00920-4
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