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Optimal Transport in Single-Cell Omics

Definition
AI-generated

Optimal transport in single-cell analysis treats two populations of cells (e.g. control vs perturbed, or two time points) as distributions and finds a minimum-cost coupling that maps cells or mass between them.

Topics
Single-cell analysis
Related
Causal Machine Learning for Single-Cell Genomics Deep Generative Model In Silico Perturbation Prediction

Why it matters in GWAS

When integrating single-cell perturbation or disease models with reference atlases, transport-based methods formalize how expression shifts under intervention—useful for interpreting whether a GWAS hit’s predicted knockdown effect matches a disease-associated program.

Example usage

"The methods explicitly include Optimal Transport in Single-Cell Omics to support interpretation of the main findings."

Causal Machine Learning for Single-Cell Genomics Deep Generative Model In Silico Perturbation Prediction Pseudotime Trajectory Inference
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

  • Schiebinger G, et al. (2019). Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell.
  • Bunne C, et al. (2024). Optimal transport for single-cell and spatial omics. Nat Rev Methods Primers.
  • 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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