ArticleRoyal Society open science2024
Approximate Bayesian computation for inferring Waddington landscapes from single-cell data.
Article in Royal Society open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Molecular noise modulates transitions in the cell-fate differentiation landscape.NPJ systems biology and applications · 2026Article
- Towards a mathematical framework for modelling cell fate dynamics.Journal of mathematical biology · 2025Article
- Transition paths across the epithelial-mesenchymal transition landscape are dictated by network logic.Development (Cambridge, England) · 2025Article
- Approximate Bayesian computation for inferring Waddington landscapes from single-cell data.Royal Society open science · 2024Article
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4 authors.
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Abstract
Single-cell technologies allow us to gain insights into cellular processes at unprecedented resolution. In stem cell and developmental biology snapshot data allow us to characterize how the transcriptional states of cells change between successive cell types. Here, we show how approximate Bayesian computation (ABC) can be employed to calibrate mathematical models against single-cell data. In our simulation study, we demonstrate the pivotal role of the adequate choice of distance measures appropriate for single-cell data. We show that for good distance measures, notably optimal transport with the Sinkhorn divergence, we can infer parameters for mathematical models from simulated single-cell data. We show that the ABC posteriors can be used (i) to characterize parameter sensitivity and identify dependencies between different parameters and (ii) to construct representations of the Waddington or epigenetic landscape, which forms a popular and interpretable representation of the developmental dynamics. In summary, these results pave the way for fitting mechanistic models of stem cell differentiation to single-cell data.
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