Graph-Dynamo: Learning stochastic cellular state transition dynamics from single cell data

Zhang, Yan; Qiu, Xiaojie; Ni, Ke; Weissman, Jonathan; Bahar, Ivet; Xing, Jianhua

doi:10.1101/2023.09.24.559170

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…They applied to EMT datasets and demonstrated that the method recovers gene regulation relations such as activation of SNAIL1 on mesenchymal genes. Separately, Zhang et al also developed a graph Dynamo formalism that describes the full dynamics including convection from the vector field, diffusion from stochastic processes, and cell birth‐death in a multidimensional cell state space (Zhang et al, 2023). Graph Dynamo also uses both the temporal cell density distributions and RNA velocities to constrain model parameters.…”

Section: Unravel Emt Transition Paths Through Snapshot Capture Of Cel...mentioning

confidence: 99%

Data‐ and theory‐driven approaches for understanding paths of epithelial–mesenchymal transition

Hong,

Xing

2024

Genesis

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SummaryReversible transitions between epithelial and mesenchymal cell states are a crucial form of epithelial plasticity for development and disease progression. Recent experimental data and mechanistic models showed multiple intermediate epithelial–mesenchymal transition (EMT) states as well as trajectories of EMT underpinned by complex gene regulatory networks. In this review, we summarize recent progress in quantifying EMT and characterizing EMT paths with computational methods and quantitative experiments including omics‐level measurements. We provide perspectives on how these studies can help relating fundamental cell biology to physiological and pathological outcomes of EMT.

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Section: Unravel Emt Transition Paths Through Snapshot Capture Of Cel...mentioning

confidence: 99%

Data‐ and theory‐driven approaches for understanding paths of epithelial–mesenchymal transition

Hong,

Xing

2024

Genesis

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Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Lederer,

Leonardi,

Talamanca

et al. 2024

Nat Methods

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Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.

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Learning cell-specific networks from dynamics and geometry of single cells

Zhang

Stumpf

2023

Preprint

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Cellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem that is central to systems biology. The majority of existing network inference methods work at the population level and construct a static representations of gene regulatory networks; they do not naturally allow for inference of differential regulation across a heterogeneous cell population. Building upon recent dynamical inference methods that model single cell dynamics using Markov processes, we propose locaTE, an information-theoretic approach which employs a localised transfer entropy to infer cell-specific, causal gene regulatory networks. LocaTE uses high-resolution estimates of dynamics and geometry of the cellular gene expression manifold to inform inference of regulatory interactions. We find that this approach is generally superior to using static inference methods, often by a significant margin. We demonstrate that factor analysis can give detailed insights into the inferred cell-specific GRNs. In application to two experimental datasets, we recover key transcription factors and regulatory interactions that drive mouse primitive endoderm formation and pancreatic development. For both simulated and experimental data, locaTE provides a powerful, efficient and scalable network inference method that allows us to distil cell-specific networks from single cell data.

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Graph-Dynamo: Learning stochastic cellular state transition dynamics from single cell data

Cited by 4 publications

References 32 publications

Data‐ and theory‐driven approaches for understanding paths of epithelial–mesenchymal transition

Data‐ and theory‐driven approaches for understanding paths of epithelial–mesenchymal transition

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Learning cell-specific networks from dynamics and geometry of single cells

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