Learning dynamical information from static protein and sequencing data

Pearce, Philip L.; Woodhouse, Francis G.; Forrow, Aden; Kelly, Ashley J.; Kusumaatmaja, Halim; Dunkel, Jörn

doi:10.1038/s41467-019-13307-x

Cited by 18 publications

(14 citation statements)

References 78 publications

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“…For example, it has already been used to solve the difficult task of elucidating the intricate three-dimensional spatio-temporal patterns associated with turbulent flows 55 . Furthermore, machine learning can provide invaluable help to infer dynamic information and underlying models from static information 56,57 .…”

Section: Box 1 | Overview Of Machine-learning Methodsmentioning

confidence: 99%

Machine learning for active matter

et al. 2020

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Section: Box 1 | Overview Of Machine-learning Methodsmentioning

confidence: 99%

Machine learning for active matter

et al. 2020

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“…For Fokker-Planck equation of the over-damped Langevin equation, the expansion of steady-state solution near stable points (attractors) indeed yields a Gaussian-mixture distribution 61 . Motivated by this, to obtain the global dynamical manifold we fit a Gaussian mixture model with a mixture weight μ* to obtain the stationary distribution of coarse-grained dynamics.…”

Section: Integrating the Rwtpm At Three Levelsmentioning

confidence: 99%

Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

Zhou

Wang

et al. 2021

Nat Commun

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Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

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“…The only solution seems then to combine analytical results with simulations, in the same philosophy than [5], but this leads to much more complex and time-consuming algorithms that the one which is developed in this article. To our point of view, this also argues against methods where a mixture model is used for reconstructing the temporal dynamics of a metastable process from stationary distributions [36].…”

Section: Limits Of the Methods For Finding The Developmental Landscapementioning

confidence: 99%

Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics

Ventre

2021

Preprint

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Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference.

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Learning dynamical information from static protein and sequencing data

Cited by 18 publications

References 78 publications

Machine learning for active matter

Machine learning for active matter

Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics

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