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Dissection and integration of bursty transcriptional dynamics for complex systems.

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Bibliographic Details
Title: Dissection and integration of bursty transcriptional dynamics for complex systems.
Authors: Gaoa, Cheng Frank1, Vaikuntanathana, Suriyanarayanan2, Riesenfel, Samantha J.3,4,5
Source: Proceedings of the National Academy of Sciences of the United States of America. 4/30/2024, Vol. 121 Issue 18, p1-12. 50p.
Subject Terms: *SYSTEM dynamics, *DISSECTION, *RNA sequencing, *VELOCITY, *TIME management
Abstract: RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
Description
Abstract:RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses. [ABSTRACT FROM AUTHOR]
ISSN:00278424
DOI:10.1073/pnas.2306901121