ArticleNature communications2024
Advanced methods for gene network identification and noise decomposition from single-cell data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Using the simple telegraph model to decipher transcriptional burst regulation across genome-wide data.iScience · 2026Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- Dynamic gene regulatory network inference from single-cell data using optimal transport.Bioinformatics (Oxford, England) · 2025Article
- Advanced methods for gene network identification and noise decomposition from single-cell data.Nature communications · 2024Article
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4 authors.
Funding
Abstract
Central to analyzing noisy gene expression systems is solving the Chemical Master Equation (CME), which characterizes the probability evolution of the reacting species' copy numbers. Solving CMEs for high-dimensional systems suffers from the curse of dimensionality. Here, we propose a computational method for improved scalability through a divide-and-conquer strategy that optimally decomposes the whole system into a leader system and several conditionally independent follower subsystems. The CME is solved by combining Monte Carlo estimation for the leader system with stochastic filtering procedures for the follower subsystems. We demonstrate this method with high-dimensional numerical examples and apply it to identify a yeast transcription system at the single-cell resolution, leveraging mRNA time-course experimental data. The identification results enable an accurate examination of the heterogeneity in rate parameters among isogenic cells. To validate this result, we develop a noise decomposition technique exploiting time-course data but requiring no supplementary components, e.g., dual-reporters.
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