repo for modeling and analyzing metabolic labeling data
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csp4ml's Introduction
Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information
Storm is a parametric inference framework for labeled kinetics data and one-shot data based on the chemical master equation. Three different stochastic models are solved analytically: a model considering only transcription and degradation, a model considering gene expression switching, transcription and degradation, and a model considering transcription, splicing and degradation. For the kinetics data, we inferred gene-specific parameters without relying on the steady-state assumption, and for the one-shot data, the steady-state assumption was re-invoked. Furthermore, Storm relaxes the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state specific transcriptional regulations.
Highlights of Storm
Storm does not require steady-state assumptions on the kinetics experiments.
Storm's stochastic model-based approach is more consistent with real biological process than the previous deterministic ODE model.
Quantitative results show that the RNA velocity inferred by Storm outperforms previous methods in terms of both consistency and correctness.