Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information.

ABSTRACT: The time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering

SUBMITTER: Peng Q 

PROVIDER: S-EPMC11611270 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature