Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription, and splicing from single-cell RNA sequencing data with TSvelo
RNA velocity approaches fit gene dynamics and infer cell fate by modeling the splicing process using single-cell RNA sequencing (scRNA-seq) data. However, due to the short time scale of splicing, high noise, and large complexity of data, existing RNA velocity methods often fail to precisely capture the complex velocity dynamics for individual genes and single cells, which makes their downstream…
Researchers have developed TSvelo, a sophisticated mathematical framework designed to analyze RNA velocity by modeling the interconnected processes of gene regulation, transcription, and splicing in single-cell RNA sequencing (scRNA-seq) data. Traditional RNA velocity methods struggle to accurately capture the intricate dynamics of individual genes and cells due to the brief nature of splicing, high noise levels, and the sheer complexity of the data.
TSvelo addresses these challenges through the use of neural ordinary differential equations, which provide a highly interpretable approach to understanding the 3D dynamics of transcription, unspliced, and spliced states across all genes at once. This framework is capable of inferring a unified temporal factor shared by genes within a single cell, making it applicable to multi-lineage datasets.
The effectiveness of TSvelo was validated through experiments conducted on six diverse scRNA-seq datasets, with two of them being multi-lineage. The results clearly demonstrated that TSvelo outperforms existing RNA velocity methods, offering a more reliable and robust approach to downstream analysis.
Written by urgent.news from eLife's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
