scvelo

To infer cell trajectories from single-cell RNA-seq data, you can use RNA velocity analysis to model mRNA splicing kinetics. This approach estimates cell state transitions and developmental directions directly from snapshot data by analyzing ratios of unspliced to spliced mRNA.

RNA velocity is a computational method that models splicing kinetics to determine cell differentiation direction. It identifies driver genes by detecting which genes exhibit expression dynamics that actively explain observed transitions between cell states in single-cell datasets.

Yes, you need scanpy and scvelo to process AnnData objects for trajectory inference. The AnnData objects must specifically contain spliced and unspliced count layers to successfully calculate RNA dynamics and estimate latent time.

You can estimate latent time and map developmental processes like hematopoiesis or neurogenesis by applying RNA velocity analysis. This technique uses splicing kinetics within single-cell RNA-seq data to reconstruct the continuous progression of cell fate decisions.

Trajectory inference using spliced and unspliced mRNA ratios depends heavily on accurate kinetic modeling and high-quality count layers. It requires single-cell RNA-seq datasets where splicing information is reliably captured, limiting its use with degraded samples or incompatible protocols.