Performs advanced RNA velocity analysis to infer cell state transitions and developmental trajectories from single-cell transcriptomics data.
The scVelo skill equips Claude with specialized knowledge for modeling RNA splicing kinetics in single-cell RNA-seq datasets. By leveraging the ratio between unspliced and spliced mRNA, it enables researchers to reconstruct developmental lineages, estimate latent time, and identify the specific driver genes behind cellular differentiation. This skill provides implementation patterns for both stochastic and dynamical modeling, integrating seamlessly with the Python single-cell ecosystem including Scanpy and AnnData to transform snapshot data into directional trajectories.
Características Principales
01Driver gene identification and velocity ranking
02Directional trajectory visualization on UMAP and t-SNE
03Stochastic and dynamical RNA velocity modeling
04Automated preprocessing and moment computation workflows
05Latent time and pseudotime estimation
060 GitHub stars
Casos de Uso
01Adding directional flow information to static single-cell clusters
02Predicting cell fate transitions in developmental biology and disease progression
03Identifying key genes driving specific cellular differentiation pathways