scvelo

RNA velocity analysis infers cell state transitions by measuring spliced and unspliced mRNA dynamics in single-cell RNA-seq data. This Skill reconstructs developmental trajectories and identifies driver genes without requiring time-course experimental data.

RNA velocity estimates cellular developmental trajectories by modeling unspliced and spliced mRNA ratios to predict future cell states. It identifies driver genes as those whose transcriptional dynamics best explain the observed cell state transition trajectories.

Yes, RNA velocity analysis requires an AnnData object containing distinct 'spliced' and 'unspliced' layers. You must preprocess your single-cell RNA-seq data using tools like velocyto or alevin-fry to generate these required input layers.

Yes, RNA velocity analysis is optimized for biological processes like hematopoiesis. It reconstructs single-cell developmental trajectories to infer cell differentiation stages and identifies key regulatory driver genes during cell state transitions.

Trajectory inference requires AnnData objects with spliced and unspliced layers generated by velocyto or alevin-fry. This Skill integrates with scanpy, numpy, scipy, and matplotlib to perform RNA velocity analysis on the preprocessed single-cell data.

RNA velocity relies on the ratio of unspliced to spliced mRNA to calculate the transcriptional rate of change. Without these distinct layers in your AnnData object, the algorithm cannot estimate cellular RNA dynamics or infer cell state transitions.