Perform RNA velocity analysis on single-cell RNA-seq AnnData objects.
npx skills add https://github.com/yf8578/clawomics --skill scvelo-yf8578
Please help me install this Agent Skill. Skill: scvelo Source: https://github.com/yf8578/clawomics/tree/main/skills/scvelo Command: npx skills add https://github.com/yf8578/clawomics --skill scvelo-yf8578
This Skill enables the analysis of RNA velocity in single-cell RNA-seq data, allowing researchers to infer cell differentiation trajectories and predict cell fates from snapshot data.
Run the scvelo skill to perform RNA velocity analysis on the provided AnnData object.
High-intent search queries and answers about installing and using this skill.
RNA velocity analysis infers cell differentiation trajectories by using spliced and unspliced mRNA counts to estimate gene expression dynamics and predict cell fates from snapshot data. This Skill computes directional cell state transitions using scVelo.
RNA velocity estimates cell state transitions by measuring the ratio of spliced to unspliced mRNA within single-cell RNA-seq data. This dynamic ratio indicates whether genes are being actively transcribed or degraded, allowing researchers to predict the future state of individual cells.
Yes, RNA velocity analysis requires AnnData objects containing both 'spliced' and 'unspliced' mRNA layers to compute gene expression dynamics. These layers are essential for scVelo to estimate transcriptional dynamics and infer cell state transitions.
Yes, this RNA velocity analysis is fully compatible with existing Scanpy workflows. It operates directly on AnnData objects managed by Scanpy, allowing seamless integration of trajectory inference and cell fate prediction into standard single-cell pipelines.
Identifying driver genes is achieved by analyzing RNA velocity dynamics to pinpoint genes exhibiting significant expression changes during cell state transitions. This Skill utilizes scVelo to highlight key transcription factors and regulators driving lineage commitment.
RNA velocity relies on accurate spliced and unspliced mRNA quantification, requiring high-quality single-cell RNA-seq data with loom or AnnData inputs. It assumes constant transcription and degradation rates across the cell population, which may not hold true for all complex biological systems.
JosephWoodallInfers cell state transitions from mRNA dynamics using scvelo and Scanpy.
crazymsnIdentify cellular trajectories from scRNA-seq data using scVelo dynamical models.
mkurmanAnalyze RNA velocity in single-cell RNA-seq data with scVelo models.
Estrella-231Infer RNA velocity and cell fate trajectories from single-cell RNA-seq AnnData.
