Perform RNA velocity analysis on single-cell RNA-seq data with scVelo.
npx skills add https://github.com/felixboehm/biochem-allergy --skill scvelo-felixboehm
Please help me install this Agent Skill. Skill: scvelo Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/scvelo Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill scvelo-felixboehm
This Skill enables the analysis of cell differentiation and state transitions in single-cell RNA sequencing data by inferring the directionality of gene expression changes over time, even from static snapshots.
Run the scvelo skill to perform RNA velocity analysis on your AnnData object, visualizing the results on a UMAP embedding.
High-intent search queries and answers about installing and using this skill.
To infer trajectory directions from single-cell RNA-seq data, you can use RNA velocity to estimate cell state transitions from spliced and unspliced mRNA dynamics. This Skill applies the scVelo package to compute these trajectories directly on an AnnData object, predicting future cell states and developmental paths.
RNA velocity estimates cell state transitions by analyzing spliced and unspliced mRNA dynamics from static sequencing snapshots. In trajectory inference, it pinpoints driver genes that orchestrate cell fate decisions by identifying the genes exhibiting the most significant expression changes along the inferred developmental paths.
Yes, scVelo works with Scanpy for single-cell trajectory analysis by complementing it with RNA velocity capabilities. This Skill requires an existing AnnData object and utilizes Scanpy alongside numpy and matplotlib to estimate latent time and visualize cell state transitions on a UMAP embedding.
Yes, you can use a dynamical model instead of a stochastic model for RNA velocity analysis. This Skill supports both stochastic and dynamical models within the scVelo package, allowing you to estimate cell state transitions and compute latent time based on your specific single-cell dataset requirements.
To estimate latent time in single-cell trajectory inference, this Skill applies scVelo to quantify the progression of cells along inferred developmental trajectories. By analyzing spliced and unspliced mRNA dynamics, it calculates a latent time vector that represents the temporal ordering of cell states.
JosephWoodallInfers cell state transitions from mRNA dynamics using scvelo and Scanpy.
JosephWoodallInfer gene regulatory networks from RNA-seq data with GRNBoost2 and GENIE3.
ya-wayCompute spatial statistics for spatial transcriptomics data with Squidpy and Scanpy.
TianGzlabInfer single-cell trajectories and pseudotime from AnnData or Loom files.
