AN
Data & Repro
AnnData
AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observation metadata (obs)…
K-Dense-AI/claude-scientific-skillsView
Data & Repro
RN
rna-velocity-agent
Maintainer FreedomIntelligence · Last updated April 1, 2026
RNA velocity analysis with scVelo for trajectory and fate decision inference.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/rna-velocity-agent
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- The RNA Velocity Agent analyzes RNA velocity from single-cell RNA sequencing to predict cellular state transitions, differentiation trajectories, and dynamic transcriptional regulation. It implements velocyto, scVelo, and deep learning approaches for trajectory inference.
- When inferring cell fate decisions and differentiation trajectories from scRNA-seq.
- To identify driver genes of cellular transitions.
- For predicting future cell states from current transcriptional profiles.
- When analyzing developmental processes or disease progression dynamics.
Source Doc
Excerpt From SKILL.md
Core Capabilities
-
Splicing-Based Velocity: Calculate RNA velocity from spliced/unspliced transcript ratios.
-
Dynamic Modeling: Deep learning models (scVelo dynamical mode) for accurate velocity estimation.
-
Trajectory Inference: Project velocity vectors onto UMAP/PCA for differentiation flow visualization.
-
Driver Gene Identification: Identify genes driving cell state transitions.
-
Latent Time Estimation: Reconstruct cellular pseudotime from velocity fields.
-
Multi-Modal Velocity: Integrate protein (CITE-seq) or chromatin (ATAC) velocity.
Workflow
-
Input: scRNA-seq data with spliced/unspliced counts (from STARsolo, velocyto, kallisto-bustools).
-
Quality Control: Filter genes by splice detection rates and expression levels.
-
Velocity Computation: Calculate velocity using steady-state or dynamical models.
-
Embedding Projection: Project velocity onto low-dimensional representations.
-
Trajectory Analysis: Identify root cells, terminal states, and differentiation paths.
-
Driver Analysis: Rank genes by velocity-based contribution to transitions.
-
Output: Velocity vectors, trajectory plots, driver genes, latent time estimates.
Example Usage
User: "Analyze RNA velocity in this hematopoiesis scRNA-seq dataset to map differentiation trajectories."
Agent Action:
Use cases
- When inferring cell fate decisions and differentiation trajectories from scRNA-seq.
- To identify driver genes of cellular transitions.
- For predicting future cell states from current transcriptional profiles.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- Single_Cell - For general scRNA-seq analysis
- Single_Cell_Foundation_Models - For cell annotation
- Spatial_Transcriptomics - For spatial velocity
Related skills
Related skills
BI
Data & Repro
bio-imaging-mass-cytometry-cell-segmentation
Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cel…
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-read-qc-umi-processing
Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and ac…
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-single-cell-batch-integration
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving b…
FreedomIntelligence/OpenClaw-Medical-SkillsView