AN
数据与复现
AnnData
AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observ…
K-Dense-AI/claude-scientific-skills查看
数据与复现
SC
scvelo
维护者 Kuan-lin Huang · 最近更新 2026年4月1日
scVelo is the leading Python package for RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling the kinetics of mRNA splicing — using the ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether a gene is being upregulated or downregulated in each cell. This allows reconstruction of developmental trajectories and identification of cell fate decis….
原始来源
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/scvelo
- 维护者
- Kuan-lin Huang
- 许可
- BSD-3-Clause
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Documentation：https：//scvelo.readthedocs.io/。
- GitHub：https：//github.com/theislab/scvelo。
- Paper：Bergen et al. (2020) Nature Biotechnology. PMID：32747759。
- scVelo是一个leading Python package ，用于 RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling kinetics of mRNA splicing — ，使用 ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether gene is being upregulated 或 downregulated in each cell. This allows reconstruction of developmental trajectories 、 identification of cell fate decisions without requiring time-course data。
- Installation：pip install scvelo。
原始文档
SKILL.md 摘录
When to Use This Skill
Use scVelo when:
- Trajectory inference from snapshot data: Determine which direction cells are differentiating
- Cell fate prediction: Identify progenitor cells and their downstream fates
- Driver gene identification: Find genes whose dynamics best explain observed trajectories
- Developmental biology: Model hematopoiesis, neurogenesis, epithelial-to-mesenchymal transitions
- Latent time estimation: Order cells along a pseudotime derived from splicing dynamics
- Complement to Scanpy: Add directional information to UMAP embeddings
Prerequisites
scVelo requires count matrices for both unspliced and spliced RNA. These are generated by:
- STARsolo or kallisto|bustools with
lamannomode - velocyto CLI:
velocyto run10x/velocyto run - alevin-fry / simpleaf with spliced/unspliced output
Data is stored in an AnnData object with layers["spliced"] and layers["unspliced"].
1. Setup and Data Loading
import scvelo as scv
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
适用场景
- **Trajectory inference ，面向 snapshot data**：Determine which direction cells are differentiating。
- **Cell fate prediction**：Identify progenitor cells 、 their downstream fates。
- **Driver gene identification**：Find genes whose dynamics best explain observed trajectories。
- **Developmental biology**：Model hematopoiesis，neurogenesis，epithelial-to-mesenchymal transitions。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
相关技能
相关技能
AR
数据与复现
Arboreto
Arboreto is a computational library for inferring gene regulatory networks (GRNs) from gene expression data using parall…
K-Dense-AI/claude-scientific-skills查看
Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for n…
FreedomIntelligence/OpenClaw-Medical-Skills查看
BI
Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep…
FreedomIntelligence/OpenClaw-Medical-Skills查看