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OmicVerse.Skills

"我又不是不会分析,就是不知道用哪个函数,参数怎么填,跑完怎么检查,数据有问题怎么办……"

License: MIT Python 3.8+ PyPI Claude Code Skills


单细胞分析跑到一半,不知道 batch correction 该用 Harmony 还是 scVI?
SCENIC 跑完 regulon 是空的,不知道是数据库没下载还是参数写错了?
bulk DEG 做完想接 GSEA,却不知道 geneset 格式从哪里来?
spatial 数据想做 deconvolution,三个方法不知道怎么选、怎么装?

把每一个分析流程固化成可调用的 Skill,让 AI Agent 真正接管你的组学分析!


安装后配合 ov.Agent 或 Claude Code 即可使用
43 个 Skill 覆盖单细胞、bulk RNA-seq、空间转录组、数据处理全流程
每个 Skill 包含完整工作流、API 细节、防御性校验和真实报错处理

Skill 目录 · 安装 · 使用 · 贡献


Skill 目录

单细胞分析

Skill 功能描述
single-cell-preprocessing QC、归一化、HVG、PCA、UMAP 完整预处理流程
single-cell-clustering-backends Leiden、Louvain、scICE、GMM 聚类后端选择
single-cell-annotation SCSA、MetaTiME、CellVote、GPTAnno、KNN 标注
single-cell-batch-integration Harmony、scVI、BBKNN、Combat 批次校正
single-cell-scenic RegDiffusion GRN、cisTarget regulon、AUCell 评分
single-cell-trajectory-inference PAGA、Palantir、VIA 轨迹推断
single-cell-rna-velocity scVelo、dynamo、latentvelo、graphvelo RNA 速率
single-cell-sctour-trajectory scTour 连续时间轨迹建模
single-cell-cellphonedb-communication CellPhoneDB v5 配体-受体通讯分析
single-cell-differential-expression Wilcoxon、t-test、memento-DE 单细胞差异表达
single-cell-differential-abundance scCODA、Milopy、Milo 细胞组成差异分析
single-cell-cytotrace2 CytoTRACE2 发育潜能预测
single-cell-cnmf-program-discovery cNMF 基因表达程序发现
single-cell-lda-topic-clustering LDA 主题模型聚类
single-cell-kb-alignment kallisto/bustools 单细胞比对定量
single-cellfate-analysis CellFateGenie 伪时间基因发现
single-downstream-analysis AUCell、scDrug、NOCD 下游分析
single-multiomics MOFA、GLUE、SIMBA 多组学整合
single-popv-annotation PopV 10 算法共识细胞类型标注
single-to-spatial-mapping Single2Spatial scRNA 到空间转录组映射
cross-modal-celltype-transfer 跨模态细胞类型迁移
reference-label-transfer 参考图谱标签迁移

Bulk RNA-seq

Skill 功能描述
bulk-deg-analysis 基因 ID 映射、DESeq2 归一化、差异表达、火山图
bulk-deseq2-analysis PyDESeq2 差异表达、fold-change 筛选、GSEA
bulk-combat-correction pyComBat 批次效应校正
bulk-wgcna-analysis WGCNA 共表达网络、模块检测、hub 基因
bulk-stringdb-ppi STRING PPI 网络构建与可视化
bulk-to-single-deconvolution Bulk2Single beta-VAE 解卷积
bulk-trajblend-interpolation BulkTrajBlend 轨迹插值
gsea-enrichment GSEA 富集分析、基因集格式处理

空间转录组

Skill 功能描述
spatial-tutorials Visium/HD、Stereo-seq 预处理、Tangram、Starfysh 解卷积

数据处理与可视化

Skill 功能描述
data-io-loading h5ad、10x、Visium 数据读写
data-transform pandas/numpy 数据清洗与变换
data-stats-analysis scipy、statsmodels 统计检验
data-viz-plots matplotlib/seaborn 发表级图表
data-export-excel Excel 报告导出
data-export-pdf PDF 报告生成
datasets-loading OmicVerse 内置数据集加载
plotting-visualization 火山图、Venn、嵌入图、热图等
biocontext-knowledge UniProt、STRING、GO、PubMed 基因注释查询
bulk-fastq-quantification Bulk RNA-seq FASTQ→counts:SRA 下载、fastp、STAR+featureCounts 或 kb-python technology='BULK',并衔接 ov.bulk.pyDEG
single-cell-foundation-model scGPT、Geneformer、scFoundation、UCE、CellPLM —— 由 ov.llm.SCLLMManager 统一驱动
tcga-preprocessing TCGA bulk RNA-seq 预处理与生存分析

安装

pip install omicverse-skills

配合 OmicVerse Agent 使用时会自动加载;也可手动克隆到 .claude/skills/:

git clone https://github.com/Starlitnightly/omicverse-skills .claude/skills/omicverse-skills

使用

Python API

from omicverse_skills import skill_root, list_skills, load_skill_text

# 列出所有 skill
for skill in list_skills():
    print(skill["slug"], "—", skill["description"])

# 加载指定 skill 内容
text = load_skill_text("single-cell-preprocessing")
print(text)

配合 ov.Agent

import omicverse as ov

agent = ov.Agent()
agent.run("对这个 AnnData 做单细胞预处理,然后跑 Leiden 聚类")

Agent 会自动通过 skill_lookup 匹配并加载对应 Skill 的执行指引。


Skill 结构

每个 Skill 目录包含:

single-cell-preprocessing/
├── SKILL.md          # 执行指引(工作流、API、校验、分支选择)
└── references/       # 深度参考(函数签名、notebook 映射、兼容性说明)

SKILL.md 供 Agent 匹配与加载;references/ 供 Agent 运行时按需读取。


贡献

欢迎提交新 Skill 或改进现有 Skill:

  1. 在 src/omicverse_skills/skills/ 下新建目录
  2. 按照现有格式编写 SKILL.md(需含 name、slug、description frontmatter)
  3. 提交 PR

Star History

Star History Chart

MIT License © Starlitnightly

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