PyDESeq2
技能 已验证 活跃Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
To enable users to perform robust differential gene expression analysis on bulk RNA-seq data using a well-documented and executable Python script.
功能
- Differential gene expression analysis with PyDESeq2
- Handles bulk RNA-seq count data
- Supports single-factor and multi-factor designs
- Performs Wald tests and FDR correction
- Generates volcano and MA plots
- Saves results to CSV and pickle formats
使用场景
- Analyzing bulk RNA-seq data for differential gene expression
- Comparing gene expression between experimental conditions
- Accounting for batch effects or covariates in analysis
- Integrating differential expression analysis into Python pipelines
非目标
- Performing single-cell RNA-seq analysis
- Analyzing raw sequencing reads (FASTQ files)
- Single-gene statistical tests outside of a DESeq2 framework
- Advanced bioinformatics tasks like variant calling or genome assembly
工作流
- Load count matrix and metadata
- Filter low-count genes and samples
- Initialize DeseqDataSet with design formula
- Run DESeq2 pipeline for normalization and fitting
- Perform statistical testing with specified contrast
- Optionally apply LFC shrinkage
- Save results and intermediate objects
- Generate plots (optional)
实践
- RNA-seq data analysis
- Statistical modeling
- Differential expression analysis
先决条件
- Python 3.10+ (3.11+ recommended)
- uv package manager
- pandas
- pydeseq2
- matplotlib (for plots)
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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