Scanpy
技能 已验证 活跃Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
To enable AI agents to perform standard and exploratory single-cell RNA-seq analysis workflows with established tools and best practices.
功能
- Perform quality control on scRNA-seq data
- Normalize and scale expression data
- Dimensionality reduction (PCA, UMAP, t-SNE)
- Cluster cells and identify marker genes
- Visualize results for interpretation and publication
- Automated QC analysis script
使用场景
- Exploratory analysis of single-cell RNA-seq datasets
- Identifying cell populations and cell types
- Generating publication-quality figures for scRNA-seq experiments
- Automating standard QC and preprocessing steps
非目标
- Deep learning-based models for scRNA-seq analysis (use scvi-tools)
- Data format questions (use anndata)
- Advanced trajectory inference beyond PAGA/DPT
工作流
- Load data
- Perform quality control (calculate metrics, filter cells/genes)
- Normalize and log-transform data
- Select highly variable genes
- Scale data and regress out unwanted variation
- Perform dimensionality reduction (PCA, neighbors, UMAP)
- Cluster cells (Leiden)
- Identify marker genes
- Annotate cell types
- Save processed data and results
实践
- Single-cell RNA-seq analysis
- Data preprocessing and QC
- Dimensionality reduction
- Clustering
- Marker gene identification
- Visualization
先决条件
- Python 3.11+
- uv package manager
- Scanpy library
- AnnData object or compatible input file
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
已验证类似扩展
PyDESeq2
100Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
AnnData
99Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Alterlab Scanpy
96Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata. Part of the AlterLab Academic Skills suite.
Pysam
99Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
Polars Bio
99High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.
Gtars
99High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.