Scanpy
Skill Verifiziert AktivStandard 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.
Funktionen
- 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
Anwendungsfälle
- 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
Nicht-Ziele
- Deep learning-based models for scRNA-seq analysis (use scvi-tools)
- Data format questions (use anndata)
- Advanced trajectory inference beyond PAGA/DPT
Workflow
- 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
Praktiken
- Single-cell RNA-seq analysis
- Data preprocessing and QC
- Dimensionality reduction
- Clustering
- Marker gene identification
- Visualization
Voraussetzungen
- Python 3.11+
- uv package manager
- Scanpy library
- AnnData object or compatible input file
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
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.