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scanpy

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Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata.

scientific-skills/Data Analysis/scanpy/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

# Scanpy: Single-Cell Analysis

## When to Use

- Use this skill when you need standard single-cell rna-seq analysis pipeline. for quality control (qc), normalization, dimensionality reduction (pca/umap/t-sne), clustering, differential expression analysis, and visualization. best suited for exploratory single-cell transcriptomics analysis using established workflows. for deep learning models, use scvi-tools; for data format issues, use anndata in a reproducible workflow.
- Use this skill when a data analytics task needs a packaged method instead of ad-hoc freeform output.
- Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
- Use this skill when `scripts/qc_analysis.py` is the most direct path to complete the request.
- Use this skill when you need the `scanpy` package behavior rather than a generic answer.

## Key Features

- Scope-focused workflow aligned to: Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata.
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