Single Cell Rna Qc
Skill ActivePerforms quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
To automate the quality control of single-cell RNA-seq data, ensuring data integrity and readiness for downstream analysis by applying scverse best practices.
Features
- Automated QC pipeline for single-cell RNA-seq
- Supports .h5ad and .h5 file formats
- MAD-based filtering and comprehensive visualizations
- Modular functions for custom workflows
- Follows scverse/scanpy best practices
Use Cases
- Performing QC analysis on single-cell RNA-seq data
- Filtering low-quality cells based on various metrics
- Assessing overall data quality through visualizations
- Following scverse/scanpy best practices for single-cell analysis
Non-Goals
- Performing downstream analysis like normalization or clustering
- Ambient RNA correction or doublet detection (these are mentioned as next steps)
- Batch correction or cell cycle scoring
Maintenance
- warning:Dependency ManagementThe script relies on common Python libraries (anndata, scanpy, etc.) but there are no explicit dependency pinning or vulnerability checks mentioned.
Trust
- warning:Issues Attention29 issues opened and 4 closed in the last 90 days indicate a slow response rate to open issues.
Execution
- warning:Pinned dependenciesWhile Python scripts have shebangs, dependencies like anndata and scanpy are not explicitly pinned in a lockfile, and side-effect headers are missing.
Installation
First, add the marketplace
/plugin marketplace add anthropics/knowledge-work-plugins/plugin install bio-research@knowledge-work-pluginsQuality Score
Trust Signals
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