Single Cell Rna Qc
This skill automates RNA quality control for single-cell data, ensuring reliable analysis and accurate research results.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add anthropics-single-cell-rna-qc npx -- -y @trustedskills/anthropics-single-cell-rna-qc
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"anthropics-single-cell-rna-qc": {
"command": "npx",
"args": [
"-y",
"@trustedskills/anthropics-single-cell-rna-qc"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill performs quality control (QC) on single-cell RNA sequencing (scRNA-seq) data. It identifies and filters out low-quality cells and genes, ensuring robust downstream analysis. The tool helps researchers improve the accuracy and reliability of their scRNA-seq experiments by removing noise and artifacts.
When to use it
- Before performing differential gene expression analysis on scRNA-seq datasets.
- When dealing with scRNA-seq data exhibiting high dropout rates or technical variation.
- To prepare scRNA-seq data for clustering, trajectory inference, or other downstream analyses.
- For initial exploration and cleaning of newly generated scRNA-seq data.
Key capabilities
- Cell filtering based on quality metrics (e.g., number of detected genes).
- Gene filtering based on expression thresholds.
- Data normalization.
- Identification of outlier cells.
Example prompts
- "Perform QC on my scRNA-seq data and filter out cells with fewer than 500 expressed genes."
- “Normalize the gene counts in this single-cell dataset.”
- "Identify and remove outlier cells from this scRNA-seq experiment."
Tips & gotchas
The skill requires properly formatted scRNA-seq count matrices as input. Ensure your data is appropriately structured before using the tool to avoid errors.
Tags
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Security Audits
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🏢 Official
Published by the company or team that built the technology.