bio-proteomics-proteomics-qc
Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-proteomics-proteomics-qc --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: ggplot2 3.5+, limma 3.58+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, scipy 1.12+, seaborn 0.13+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Proteomics Quality Control **"Check the quality of my proteomics data"** → Assess data quality through identification rates, missing value patterns, replicate correlation, intensity distributions, and batch effect detection before downstream analysis. - Python: `pandas` + `matplotlib`/`seaborn` for QC metrics and visualization - R: `limma::plotMDS()`, correlation heatmaps, CV distributions ## Sample Quality Metrics ```python import pandas as pd import numpy as np def sample_qc_metrics(intensity_matrix): '''Calculate per-sample QC metrics''' metrics = pd.DataFrame(index=intensity_matrix.columns) metrics['n_proteins'] = inten
- Version Compatibility
- Sample Quality Metrics
- Replicate Correlation
- Missing Value Patterns
- Batch Effect Detection with PCA
- R: QC with limma
- Coefficient of Variation
- Digestion Efficiency
- QC Report Summary
- Related Skills
What does the bio-proteomics-proteomics-qc skill do?
Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-proteomics-proteomics-qc --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.