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 majiayu000/claude-skill-registry --skill 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.
# Proteomics Quality Control ## 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'] = intensity_matrix.notna().sum() metrics['median_intensity'] = intensity_matrix.median() metrics['mean_intensity'] = intensity_matrix.mean() metrics['cv'] = intensity_matrix.std() / intensity_matrix.mean() metrics['missing_pct'] = 100 * intensity_matrix.isna().sum() / len(intensity_matrix) return metrics qc = sample_qc_metrics(log2_intensities) print(qc) ``` ## Replicate Correlation ```python import seaborn as sns import matplotlib.pyplot as plt from scipy.stats import pearsonr def replicate_correlation(intensity_matrix, sample_groups): '''Calculate within-group correlations''' corr_matrix = intensity_matrix.corr(method='pearson') # Mask for within-group comparisons results = [] for group in sample_groups.unique(): group_samples = sample_groups[sample_groups == group].index for i, s1 in enumerate(group_samples): for s2 in group_samples[i+1:]: r = corr_matrix.loc[s1, s2] results.append({'group': group, 'sample1': s1, 'sample2':
- 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 majiayu000/claude-skill-registry --skill 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 majiayu000/claude-skill-registry, a repository with 534 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.