bio-crispr-screens-batch-correction
Batch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.
npx skills add majiayu000/claude-skill-registry --skill batch-correction --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.
# Batch Correction ## Median Normalization ```python import numpy as np import pandas as pd from scipy import stats def median_normalize(counts_df, batch_column='batch'): '''Normalize counts to median within each batch.''' normalized = counts_df.copy() guide_columns = [c for c in counts_df.columns if c not in [batch_column, 'gene', 'guide']] for batch in counts_df[batch_column].unique(): batch_mask = counts_df[batch_column] == batch batch_data = counts_df.loc[batch_mask, guide_columns] sample_medians = batch_data.median(axis=0) global_median = sample_medians.median() scale_factors = global_median / sample_medians normalized.loc[batch_mask, guide_columns] = batch_data * scale_factors return normalized counts_df = pd.read_csv('screen_counts.csv') normalized = median_normalize(counts_df, 'batch') ``` ## Size Factor Normalization ```python def size_factor_normalize(counts_df, reference='geometric_mean'): '''DESeq2-style size factor normalization.''' guide_cols = [c for c in counts_df.columns if c.startswith('sample_')] counts = counts_df[guide_cols].values counts_nonzero = np.where(counts == 0, np.nan, counts) if reference == 'geometric_mean': log_counts = np.log(counts_nonzero) geomet
- Median Normalization
- Size Factor Normalization
- Quantile Normalization
- Control-Based Normalization
- Batch Effect Removal with ComBat
- Batch-Aware Log-Fold Change
- Replicate Correlation Check
- Batch QC Metrics
- Visualization
- Related Skills
What does the bio-crispr-screens-batch-correction skill do?
Batch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill batch-correction --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.