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 FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-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.
## Version Compatibility Reference examples tested with: DESeq2 1.42+, MAGeCK 0.5+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, scipy 1.12+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Batch Correction **"Correct batch effects in my CRISPR screens"** → Normalize and harmonize sgRNA count data across screen batches to remove systematic technical variation while preserving biological signal. - Python: `scipy`/`sklearn` for median normalization and batch correction - CLI: `mageck test` with batch-aware design ## Median Normalization **Goal:** Remove systematic library-size differences between batches. **Approach:** Scale each sample within a batch so that sample medians match a global median, correcting for sequencing depth variation. ```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 withi
- Version Compatibility
- 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 FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-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 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.