A comprehensive analysis engine for processing pooled CRISPR screening data to identify essential genes and hit candidates through statistical validation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install crispr-screen-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install crispr-screen-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is CRISPR Screen Analyzer?
CRISPR Screen Analyzer is a specialized tool within the Openclaw Skills library designed for functional genomics. It enables researchers and bioinformaticians to process complex sgRNA count matrices to identify genetic dependencies, drug resistance markers, and synthetic lethal interactions. By leveraging Robust Rank Aggregation (RRA), the skill provides a statistically rigorous framework for calling hits from viability or drug-selection screens.
The skill emphasizes data integrity by calculating critical quality control metrics such as the Gini index and dropout rates. This ensures that only high-quality library representations move forward to the statistical analysis phase. Whether you are running genome-wide viability screens or focused drug sensitivity studies, this skill automates the transition from raw counts to actionable biological insights.
CRISPR Screen Analyzer Use Cases
- Identifying essential genes required for cell survival in genome-wide viability screens.
- Discovering gene knockouts that confer resistance to specific drug treatments.
- Conducting drug sensitivity screens to find synthetic lethal interactions for combination therapies.
- Evaluating the quality of CRISPR libraries through Gini index and read depth assessment.
- Comparing genetic dependencies across multiple treatment conditions or different cell lineages.
How CRISPR Screen Analyzer Works
- The skill ingests an sgRNA count matrix and a corresponding samplesheet defining experimental conditions.
- It performs initial Quality Control by calculating library evenness (Gini index) and identifying sgRNA dropout rates.
- Log Fold Changes (LFC) are calculated between defined control and treatment groups using pseudocounts to maintain statistical stability.
- Robust Rank Aggregation (RRA) is applied, involving Z-score calculation and P-value generation via a two-tailed normal test.
- Multiple testing correction is performed using the Benjamini-Hochberg procedure to generate False Discovery Rate (FDR) values.
- Significant hits are identified and categorized based on user-defined FDR and LFC thresholds.
- Results are exported as structured CSV files for both individual sgRNA statistics and aggregated gene-level summaries.
CRISPR Screen Analyzer Setup
To use this component of Openclaw Skills, ensure you have Python 3.7+ and the necessary dependencies installed:
pip install numpy pandas scipy
You can then run the analyzer directly from your terminal:
# Basic analysis with default parameters
python scripts/main.py --counts sgrna_counts.txt --samples samplesheet.csv
# Advanced analysis with specific conditions and FDR control
python scripts/main.py --counts counts.txt --samples samples.csv --control "Ctrl1,Ctrl2" --treatment "Treat1,Treat2" --fdr 0.01
CRISPR Screen Analyzer Data Schema & Taxonomy
The skill processes and generates data according to the following structures:
Input Requirements
- Count Matrix: A tab or comma-delimited file where rows are sgRNAs and columns are sample names.
- Samplesheet: A mapping file linking sample names to experimental groups (Control vs Treatment).
Output Files
| File Name | Description | Key Columns |
|---|---|---|
sgrna_results.csv |
Full statistical output for every sgRNA | sgrna, lfc, pvalue, fdr |
hits.csv |
Filtered list of significant candidates | Gene, mean_lfc, fdr |
qc_report.txt |
Summary of library quality metrics | Gini_index, Total_reads |
CRISPR Screen Analyzer Advanced Features
- Support for multi-replicate and multi-condition comparison within a single execution workflow.
- Integrated gene-level aggregation logic requiring multiple significant sgRNAs to reduce false positives.
- Customizable statistical thresholds (FDR and LFC) to balance sensitivity and specificity.
- Automated QC status flagging (e.g., warning if Gini index > 0.4).
- Extensible Python API for integration into larger bioinformatics pipelines.
SKILL.md
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