Python reimplementation of RNAither adapted for high-throughput CRISPR screening analysis
Project description
pyRNAither
Python reimplementation of RNAither adapted for high-throughput CRISPR screening analysis
📖 Overview
pyRNAither is a powerful Python package for analyzing high-throughput CRISPR screening data. It provides a comprehensive suite of tools for quality control, normalization, statistical analysis, and visualization of CRISPR screening results.
✨ Features
- Data Processing: Efficient handling of large-scale CRISPR screening data
- Quality Control: Comprehensive QC metrics and visualization
- Normalization: Multiple normalization methods for CRISPR data
- Statistical Analysis: Advanced statistical tests for hit identification
- Visualization: Publication-quality plots and interactive visualizations
- Modular Design: Easy to extend and customize for specific needs
🚀 Installation
Using pip
pip install pyRNAither
From source
# Clone the repository
git clone https://github.com/Takshan/pyRNAither.git
cd pyRNAither
# Install in development mode
pip install -e .
📚 Documentation
For detailed documentation, including API reference and examples, please visit our documentation.
🎯 Quick Start
We are currently wokring on a quick start guide consisting of example analysis pipelines and a step-by-step guide to get started with pyRNAither. In the meantime, please refer to the documentation.
Basic Usage
import pyrnaither as prt
#load data
Example Analysis Pipeline
import pyrnaither as prt
from pyrnaither import stats, visualization
# Load and preprocess data
📊 Example Plots
Quality Control Metrics
Volcano Plot
Heatmap
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
📧 Contact
For questions or support, please contact:
- Rahul Brahma: rahul.brahma[at]uni-greifswald[dot]de
- Yasas Wijesekara: yasas.wijesekara[at]uni-greifswald[dot]de
📚 References
- Rieber N, Knapp B, Eils R, Kaderali L. RNAither, an automated pipeline for the statistical analysis of high-throughput RNAi screens. Bioinformatics. 2009 Mar 1;25(5):678-9. doi: 10.1093/bioinformatics/btp014. Epub 2009 Jan 25. PMID: 19168909.
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