scallops 1.0.0

Comprehensive package for analysis of Optical Pooled Screens (OPS) for biological data

SCALLOPS

Description

SCALLOPS (Scalable Library for Optical Pooled Screens) is a comprehensive Python package designed to streamline and scale the analysis of Optical Pooled Screens (OPS) for biological data. With a focus on handling large-scale, high-throughput screening data, SCALLOPS provides tools for efficiently processing, analyzing, and interpreting OPS data, leveraging modern distributed computing frameworks like Dask.

Installation

Option 1: Install from PyPI (Recommended)

For most users, the easiest way to install SCALLOPS is via pip. This will install the pre-compiled binary wheels for your operating system (Linux, Windows, or macOS).

pip install scallops

Note: SCALLOPS requires Python 3.11 or newer.

Option 2: Install from Source (For Development)

If you wish to contribute to the codebase or need the latest unreleased changes:

1. Clone the repository and change to the scallops directory:

git clone https://github.com/Genentech/scallops.git
cd scallops

2. Install SCALLOPS in editable mode with dependencies:

pip install -r requirements.txt -e .

Main Focus Areas:

• High-Throughput Data Processing: SCALLOPS is built to manage massive datasets typical of OPS experiments, allowing users to efficiently process and analyze data across multiple scales.
• Scalability and Performance: The package is optimized for both local and cloud-based distributed environments, making it ideal for scaling to large datasets without compromising performance.

Modular Workflows:

SCALLOPS provides an end-to-end WDL workflow that can be customized, allowing users to tailor analyses to their specific experimental needs.

Key Features:

• Efficient Data Handling: SCALLOPS utilizes advanced memory management and lazy evaluation techniques, which minimize resource usage while handling large datasets.
• Command-Line Interface (CLI): Automates batch processing and simplifies integration into larger pipelines.
• Customizable Outputs: The package generates versatile outputs, including data visualizations and summary statistics, which can be integrated into downstream analyses.
• Notebook Examples: SCALLOPS includes practical Jupyter notebooks that walk users through typical workflows, making it easy to get started with real-world datasets.
• Custom Features: Advanced users can extend SCALLOPS with their own custom functions and workflows, ensuring the package can grow with the complexity of the data.
• Comprehensive API: SCALLOPS provides a rich API that exposes all the package functionalities, allowing users to integrate it directly into their own Python scripts and workflows. This makes SCALLOPS highly adaptable, enabling users to build fully customized data pipelines and analyses tailored to their unique experimental needs.

Typical Use Cases:

• Large-Scale Screening Projects: SCALLOPS is designed for handling the immense data loads of genome-wide OPS projects, helping users efficiently identify and quantify biological perturbations.
• Data-Driven Insights: SCALLOPS facilitates the discovery of patterns and trends in OPS data, helping users extract and interpret complex biological systems' data.

Contributing to SCALLOPS

We welcome all forms of contributions, including bug reports, documentation improvements, and feature enhancements.