scllm 0.1.2

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scllm

A Python package for annotating single-cell RNA sequencing data using Large Language Models.

Overview

scllm leverages the power of Large Language Models to automatically annotate cell types in single-cell RNA sequencing data. It integrates seamlessly with scanpy and provides an intuitive interface for cell type annotation based on marker gene expression.

Installation

You can install scllm using pip:

pip install scllm

Or using uv:

uv pip install scllm

Quick Start

import scanpy as sc
import scllm

# Load your data
adata = sc.read_h5ad('your_data.h5ad')

# Perform clustering if not already done
sc.tl.leiden(adata)

# Initialize your LLM (example with OpenAI)
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()

# Annotate clusters
adata = scllm.annotate_cluster(llm, adata, cluster_key='leiden')

# Access annotations
print(adata.obs['leiden_annotated'])

Features

• Automatic cell type annotation using LLMs
• Seamless integration with scanpy
• Support for multiple LLM providers
• Interactive Jupyter notebook examples
• Customizable annotation parameters

Documentation

For detailed documentation and examples, visit our documentation page.

Check out our example notebooks:

• Cell Type Annotation

Requirements

• Python ≥ 3.10
• scanpy ≥ 1.11.0
• langchain ≥ 0.3.7
• And other dependencies listed in pyproject.toml

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use scllm in your research, please cite:

@software{vohringer2024scllm,
  author = {Vöhringer, Harald},
  title = {scllm: Single-Cell Annotation with Large Language Models},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/sagar87/scllm}
}

Contact

Harald Vöhringer - [e-mail]

Project Link: https://github.com/sagar87/scllm