Skip to main content

scllm is a Python package for single-cell analysis using LLMs.

Project description

scllm

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

PyPI version Documentation License: MIT

Overview: LangChain 🤝 scanpy

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, factor analalysis and differential expression analysis 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 os

# Enter your API key for ChatGPT
os.environ["OPENAI_API_KEY"] = "Enter your API key here."

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(temperature=0.0, model="gpt-4o-mini")

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

# 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:

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 - harald.voeh@gmail.com

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scllm-0.1.3.tar.gz (2.4 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

scllm-0.1.3-py3-none-any.whl (12.7 kB view details)

Uploaded Python 3

File details

Details for the file scllm-0.1.3.tar.gz.

File metadata

  • Download URL: scllm-0.1.3.tar.gz
  • Upload date:
  • Size: 2.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.6.4

File hashes

Hashes for scllm-0.1.3.tar.gz
Algorithm Hash digest
SHA256 92928a92437890459105a894259765020931d518b991c53b9f4476a9302bc755
MD5 674851bed51f27457a1a9cbfd03a7d6c
BLAKE2b-256 54ed224a80ea273f474155a15ffb369d10dfddbf924d6c125e65d57e3f8a2479

See more details on using hashes here.

File details

Details for the file scllm-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: scllm-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 12.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.6.4

File hashes

Hashes for scllm-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 1ddc6221b28406fa775074eae7d81ab33a7b127aba43a393d6a1b510f5bc7715
MD5 5c55133263238adc25574139287c534a
BLAKE2b-256 7307d93880a8abf11ca57158c1c7f6e3386b8a7bc2444a33604bb347505eb7e9

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page