llm2geneset 0.0.4

llm2geneset

llm2geneset

llm2geneset

This project combines LLMs, gene set generation, and overrepresentation analysis to power analyis of RNA-seq, scRNA-seq, and proteomics data sets.

If you have an OpenAI API key, you can try out the web application at https://llm2geneset.streamlit.app.

Pre-print

A bioRxiv pre-print describing this work is available here:

Enhancing Gene Set Overrepresentation Analysis with Large Language Models

Jiqing Zhu, Rebecca Y. Wang, Xiaoting Wang, Ricardo Azevedo, Alex Moreno, Julia A. Kuhn, Zia Khan doi: https://doi.org/10.1101/2024.11.11.621189

OpenAI API Key Setup

Please read OpenAI's best practicies for API key safety and make sure you have your OpenAI API key setup as environment variables e.g.:

export OPENAI_API_ORG="org-XXXX"
export OPENAI_API_KEY="XXXXX"

Installation using pixi

An environment to run llm2geneset can be configured using pixi

Once pixi is installed, run pixi shell in the llm2geneset directory

cd llm2geneset
pixi shell

Installing as a pypi package

You can also install the package using pip as it is available on pypi https://pypi.org/project/llm2geneset

pip install llm2geneset

Usage

You can use the package in a script as follows after running pixi shell.

import openai
import llm2geneset
import asyncio

async def main():
    aclient = openai.AsyncClient()
    genes = await llm2geneset.get_genes(aclient, "Antigen presentation")
    print(','.join(genes['parsed_genes']))
    res = await llm2geneset.gs_proposal(
        aclient, genes, n_pathways=5,
        n_background=19846)
    res = await llm2geneset.gs_proposal(aclient, genes['parsed_genes'])
    print(res['ora_results'])

if __name__ == "__main__":
    asyncio.run(main())

# Output:
# HLA-A,HLA-B,HLA-C,HLA-DRA,HLA-DRB1,HLA-DRB3,HLA-DRB4,...
# set_descr  generatio   bgratio  richFactor  foldEnrich
# 1  Antigen processing and presentation via MHC cl...   0.500000  0.001209    0.625000  413.458333
# ...

See also notebooks/simple_example.ipynb for an example of how to use it within a jupyter notebook.

Webapp Interface

Streamlit is included in the llm2geneset environment. You can run the webapp interface as follows.

pixi shell
streamlit run webapp/app.py

pypi deployment

Bump version in setup.cfg and run the following according to the instructions for pyscaffold-markdown

pixi shell
tox -e docs  # to build documentation
tox -e build -- --wheel # to build the package distribution w/o source
tox -e publish  # to test project uploads correctly in test.pypi.org
tox -e publish -- --repository pypi  # release package to PyPI

Note

This project has been set up using PyScaffold 4.5. For details and usage information on PyScaffold see https://pyscaffold.org/.