Awesome

Cell2Sentence

NOTICE:

🎉 (02/15/2024) pythia-160m-c2s trained on full cell sentences is available on the Hugging Face hub here! This new model generates and predicts cell types from entire cells directly in text with the Pythia-160 base model.

🎉 (02/15/2024) Our updated preprint was posted on BioRxiv here. We introduce our latest results, including full cell sentence generation, combinatorial cell label prediction, abstract generation, and training on a large multi-tissue dataset of 36M cells.

Cell2Sentence is a novel method for adapting large language models to single-cell transcriptomics. We transform single-cell RNA sequencing data into sequences of gene names ordered by expression level, termed "cell sentences". This repository provides scripts and examples for converting cells to cell sentences, fine-tuning language models, and converting outputs back to expression values.

Requirements

Cell2Sentence requires Python 3.10+ and Conda. Create your python environment with conda (note: you need to install conda or miniconda):

conda env create -f environment.yml
conda develop .

Make sure to activate your conda environment with conda activate c2s.

Quickstart

To get started with some sample data:

1. Download a subset of 1000 cells from [1] to the data/ directory: python retrieve_example_data.py.
2. Transform raw transcript counts into cell sentences: python transform.py.

To transform your own data, place your .h5ad file in the data/ directory and run python transform.py --data_filepath data/<your_filepath> --output_dir <your_output_dir>. The --output_dir parameter lets you specify where to place the cell sentences.

The transform.py script creates three output directories:

• eval/ which contains figures and evaluation metrics.
• cell_sentences/ which contains txt files with raw cell sentences and gene vocabularies.
• cell_sentences_hf/ which contains cell sentences and types formatted as an arrow dataset.

[1] C Domínguez Conde et al. “Cross-tissue immune cell analysis reveals tissue-specific features in humans”. In: Science 376.6594 (2022), eabl5197.

Fine-tuning

Fine-tune a GPT-2 model with this script:

python train.py \
    --data_dir data/cell_sentences_hf/ \
    --output_dir <your_output_dir> \
    --model_name gpt2 \
    --num_train_epochs 10 \
    --gradient_accumulation_steps 4 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 16 \
    --fp16 True \
    --logging_steps 32 \
    --save_steps 500

By default, models are saved to the data/model/ directory. Edit the --data_dir value to point to your own data directory if needed.

Switch the model_name to the name of any other models you'd like to fine-tune. Note that you may need to adjust the per_device_batch_size, gradient_accumulation_steps, and gradient_checkpointing parameters if you employ larger models. The default configuration is provided for training on a single Nvidia A5000 GPU.

Citation

Please cite the cell2sentence paper if you use this repo.

@article {Levine2023.09.11.557287,
	author = {Daniel Levine and Syed Asad Rizvi and Sacha L{\'e}vy and Nazreen Pallikkavaliyaveetil MohammedSheriff and Ruiming Wu and Zihe Zhang and Antonio Fonseca and Xingyu Chen and Sina Ghadermarzi and Rahul M. Dhodapkar and David van Dijk},
	title = {Cell2Sentence: Teaching Large Language Models the Language of Biology},
	elocation-id = {2023.09.11.557287},
	year = {2023},
	doi = {10.1101/2023.09.11.557287},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/09/14/2023.09.11.557287},
	eprint = {https://www.biorxiv.org/content/early/2023/09/14/2023.09.11.557287.full.pdf},
	journal = {bioRxiv}
}

Maintainers

• Sacha Lévy (sacha.levy@yale.edu)
• Daniel Levine (daniel.levine@yale.edu)
• Syed Rizvi (syed.rizvi@yale.edu)