README.md

metadata

license: cc-by-nc-nd-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: val
        path: data/val-*
dataset_info:
  features:
    - name: input_ids
      dtype: string
    - name: cell_type
      dtype: string
  splits:
    - name: train
      num_bytes: 2314316937
      num_examples: 218732
    - name: test
      num_bytes: 288846799
      num_examples: 27388
    - name: val
      num_bytes: 289505418
      num_examples: 27382
  download_size: 2322876358
  dataset_size: 2892669154
task_categories:
  - text-generation
  - question-answering
language:
  - en
tags:
  - biology
  - pytorch
  - causal-lm
size_categories:
  - 100K<n<1M

Overview

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 dataset was constructed from the immune tissue dataset in Domínguez et al., and it was used to train the Pythia-160m model capable of generating complete cells described in our paper. Details about the Cell2Sentence transformation and preprocessing pipeline can be found in our paper and GitHub repo linked below.

GitHub: https://github.com/vandijklab/cell2sentence-ft
Paper: https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3
Model Card: https://huggingface.co/vandijklab/pythia-160m-c2s