File size: 1,555 Bytes
75563bb 50bedf1 dc1f050 75563bb dc1f050 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
---
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.](https://www.science.org/doi/10.1126/science.abl5197),
and it was used to train the [Pythia-160m model](https://huggingface.co/EleutherAI/pythia-160m) 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> |