| | --- |
| | 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> |
