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--- |
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license: cc-by-nc-nd-4.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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- split: val |
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path: data/val-* |
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dataset_info: |
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features: |
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- name: input_ids |
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dtype: string |
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- name: cell_type |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2314316937 |
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num_examples: 218732 |
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- name: test |
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num_bytes: 288846799 |
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num_examples: 27388 |
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- name: val |
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num_bytes: 289505418 |
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num_examples: 27382 |
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download_size: 2322876358 |
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dataset_size: 2892669154 |
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task_categories: |
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- text-generation |
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- question-answering |
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language: |
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- en |
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tags: |
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- biology |
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- pytorch |
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- causal-lm |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Overview |
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Cell2Sentence is a novel method for adapting large language models to single-cell transcriptomics. |
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We transform single-cell RNA sequencing data into sequences of gene names ordered by expression level, termed "cell sentences". |
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This dataset was constructed from the immune tissue dataset in [Domínguez et al.](https://www.science.org/doi/10.1126/science.abl5197), |
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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. |
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Details about the Cell2Sentence transformation and preprocessing pipeline can be found in our paper and GitHub repo linked below. |
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GitHub: <https://github.com/vandijklab/cell2sentence-ft> |
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Paper: <https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3> |
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Model Card: <https://huggingface.co/vandijklab/pythia-160m-c2s> |
