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--- |
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license: cc-by-nc-nd-4.0 |
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language: |
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- en |
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base_model: EleutherAI/pythia-410m |
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library_name: transformers |
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tags: |
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- biology |
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- scRNAseq |
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--- |
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# Overview |
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This is the C2S-Pythia-410m-cell-type-conditioned-cell-generation model, built on the Pythia-410m architecture developed |
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by EleutherAI, fine-tuned using Cell2Sentence (C2S) on a comprehensive collection of single-cell RNA sequencing |
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(scRNA-seq) datasets from CellxGene and the Human Cell Atlas. Cell2Sentence is a pioneering technique that adapts |
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large language models (LLMs) to single-cell biology by converting scRNA-seq data into "cell sentences" — ordered |
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sequences of gene names based on expression levels. This model is specifically trained for cell type-conditioned |
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single-cell generation, enabling the generation of realistic single-cell profiles conditioned on specified cell |
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types. |
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# Training Data |
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This model was trained on over 57 million human and mouse cells gathered from over 800 single-cell RNA sequencing |
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datasets from CellxGene and the Human Cell Atlas. This dataset covers a broad range of cell types and conditions |
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from multiple tissues in both human and mouse. |
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This model was trained with the top 200 genes per cell sentence. |
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# Tasks |
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This model is designed for: |
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- Cell type-conditioned single-cell generation: Generating single-cell profiles conditioned on specific cell types, allowing for the creation of synthetic cells that reflect the gene expression patterns of targeted cell types. |
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# Cell2Sentence Links |
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- GitHub: https://github.com/vandijklab/cell2sentence |
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- Paper: https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3 |
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# Pythia Links |
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- Paper: https://arxiv.org/pdf/2304.01373 |
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- Hugging Face: https://huggingface.co/EleutherAI/pythia-410m |
