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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-1b |
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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-Scale-1B pretrained model, based on the Pythia-1b architecture |
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developed by EleutherAI, fine-tuned using the Cell2Sentence (C2S) framework on a wide array of single-cell RNA sequencing |
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(scRNA-seq) datasets from CellxGene and the Human Cell Atlas. Cell2Sentence is a cutting-edge method that |
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adapts large language models (LLMs) to single-cell biology by converting scRNA-seq data into |
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"cell sentences" — ordered sequences of gene names based on expression levels. This model has been trained |
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to perform a broad range of single- and multi-cell tasks, making it a versatile tool for various single-cell |
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and multi-cell analyses. |
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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 a variable number of genes per cell sentence, with a maximum context length of 8192 tokens. |
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The context length of the default Pythia model was extended using rotary positional embeddings prior to C2S training. |
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- Cells: For multi cell samples, each training sample contained between 5 and 20 cells, with the same number of genes for each of the cells in the same sample. |
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- Genes: For single cell samples, each cell sentence contained between 100 and 2048 genes. For multi cell samples, each cell sentence per cell contained between 100 and 400 genes. |
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# Tasks |
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This model is designed for the following tasks: |
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Single-Cell Tasks |
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- Unconditional single-cell generation: Generate single cell sentences unconditionally. |
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- Cell type prediction: Predict the cell type of a given single cell. |
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- Cell type-conditioned generation: Generate a single cell sentence conditioned on a specific cell type. |
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Multi-Cell Tasks |
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- Unconditional multi-cell generation: Generate multiple cell sentences unconditionally. |
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- Tissue prediction: Predict the tissue of origin for a group of cells. |
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- Cell type prediction: Predict the cell type for each cell in a group of multiple cells. |
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- Tissue-conditioned multi-cell generation: Generate multiple cell sentences conditioned on a specific tissue. |
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- Cell type-conditioned multi-cell generation: Generate multiple cell sentences conditioned on the cell type of each individual cell. |
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- Multi-cells to abstract: Generate a research paper abstract based on the provided multi-cell sentences. |
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- Abstract to multi-cells: Generate multiple cell sentences based on a given research paper abstract. |
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Gene Set Tasks |
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- Gene set name to genes: Generate an alphabetical list of genes given a gene set name. |
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- Genes to gene set name: Generate the name of a gene set given an alphabetical list of genes. |
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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 |
