Upload scelmo-celllines-gpt-3.5 - scELMo cell line embeddings generated using GPT-3.5-turbo

README.md

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+# scELMo Cellline Embeddings
+
+This directory contains cellline embeddings generated using scELMo methodology.
+
+## Source
+
+These embeddings are converted from the official scELMo repository:
+**https://github.com/HelloWorldLTY/scELMo**
+
+## Model Information
+
+- **Model**: gpt-3.5-turbo
+- **Embedding Dimension**: 1536
+- **Type**: cellline
+- **Aggregation Mode**: wa
+- **API Model**: text-embedding-ada-002
+
+## Files
+
+- `gene_embeddings.pkl`: Gene embeddings dictionary in PerturbLab format
+  - Format: `{'embeddings': {gene_name: embedding_array}, 'gene_list': [gene_names]}`
+- `config.json`: Model configuration
+
+## Usage
+
+```python
+from perturblab.model.scelmo import scELMoModel
+
+# Load model
+model = scELMoModel.from_pretrained('scelmo-celllines-gpt-3.5')
+
+# Use embeddings
+embeddings = model.predict_embeddings(adata, aggregation_mode='wa')
+```
+
+## Citation
+
+If you use these embeddings, please cite the original scELMo paper:
+
+```bibtex
+@article{liu2023scelmo,
+  title={scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis},
+  author={Liu, Tianyu and Chen, Tianqi and Zheng, Wangjie and Luo, Xiao and Zhao, Hongyu},
+  journal={Cell Patterns (in press)},
+  pages={2023--12},
+  year={2025},
+  publisher={Cell Press}
+}
+```

config.json

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+{
+  "model_series": "scelmo",
+  "model_name": "gpt-3.5-turbo",
+  "model_type": "embedding_extractor",
+  "embedding_dim": 1536,
+  "aggregation_mode": "wa",
+  "api_model": "text-embedding-ada-002",
+  "source_type": "cellline",
+  "description": "Cell line embeddings generated using GPT-3.5-turbo"
+}

gene_embeddings.pkl

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+size 24599857