File size: 1,795 Bytes
9e19165 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # CDT2-data
Data files for [Central Dogma Transformer II (CDT-II)](https://github.com/nobusama/CDT2).
## Files
| File | Description | Size |
|------|-------------|------|
| `morris_celllevel_effects_2361.h5` | Cell-level perturbation effects (TSS, 2,361 genes) | 41 MB |
| `morris_snp_celllevel_effects_2361.h5` | Cell-level perturbation effects (SNP, 2,361 genes) | 34 MB |
| `k562_gene_embeddings_aligned.h5` | Gene embeddings from scGPT (2,360 genes) | 4.4 MB |
| `cdt_morris_celllevel_best.pt` | Trained CDT-II model weights | 80 MB |
## Enformer Embeddings (Not Included)
The Enformer embeddings must be generated using the notebooks in the CDT2 repository:
- `morris_28genes_enformer.h5` - Generate with `notebooks/embeddings/Morris_28genes_Enformer.ipynb`
- `morris_snp_enformer.h5` - Generate with `notebooks/embeddings/Morris_SNP_Enformer.ipynb`
These notebooks run Enformer inference on Google Colab and save the embeddings to Google Drive.
## Usage
```python
from huggingface_hub import hf_hub_download
# Download cell-level effects
effects_path = hf_hub_download(
repo_id="nobusama17/CDT2-data",
filename="morris_celllevel_effects_2361.h5",
repo_type="dataset"
)
# Download model weights
model_path = hf_hub_download(
repo_id="nobusama17/CDT2-data",
filename="cdt_morris_celllevel_best.pt",
repo_type="dataset"
)
```
## Data Source
- Morris STING-seq v2 dataset (60,505 K562 cells, 447 perturbation loci)
- Reference: [Morris et al. Science 2023](https://www.science.org/doi/10.1126/science.adh7699)
## Citation
```bibtex
@article{ota2025cdtii,
title={Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms},
author={Ota, Nobuyuki},
journal={bioRxiv},
year={2025}
}
```
## License
MIT License
|