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| Metadata-Version: 2.4 | |
| Name: arc-stack | |
| Version: 0.1.2 | |
| Summary: Stack is a single-cell foundation model that enables in-context learning at inference time. | |
| Author-email: Mingze Dong <mingze.dong@arcinstitute.org>, Yusuf Roohani <yusuf.roohani@arcinstitute.org> | |
| License: Attribution-NonCommercial-ShareAlike 4.0 International | |
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| Project-URL: Homepage, https://github.com/ArcInstitute/stack | |
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| Requires-Python: >=3.9 | |
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| Requires-Dist: h5py>=3.8 | |
| Requires-Dist: geomloss>=0.2.6 | |
| Requires-Dist: numpy>=1.22 | |
| Requires-Dist: pandas>=1.5 | |
| Requires-Dist: psutil>=5.9 | |
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| Requires-Dist: PyYAML>=6.0 | |
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| Dynamic: license-file | |
| # Stack: In-context learning of single-cell biology | |
| Stack is a large-scale encoder-decoder foundation model trained on 150 million uniformly-preprocessed single cells. It introduces a novel tabular attention architecture that enables both intra- and inter-cellular information flow, setting cell-by-gene matrix chunks as the basic input data unit. Through in-context learning, Stack offers substantial performance improvements in generalizing biological effects and enables generation of unseen cell profiles in novel contexts. | |
| ## Installation | |
| ### Using pip | |
| ```bash | |
| # Install from PyPI | |
| pip install arc-stack | |
| # Or install from source for development | |
| git clone https://github.com/ArcInstitute/stack.git | |
| cd stack | |
| pip install -e . | |
| ``` | |
| ### Using uv | |
| ```bash | |
| # Install from PyPI | |
| uv pip install arc-stack | |
| # Or install from source for development | |
| git clone https://github.com/ArcInstitute/stack.git | |
| cd stack | |
| uv pip install -e . | |
| ``` | |
| ## Quick Start | |
| - Use Stack to embed your single-cell data: [Notebook](notebooks/tutorial-embed.ipynb) | |
| - Use Stack to zero-shot predict unseen perturbation/observation profiles: [Notebook](notebooks/tutorial-predict.ipynb) | |
| ### Training Stack from Scratch | |
| ```bash | |
| # Once installed, the console entry point becomes available | |
| stack-train \ | |
| --dataset_configs "/path/to/data:false:gene_symbols" \ | |
| --genelist_path "hvg_genes.pkl" \ | |
| --save_dir "./checkpoints" \ | |
| --sample_size 256 \ | |
| --batch_size 32 \ | |
| --n_hidden 100 \ | |
| --token_dim 16 \ | |
| --n_layers 9 \ | |
| --max_epochs 10 | |
| # Alternatively, invoke the module directly when working from a cloned repo | |
| python -m stack.cli.launch_training [args...] | |
| ``` | |
| ### Fine-tuning Stack with Frozen Teacher | |
| ```bash | |
| stack-finetune \ | |
| --checkpoint_path "./checkpoints/pretrained.ckpt" \ | |
| --dataset_configs "human:/path/to/data:donor_id:cell_type:false" \ | |
| --genelist_path "hvg_genes.pkl" \ | |
| --save_dir "./finetuned_checkpoints" \ | |
| --sample_size 512 \ | |
| --batch_size 8 \ | |
| --replacement_ratio 0.75 \ | |
| --max_epochs 8 | |
| # Or use uv run | |
| uv run stack-finetune [args...] | |
| # Repository wrapper remains available for local development | |
| python -m stack.cli.launch_finetuning [args...] | |
| ``` | |
| ### Running Stack with configuration files | |
| Both `launch_training.py` and `launch_finetuning.py` accept a `--config` flag that points to a YAML or JSON file. Any command line | |
| arguments omitted after `--config` inherit their values from the file, while flags provided on the command line override the | |
| configuration. Example configs mirroring the provided Slurm scripts live under `configs/`: | |
| ```bash | |
| # Train with the preset configuration | |
| stack-train --config configs/training/bc_large.yaml | |
| # Override a single hyperparameter without editing the file | |
| stack-train --config configs/training/bc_large.yaml --learning_rate 5e-5 | |
| # Fine-tune using a config file | |
| stack-finetune --config configs/finetuning/ft_parsecg.yaml | |
| # Direct module invocation is still supported if you prefer python -m | |
| python -m stack.cli.launch_training --config configs/training/bc_large.yaml | |
| ``` | |
| > **Note:** YAML configs require [`pyyaml`](https://pyyaml.org/). Install it with `pip install pyyaml` or use a JSON config file. | |
| ### Extracting Stack Embeddings | |
| ```bash | |
| stack-embedding \ | |
| --checkpoint "./checkpoints/pretrained.ckpt" \ | |
| --adata "data.h5ad" \ | |
| --genelist "hvg_genes.pkl" \ | |
| --output "embeddings.h5ad" \ | |
| --batch-size 32 | |
| # Or use uv run | |
| uv run stack-embedding \ | |
| --checkpoint "./checkpoints/pretrained.ckpt" \ | |
| --adata "data.h5ad" \ | |
| --genelist "hvg_genes.pkl" \ | |
| --output "embeddings.h5ad" \ | |
| --batch-size 32 | |
| ``` | |
| ### In-Context Generation with Stack | |
| ```bash | |
| stack-generation \ | |
| --checkpoint "./checkpoints/pretrained.ckpt" \ | |
| --base-adata "base_data.h5ad" \ | |
| --test-adata "test_data.h5ad" \ | |
| --genelist "hvg_genes.pkl" \ | |
| --output-dir "./generations" \ | |
| --split-column "donor_id" | |
| # Or use uv run | |
| uv run stack-generation \ | |
| --checkpoint "./checkpoints/pretrained.ckpt" \ | |
| --base-adata "base_data.h5ad" \ | |
| --test-adata "test_data.h5ad" \ | |
| --genelist "hvg_genes.pkl" \ | |
| --output-dir "./generations" \ | |
| --split-column "donor_id" | |
| ``` | |
| ## Model Architecture | |
| - **Tabular Attention**: Alternating cell-wise and gene-wise attention layers | |
| - **Token Dimension**: Configurable token embedding dimension (default: 16) | |
| - **Hidden Dimension**: Gene dimension reduction (default: 100) | |
| - **Masking Strategy**: Rectangular masking with variable rates (0.1-0.8) | |
| ## Data Preparation | |
| ### Computing Highly Variable Genes (HVGs) | |
| ```python | |
| from stack.data.datasets import DatasetConfig, compute_hvg_union | |
| configs = [DatasetConfig(path="/data/path", filter_organism=True)] | |
| hvg_genes = compute_hvg_union(configs, n_top_genes=1000, output_path="hvg.pkl") | |
| ``` | |
| ### Dataset Configuration Format | |
| - **Human datasets**: `human:/path:donor_col:cell_type_col[:filter_organism[:gene_col]]` | |
| - **Drug datasets**: `drug:/path:condition_col:cell_line_col:control_condition[:filter_organism[:gene_col]]` | |
| ## Key Features | |
| - **In-Context Learning**: Zero-shot generalization to new biological contexts | |
| - **Multi-Dataset Training**: Simultaneous training on multiple single-cell datasets | |
| - **Frozen Teacher Fine-tuning**: Novel fine-tuning procedure with stable teacher targets | |
| - **Efficient Data Loading**: Optimized HDF5 loading with sparse matrix support | |
| > **Note:** `scShiftAttentionModel` remains available as an alias for backward compatibility. | |
| ## Citation | |
| If you use Stack in your research, please cite the Stack [paper](https://www.biorxiv.org/content/10.64898/2026.01.09.698608v1). | |
| ## Licenses | |
| Stack code is [licensed](LICENSE) under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0). | |
| The model weights and output are licensed under the [Arc Research Institute Stack Model Non-Commercial License](MODEL_LICENSE.md) and subject to the [Arc Research Institute Stack Model Acceptable Use Policy](MODEL_ACCEPTABLE_USE_POLICY.md). | |