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| # scprint | ||
| # scPRINT: Large Cell Model for scRNAseq data | ||
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| [](https://codecov.io/gh/jkobject/scPRINT) | ||
| [](https://github.com/jkobject/scPRINT/actions/workflows/main.yml) | ||
| [](https://badge.fury.io/py/scprint) | ||
| [](https://scprint.readthedocs.io/en/latest/?badge=latest) | ||
| [](https://pepy.tech/project/scprint) | ||
| [](https://pepy.tech/project/scprint) | ||
| [](https://pepy.tech/project/scprint) | ||
| [](https://img.shields.io/github/issues/jkobject/scPRINT) | ||
| [](https://github.com/psf/black) | ||
| []() | ||
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| Awesome Large Transcriptional Model created by Jeremie Kalfon | ||
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| scprint = single cell pretrained regulation inference neural network from transcripts | ||
| scPRINT is a large transformer model built for the inference of gene networks (connections between genes explaining the cell's expression profile) from scRNAseq data. | ||
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| using: | ||
| It uses novel encoding and decoding of the cell expression profile and new pre-training methodologies to learn a cell model. | ||
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| scPRINT can be used to perform the following analyses: | ||
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| ## Install it from PyPI | ||
| - __expression denoising__: increase the resolution of your scRNAseq data | ||
| - __cell embedding__: generate a low-dimensional representation of your dataset | ||
| - __label prediction__: predict the cell type, disease, sequencer, sex, and ethnicity of your cells | ||
| - __gene network inference__: generate a gene network from any cell or cell cluster in your scRNAseq dataset | ||
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| first have a good version of pytorch installed | ||
| [Read the paper!](https://www.biorxiv.org/content/10.1101/2024.07.29.605556v1) if you would like to know more about scPRINT. | ||
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| you might need to make it match your cuda version etc.. | ||
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| We only support torch>=2.0.0 | ||
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| then install laminDB | ||
| ## Install `scPRINT` | ||
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| ```bash | ||
| pip install 'lamindb[jupyter,bionty]' | ||
| For the moment scPRINT has been tested on MacOS and Linux (Ubuntu 20.04) with Python 3.10. | ||
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| If you want to be using flashattention2, know that it only supports triton 2.0 MLIR's version and torch==2.0.0 for now. | ||
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| ```python | ||
| conda create -n "[whatever]" python==3.10 | ||
| git clone https://github.com/jkobject/scPRINT | ||
| #one of | ||
| pip install scPRINT # OR | ||
| pip install scPRINT[dev] # for the dev dependencies (building etc..) AND/OR [dev,flash] | ||
| pip install scPRINT[flash] && pip install -e "git+https:/ | ||
| /github.com/triton-lang/triton.git@legacy-backend | ||
| #egg=triton&subdirectory=python" # to use flashattention2, you will need to install triton 2.0.0.dev20221202 specifically, working on removing this dependency # only if you have a compatible gpu (e.g. not available for apple GPUs for now, see https://github.com/triton-lang/triton?tab=readme-ov-file#compatibility) | ||
| ``` | ||
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| then install scPrint | ||
| We make use of some additional packages we developed alongside scPRint. | ||
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| ```bash | ||
| pip install scprint | ||
| Please refer to their documentation for more information: | ||
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| I had to install a specific version of pytorch, torchaudio, torchtext.. for my cuda version. | ||
| My cuda compiler nvcc compiles cuda 11.7. my cuda-smi (api) is currently 12.1. | ||
| - [scDataLoader](https://github.com/jkobject/scDataLoader): a dataloader for training large cell models. | ||
| - [GRnnData](https://github.com/cantinilab/GRnnData): a package to work with gene networks from single cell data. | ||
| - [benGRN](https://github.com/jkobject/benGRN): a package to benchmark gene network inference methods from single cell data. | ||
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| Please install all of it for your cuda version and it should still work. | ||
| ### lamin.ai | ||
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| for more information on this, please see [installation.md](installation.md). | ||
| ``` | ||
| ⚠️ if you want to use the scDataloader's multi-dataset mode or if you want to preprocess datasets and other functions of the model, you will need to use lamin.ai. | ||
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| In that case, connect with google or github to [lamin.ai](https://lamin.ai/login), then be sure to connect before running anything (or before starting a notebook): `lamin login <email> --key <API-key>`. Follow the instructions on [their website](https://docs.lamin.ai/guide). | ||
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| ## Usage | ||
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| ### scPRINT's basic commands | ||
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| This is the most minimal example of how scPRINT works: | ||
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| ```py | ||
| from lightning.pytorch import Trainer | ||
| from scprint import scPrint | ||
| from scdataloader import DataModule | ||
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| ... | ||
| datamodule = DataModule(...) | ||
| model = scPrint(...) | ||
| # to train / fit / test the model | ||
| trainer = Trainer(...) | ||
| trainer.fit(model, datamodule=datamodule) | ||
| # to do predictions Denoiser, Embedder, GNInfer | ||
| denoiser = Denoiser(...) | ||
| adata = sc.read_h5ad(...) | ||
| denoiser(model, adata=adata) | ||
| ... | ||
| ``` | ||
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| or, from a bash command line | ||
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| ```bash | ||
| $ python -m scPrint/__main__.py | ||
| #or | ||
| $ scprint fit/train/predict/test | ||
| $ scprint fit/train/predict/test/denoise/embed/gninfer --config config/[medium|large|vlarge] ... | ||
| ``` | ||
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| find out more about the commands by running `scprint --help` or `scprint [command] --help`. | ||
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| more examples of using the command line are available in the [docs](./docs/usage.md). | ||
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| ### Notes on GPU/CPU usage with triton | ||
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| If you do not have [triton](https://triton-lang.org/main/python-api/triton.html) installed you will not be able to take advantage of GPU acceleration, but you can still use the model on the CPU. | ||
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| In that case, if loading from a checkpoint that was trained with flashattention, you will need to specify `transformer="normal"` in the `load_from_checkpoint` function like so: | ||
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| ```python | ||
| model = scPrint.load_from_checkpoint( | ||
| '../data/temp/last.ckpt', precpt_gene_emb=None, | ||
| transformer="normal") | ||
| ``` | ||
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| for more information on usage please see the documentation in https://jkobject.com/scPrint | ||
| We now explore the different usages of scPRINT: | ||
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| ### I want to generate gene networks from scRNAseq data: | ||
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| -> Refer to the section . gene network inference in [this notebook](./notebooks/cancer_usecase.ipynb#). | ||
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| -> More examples in this notebook [notebooks/assessments/bench_omni.ipynb](../notebooks/bench_omni.ipynb). | ||
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| ### I want to generate cell embeddings and cell label predictions from scRNAseq data: | ||
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| -> Refer to the embeddings and cell annotations section in [this notebook](./notebooks/cancer_usecase.ipynb#). | ||
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| ### I want to denoising my scRNAseq dataset: | ||
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| -> Refer to the Denoising of B-cell section in [this notebook](./notebooks/cancer_usecase.ipynb). | ||
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| -> More example in our benchmark notebook [notebooks/assessments/bench_denoising.ipynb](../notebooks/bench_denoising.ipynb). | ||
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| ### I want to generate an atlas-level embedding | ||
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| -> Refer to the notebook [figures/nice_umap.ipynb](../figures/nice_umap.ipynb). | ||
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| ### I need to generate gene tokens using pLLMs | ||
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| To run scPRINT, you can use the option to define the gene tokens using protein language model embeddings of genes. This is done by providing the path to a parquet file of the precomputed set of embeddings for each gene name to scPRINT via "precpt_gene_emb" | ||
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| -> To generate this file please refer to the notebook [notebooks/generate_gene_embeddings.ipynb](../notebooks/generate_gene_embeddings.ipynb). | ||
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| ### I want to pre-train scPRINT from scratch on my own data | ||
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| -> Refer to the documentation page [pretrain scprint](pretrain.md) | ||
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| ### Documentation | ||
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| For more information on usage please see the documentation in [https://www.jkobject.com/scPrint/](https://www.jkobject.com/scPrint/) | ||
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| ### Model Weights | ||
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| Model weights are available on [hugging face](https://huggingface.co/jkobject/scPRINT/). | ||
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| ## Development | ||
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| Read the [CONTRIBUTING.md](CONTRIBUTING.md) file. | ||
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| ### What is included? | ||
| Read the [training runs](https://wandb.ai/ml4ig/scprint_scale/reports/scPRINT-trainings--Vmlldzo4ODIxMjgx?accessToken=80metwx7b08hhourotpskdyaxiflq700xzmzymr6scvkp69agybt79l341tv68hp) document to know more about how pre-training was performed and the its behavior. | ||
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| - 📃 Documentation structure using [mkdocs](http://www.mkdocs.org) | ||
| - 🧪 Testing structure using [pytest](https://docs.pytest.org/en/latest/) | ||
| If you want [codecov](https://about.codecov.io/sign-up/) Reports and Automatic Release to [PyPI](https://pypi.org) | ||
| On the new repository `settings->secrets` add your `PYPI_API_TOKEN` and `CODECOV_TOKEN` (get the tokens on respective websites) | ||
| - ✅ Code linting using [flake8](https://flake8.pycqa.org/en/latest/) | ||
| - 📊 Code coverage reports using [codecov](https://about.codecov.io/sign-up/) | ||
| - 🛳️ Automatic release to [PyPI](https://pypi.org) using [twine](https://twine.readthedocs.io/en/latest/) and github actions. | ||
| Acknowledgement: | ||
| [python template](https://github.com/rochacbruno/python-project-template) | ||
| [laminDB](https://lamin.ai/) | ||
| [lightning](https://lightning.ai/) | ||
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| ## Work in progress: | ||
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| acknowledgement: | ||
| [python template](https://github.com/rochacbruno/python-project-template) | ||
| [scGPT]() | ||
| [laminDB]() | ||
| 1. remove the triton dependencies | ||
| 2. add version with additional labels (tissues, age) and organisms (mouse, zebrafish) and more datasets from cellxgene | ||
| 3. version with separate transformer blocks for the encoding part of the bottleneck learning and for the cell embeddings | ||
| 4. improve classifier to output uncertainties and topK predictions when unsure | ||
| 5. | ||
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| Awesome Large Cell Model created by Jeremie Kalfon. |
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