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@@ -4,75 +4,17 @@ Rare variant association testing using deep learning and data-driven burden scor | |
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| [](https://deeprvat.readthedocs.io/en/latest/?badge=latest) | ||
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| ## Installation | ||
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| 1. Clone this repository: | ||
| ``` | ||
| git clone [email protected]:PMBio/deeprvat.git | ||
| ``` | ||
| 1. Change directory to the repository: `cd deeprvat` | ||
| 1. Install the conda environment. We recommend using [mamba](https://mamba.readthedocs.io/en/latest/index.html), though you may also replace `mamba` with `conda` | ||
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| *note: [the current deeprvat env does not support cuda when installed with conda](https://github.com/PMBio/deeprvat/issues/16), install using mamba for cuda support.* | ||
| ``` | ||
| mamba env create -n deeprvat -f deeprvat_env.yaml | ||
| ``` | ||
| 1. Activate the environment: `mamba activate deeprvat` | ||
| 1. Install the `deeprvat` package: `pip install -e .` | ||
| ## Installation and usage | ||
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| If you don't want to install the gpu related requirements use the `deeprvat_env_no_gpu.yml` environment instead. | ||
| ``` | ||
| mamba env create -n deeprvat -f deeprvat_env_no_gpu.yaml | ||
| ``` | ||
| Please consult our [documentation](https://deeprvat.readthedocs.io/en/latest/) | ||
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| ## Basic usage | ||
| ## Citation | ||
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| ### Customize pipelines | ||
| If you use this package, please cite: | ||
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| Before running any of the snakefiles, you may want to adjust the number of threads used by different steps in the pipeline. To do this, modify the `threads:` property of a given rule. | ||
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| If you are running on an computing cluster, you will need a [profile](https://github.com/snakemake-profiles) and may need to add `resources:` directives to the snakefiles. | ||
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| ### Run the preprocessing pipeline on VCF files | ||
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| Instructions [here](https://deeprvat.readthedocs.io/en/latest/preprocessing.html) | ||
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| ### Annotate variants | ||
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| Instructions [here](https://deeprvat.readthedocs.io/en/latest/annotations.html) | ||
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| ### Try the full training and association testing pipeline on some example data | ||
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| ``` | ||
| mkdir example | ||
| cd example | ||
| ln -s [path_to_deeprvat]/example/* . | ||
| snakemake -j 1 --snakefile [path_to_deeprvat]/pipelines/training_association_testing.snakefile | ||
| ``` | ||
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| Replace `[path_to_deeprvat]` with the path to your clone of the repository. | ||
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| Note that the example data is randomly generated, and so is only suited for testing whether the `deeprvat` package has been correctly installed. | ||
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| ### Run the association testing pipeline with pretrained models | ||
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| ``` | ||
| mkdir example | ||
| cd example | ||
| ln -s [path_to_deeprvat]/example/* . | ||
| ln -s [path_to_deeprvat]/pretrained_models | ||
| snakemake -j 1 --snakefile [path_to_deeprvat]/pipelines/association_testing_pretrained.snakefile | ||
| ``` | ||
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| Replace `[path_to_deeprvat]` with the path to your clone of the repository. | ||
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| Again, note that the example data is randomly generated, and so is only suited for testing whether the `deeprvat` package has been correctly installed. | ||
| Clarke, Holtkamp et al., “Integration of Variant Annotations Using Deep Set Networks Boosts Rare Variant Association Genetics.” bioRxiv. https://dx.doi.org/10.1101/2023.07.12.548506 | ||
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| ## Credits | ||
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