Rare variant association testing using deep learning and data-driven burden scores.
A good place to start is in :doc:`Basic usage </quickstart>`, to install the package and make sure it runs correctly.
To run DeepRVAT on your data, first consult Modes of usage :doc:`here </practical>`, then proceed based on which mode is right for your use case.
For all modes, you'll want to consult Input data: Common requirements for all pipelines and Configuration file: Common parameters :doc:`here </deeprvat>`.
For all modes of usage other than association testing with precomputed burdens, you'll need to :doc:`preprocess </preprocessing>` your genotype data, followed by :doc:`annotating </annotations>` your variants.
To train custom DeepRVAT models, rather than using precomputed burdens or our provided pretrained models, you'll need to additionally run :doc:`seed gene discovery </seed_gene_discovery>`.
Finally, consult the relevant section for your use case :doc:`here </deeprvat>`.
If running DeepRVAT on a cluster (recommended), some helpful tips are :doc:`here </cluster>`.
If you use this package, please cite:
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
To report a bug or make a feature request, please create an issue on GitHub.
.. toctree:: :maxdepth: 2 :caption: Contents: installation.md quickstart.md preprocessing.md annotations.md seed_gene_discovery.md deeprvat.md cluster.md practical.md ukbiobank.md apidocs/index