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# What | ||
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*What does this PR do, and preferably how* | ||
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# Testing | ||
*Testing this PR involves X,Y,Z* | ||
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## Test scenarios | ||
*Test these things* | ||
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1. Instructions | ||
2. ... | ||
3. ... |
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# GitHub Action that uses Black to reformat the Python code in an incoming pull request. | ||
# If all Python code in the pull request is complient with Black then this Action does nothing. | ||
# Othewrwise, Black is run and its changes are committed back to the incoming pull request. | ||
# https://github.com/cclauss/autoblack | ||
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name: autoblack_pull_request | ||
on: [ pull_request ] | ||
jobs: | ||
black-code: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: actions/checkout@v3 | ||
with: | ||
ref: ${{ github.head_ref }} | ||
- uses: actions/setup-python@v4 | ||
with: | ||
python-version: '3.11' | ||
- run: pip install black | ||
- run: black --check . | ||
- name: If needed, commit black changes to the pull request | ||
if: failure() | ||
run: | | ||
printenv | grep GITHUB | ||
git config --global user.name 'PMBio' | ||
git config --global user.email 'PMBio@users.noreply.github.com' | ||
git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY | ||
git remote -v | ||
git branch | ||
git status | ||
black . | ||
git status | ||
echo ready to commit | ||
git commit -am "fixup! Format Python code with psf/black pull_request" | ||
echo ready to push | ||
git push |
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name: "Pull Request Docs Check" | ||
run-name: "Docs Check 📑📝" | ||
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on: | ||
- pull_request | ||
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jobs: | ||
docs-build: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: actions/checkout@v3 | ||
- uses: ammaraskar/sphinx-action@0.4 | ||
with: | ||
docs-folder: "docs/" | ||
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docs-link-check: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: actions/checkout@v3 | ||
- uses: ammaraskar/sphinx-action@0.4 | ||
with: | ||
docs-folder: "docs/" | ||
build-command: "make linkcheck" |
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# .readthedocs.yaml | ||
# Read the Docs configuration file | ||
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details | ||
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# Required | ||
version: 2 | ||
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# Set the OS, Python version and other tools you might need | ||
build: | ||
os: ubuntu-22.04 | ||
tools: | ||
python: "3.12" | ||
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sphinx: | ||
configuration: docs/conf.py | ||
fail_on_warning: true | ||
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python: | ||
install: | ||
- requirements: docs/requirements.txt |
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cff-version: 1.2.0 | ||
title: DeepRVAT | ||
message: >- | ||
If you use this software, please cite it using the | ||
metadata from this file. | ||
type: software | ||
authors: | ||
- given-names: Brian | ||
family-names: Clarke | ||
orcid: 'https://orcid.org/0000-0002-6695-286X' | ||
- given-names: Eva | ||
family-names: Holtkamp | ||
orcid: 'https://orcid.org/0000-0002-2129-9908' | ||
- given-names: Hakime | ||
family-names: Öztürk | ||
- given-names: Marcel | ||
family-names: Mück | ||
orcid: 'https://orcid.org/0009-0000-3129-2630' | ||
- given-names: Magnus | ||
family-names: Wahlberg | ||
orcid: 'https://orcid.org/0009-0001-9140-2392' | ||
- given-names: Kayla | ||
family-names: Meyer | ||
orcid: 'https://orcid.org/0009-0003-5063-5266' | ||
- given-names: Felix | ||
family-names: Munzlinger | ||
orcid: 'https://orcid.org/0009-0005-1407-8145' | ||
- given-names: Felix | ||
family-names: Brechtmann | ||
orcid: 'https://orcid.org/0000-0002-0110-152X' | ||
- given-names: Florian Rupert | ||
family-names: Hölzlwimmer | ||
orcid: 'https://orcid.org/0000-0002-5522-2562' | ||
- given-names: Julien | ||
family-names: Gagneur | ||
orcid: 'https://orcid.org/0000-0002-8924-8365' | ||
- given-names: Oliver | ||
family-names: Stegle | ||
orcid: 'https://orcid.org/0000-0002-8818-7193' | ||
identifiers: | ||
- type: doi | ||
value: 10.1101/2023.07.12.548506 | ||
repository-code: 'https://github.com/PMBio/deeprvat' | ||
abstract: >- | ||
Integration of variant annotations using deep set networks | ||
boosts rare variant association genetics. | ||
Rare genetic variants can strongly predispose to disease, | ||
yet accounting for rare variants in genetic analyses is | ||
statistically challenging. While rich variant annotations | ||
hold the promise to enable well-powered rare variant | ||
association tests, methods integrating variant annotations | ||
in a data-driven manner are lacking. Here, we propose | ||
DeepRVAT, a set neural network-based approach to learn | ||
burden scores from rare variants, annotations and | ||
phenotypes. In contrast to existing methods, DeepRVAT | ||
yields a single, trait-agnostic, nonlinear gene impairment | ||
score, enabling both risk prediction and gene discovery in | ||
a unified framework. On 21 quantitative traits and | ||
whole-exome-sequencing data from UK Biobank, DeepRVAT | ||
offers substantial increases in gene discoveries and | ||
improved replication rates in held-out data. Moreover, we | ||
demonstrate that the integrative DeepRVAT gene impairment | ||
score greatly improves detection of individuals at high | ||
genetic risk. We show that pre-trained DeepRVAT scores | ||
generalize across traits, opening up the possibility to | ||
conduct highly computationally efficient rare variant | ||
tests. | ||
license: MIT |
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