fastlmm
Python version of Factored Spectrally Transformed Linear Mixed Models
Science Score: 33.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: biorxiv.org, nature.com -
✓Committers with academic emails
1 of 16 committers (6.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.9%) to scientific vocabulary
Last synced: 10 months ago
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JSON representation
Repository
Python version of Factored Spectrally Transformed Linear Mixed Models
Basic Info
- Host: GitHub
- Owner: fastlmm
- License: apache-2.0
- Language: Python
- Default Branch: master
- Homepage: https://fastlmm.github.io/
- Size: 142 MB
Statistics
- Stars: 52
- Watchers: 3
- Forks: 12
- Open Issues: 7
- Releases: 0
Fork of mycecilia/FaST-LMM
Created over 6 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
License
README.md
FaST-LMM
FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed Models, is a program for performing genome-wide association studies (GWAS) on datasets of all sizes, up to one millions samples.
This release contains the following features, each illustrated with an IPython notebook.
- Core FaST-LMM (notebook) -- Lippert et al., Nature Methods 2011
Improvements:
- New features for single_snp (including effect size and multiple phenotype support) and epistasis (including reporting beta and using pre-computed eigenvalue decompositions) (notebook) -- Lippert et al., Nature Methods 2011
- Ludicrous-Speed GWAS (notebook) -- Kadie and Heckerman, bioRxiv 2018
- Heritability with Spatial Correction (notebook), Heckerman et al., PNAS 2016
- Two Kernels (notebook) -- Widmer et al., Scientific Reports 2014
- Set Analysis (notebook) -- Lippert et al., Bioinformatics 2014
- Epistasis (notebook) -- Lippert et al., Scientific Reports, 2013
- Prediction (notebook) -- Lippert et al., Nature Methods 2011
A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.
Quick install:
pip install fastlmm
If you need support for BGEN files, instead do:
pip install fastlmm[bgen]
For best performance, be sure your Python distribution includes a fast version of NumPy. We use Anaconda's Miniconda.
Documentation
- IPython Notebooks:
- Main Documentation
- Project Home and Full Annotated Bibliography
Code
Contacts
- Email the developers at fastlmm-dev@python.org.
- Join the user discussion and announcement list (or use web sign up).
- Open an issue on GitHub.
Owner
- Name: fastlmm
- Login: fastlmm
- Kind: organization
- Repositories: 4
- Profile: https://github.com/fastlmm
GitHub Events
Total
- Issues event: 5
- Watch event: 4
- Issue comment event: 21
- Push event: 15
- Fork event: 1
- Create event: 3
Last Year
- Issues event: 5
- Watch event: 4
- Issue comment event: 21
- Push event: 15
- Fork event: 1
- Create event: 3
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Carl Kadie | c****k@m****m | 474 |
| Carl Kadie | c****k@m****m | 147 |
| Christoph Lippert | c****t@g****m | 31 |
| jennilis | j****l@m****m | 24 |
| Chris Widmer | c****r@m****m | 22 |
| David Heckerman | h****a@m****m | 12 |
| Ubuntu | a****r@c****t | 9 |
| David Heckerman | h****a | 5 |
| Christoph Lippert | c****t@H****l | 3 |
| Nicolo Fusi | n****i@g****m | 3 |
| Gao Wang | g****w@u****u | 2 |
| Carl Kadie | c****k@m****m | 1 |
| Christoph Lippert | c****t@H****t | 1 |
| DSLituiev | d****v@g****m | 1 |
| Gao Wang | w****w@g****m | 1 |
| omerwe | o****e@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 50
- Total pull requests: 6
- Average time to close issues: 5 months
- Average time to close pull requests: 5 months
- Total issue authors: 25
- Total pull request authors: 5
- Average comments per issue: 4.0
- Average comments per pull request: 1.17
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 10
- Pull requests: 0
- Average time to close issues: 4 days
- Average time to close pull requests: N/A
- Issue authors: 7
- Pull request authors: 0
- Average comments per issue: 4.6
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- snowformatics (15)
- CSGallagher (3)
- CarlKCarlK (3)
- vb-iman (3)
- remomomo (2)
- Jorge-Hernansanz (2)
- lucasmiranda42 (2)
- AbestSG (1)
- JorgeHB14 (1)
- code-review-doctor (1)
- limin321 (1)
- DesmondSmith (1)
- sariya (1)
- hans19-zs (1)
- carrot881216 (1)
Pull Request Authors
- gaow (2)
- ofrei (2)
- eric-czech (1)
- lucasmiranda42 (1)
- code-review-doctor (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 1,483 last-month
- Total dependent packages: 1
- Total dependent repositories: 7
- Total versions: 60
- Total maintainers: 4
pypi.org: fastlmm
Fast GWAS
- Documentation: https://fastlmm.readthedocs.io/
- License: Apache 2.0
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Latest release: 0.6.12
published over 1 year ago
Rankings
Dependent packages count: 4.8%
Dependent repos count: 5.5%
Average: 8.2%
Downloads: 9.8%
Stargazers count: 10.4%
Forks count: 10.5%
Maintainers (4)
Last synced:
11 months ago
Dependencies
.github/workflows/ci.yml
actions
- actions/checkout v2 composite
- actions/upload-artifact v2 composite
- conda-incubator/setup-miniconda v2 composite
Dockerfile
docker
- python 3.8-slim-buster build
fastlmm/pyplink/setup.py
pypi
- matplotlib *
- numpy *
- pandas *
- scikit-learn *
- scipy *
requirements.txt
pypi
- cloudpickle >=2.2.0
- fastlmmclib >=0.0.2
- matplotlib >=1.5.1
- pandas >=1.1.1
- psutil >=5.6.7
- pysnptools >=0.5.7
- scikit-learn >=0.19.1
- statsmodels >=0.10.1
setup.py
pypi
- cloudpickle >=2.2.0
- fastlmmclib >=0.0.2
- matplotlib >=1.5.1
- pandas >=1.1.1
- psutil >=5.6.7
- pysnptools >=0.5.7
- scikit-learn >=0.19.1
- statsmodels >=0.10.1