Random effects
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 761
- Forks
- 90
- Avg merge
- 25m
- Merged PRs (30d)
- 10
Description
The lack of random effects in the original DEseq2 R package was a huge oversight that causes false positives in cases of genotypically driven expression in experiments with multiple samples per donor. Fixing this here would require swapping sklearn for statsmodels, but it would be worth it for such a critical feature. Let me know how I can help.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the current Python differential-expression implementation and its scikit-learn dependency, then compare the requested random-effects support with statsmodels. Done means supporting experiments with multiple samples per donor while reducing the stated genotype-driven false positives; the issue does not name specific files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- bioinformatics
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100