Hands on session for the day on GWAS in the context of the PSL Intensive Week on ML and Genomics 2022
Course website: https://data-psl.github.io/intensive-week-genomics-2022/
Prepared by Chloé-Agathe Azencott with the help of Vivien Goepp.
The notebooks cover the same tools as the lecture:
- Practical 1:
- T-test and Manhattan plots
- PCA-based correction for population stratification
- Multivariate linear regression
- Practical 2:
- Lasso
- Stability
- Practical 3:
- Elastic-net
- Practical 4:
- Multi-task lasso
- Practical 5 (to be made available):
- Network-constrained lasso
The first 3 practicals require writting very little code: most questions are about commenting on the results. In the 4th practical, you will have to reuse and adapt code from the previous practicals.
The lecture is based on a version of these slides from a January 2022 lecture.
You can either
- Download the repository as zip under the green "Code" button on the top right corner of this page
or
- Fork the repository (using the "Fork" button in the upper right corner), then clone ''your'' version of the repo (with URL
https://github.com/<your_github_username>/ml-in-genomics-2022/to your machine using instructions under the green "Code" button on the top right corner of the page, then add the original repo as an upstream branch withgit remote add upstream git@github.com:chagaz/ml-in-genomics-2022.gitThen whenever you want to synchronize your version with the upstream version, you can usegit checkout main git fetch upstream git merge upstream/main
You will need Python3, a few numerical python librairies (numpy, matplotlib, pandas, seaborn, scikit-learn) and Jupyter Lab/Notebook. An easy way to set up from scratch is to
- Install miniconda
- Create a conda environment using the
environment.ymlfile in this repo :conda env create --file=environment.yml -n mlgenwhich you can then activate usingconda activate mlgen - Start Jupyter from this environment and navigate to the notebook you want to run.