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2021-ml-in-genomics

This repo contains the course material for the course Feature selection in GWAS given at the Machine Learning in Genomics intensive week (day 4). The slides are from Chloé-Agathe Azencott and the practicals are adapted from https://github.com/chagaz/ds3-2018-genetics.

Content of the course

This repo includes the slides of the lecture and the jupyter notebooks of the practical sessions. The notebooks cover the same tools as the lecture:

  • practical1:
    • T-test and Manhattan plots
    • Linear regression
    • Lasso
  • practical2:
    • Elastic-net
    • Multi-task lasso
    • Network-constained lasso

The practicals require writting very little code: most questions are about commenting on the results. Corrected version of the practicals are provided.

Installation

  • Clone the repository git clone https://github.com/goepp/ml-in-genomics-2021/

  • You need to download the heavy files athaliana_small.X.txt and athaliana_small.W.txt here and place them in practical/data/. Alternatively, you can just run the notebooks cells which download these two files.

Quick setup from scratch

You need python3, conda, and jupyter notebook. An easy way to set things up from scratch is:

  • Download https://conda.io/en/master/miniconda.html.

  • Create a conda environment: conda env create --file=environment.yml.

  • Activate the conda env: conda activate mlgen.

  • Run the jupyter notebook from within the conda env: jupyter notebook and your notebook should open in a web browser. You're good to go!