For assignment01:
- process the code in the jupiter notebooks starting from step1 to step 4
- data was processed outside of the local machine and a pickle file was generated and imported. Local machine has an intel i7 (12th gen) with 16 GB of RAM and a clean Ubuntu Nobel (v22) with surface pro 9 kernel (v6.9) distribution install.
update: this data has now been processed on a Macbook M4 Pro with 24 GB of RAM. See assignment 2 for the updated data preparation.
- environment file is shared across the repository (see cpbs7602.yml) and was installed via terminal after miniconda3 install. conda env create --file {path_to_cpbs7602_yaml} For conveinence, the contents of the environment file are listed at the bottom of the README.
For assignment03:
- process this code in the jupyter notebook using the tpm_scaled data, the sample_key.csv, and the subject_phenotypes.txt files.
- as with assignment01, the environment file should have all packages needed.
- process the dimensionality reduction, then the tissue model, then the age model.
name: cpbs7602 channels:
- conda-forge
- defaults dependencies:
- ipython=8.*
- ipywidgets
- jupyterlab=4.2.*
- jupyter_contrib_nbextensions=0.7.*
- jupytext=1.*
- matplotlib=3.9.*
- nodejs=22.*
- pandas=2.2.*
- pandoc=3.*
- pip
- pre-commit=4.0.*
- python=3.12.*
- pyyaml=6.*
- scipy=1.14.*
- seaborn=0.13.*
- scikit-learn=1.5.*
- statsmodels=0.14.*
- tqdm=4.*
- umap-learn