Paper reproducibility

The everyday install is pip install phenome. The analyses in the paper were run with Python 3.12 and PyTorch 2.12, and with PhenoMe(seed=42). Those versions are pinned in the conda environments below, not by the pip install.

GPU, which is the setup used for those analyses:

git clone https://github.com/PhenoMe-Ecosystem/PhenoMe.git
cd PhenoMe
conda env create -f envs/environment-gpu.yml
conda activate phenome-gpu

The same PyTorch pin on CPU:

conda env create -f envs/environment-cpu.yml
conda activate phenome-cpu

The notebooks that regenerate the main analyses are in Notebooks/examples/: BBBC021, BBBC014, the Human Protein Atlas, the E. coli antibiotic time course, CellCognition, and STED-FM on synaptic proteins. Each one calls PhenoMe(device=device, seed=42). The STED-FM notebook also needs the STED-FM package, which is not part of the PhenoMe install. The install commands and the dataset scripts are in that notebook and in Notebooks/examples/sted_fm_utils/.