aman
acadbd780c
Full reproducible pipeline: .mcool + ChIP-seq bigwigs → latent embeddings → A/B compartment calls → cross-cell comparison. Key results (chr21, 25 kb, latent dim=32): - Test AUC=0.777, AP=0.759 (converged epoch 31/300) - GM12878 A/B silhouette (cosine) = 0.775 - IMR90 zero-shot silhouette = 0.443 - A-compartment bins stable across cell types (mean cosine Δ=0.042) - B-compartment bins shift substantially (mean cosine Δ=0.451) - 101 B→A and 70 A→B compartment switches GM12878→IMR90
28 lines
823 B
YAML
28 lines
823 B
YAML
name: chromatin_gnn
|
|
# Installation (pip-based, conda used only for Python):
|
|
# conda create -n chromatin_gnn python=3.10 -y
|
|
# conda activate chromatin_gnn
|
|
# pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cpu
|
|
# pip install -r requirements.txt
|
|
#
|
|
# For GPU support replace the torch line with:
|
|
# pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cu118
|
|
channels:
|
|
- defaults
|
|
dependencies:
|
|
- python=3.10
|
|
- pip
|
|
- pip:
|
|
- torch==2.1.2 # CPU build; see GPU note above
|
|
- torch-geometric==2.5.3
|
|
- cooler==0.9.3
|
|
- pyBigWig==0.3.25
|
|
- pandas==2.1.4
|
|
- "numpy>=1.24,<2.0" # cooler 0.9.3 requires numpy<2.0
|
|
- scikit-learn==1.4.2
|
|
- matplotlib==3.8.4
|
|
- umap-learn==0.5.12
|
|
- scipy==1.12.0
|
|
- seaborn==0.13.2
|
|
- tqdm==4.66.2
|