| --- |
| license: cc-by-nc-2.0 |
| library_name: diffusers |
| tags: |
| - transcriptomics |
| - bioinformatics |
| - gene-expression |
| - genomics |
| datasets: |
| - silicobio/hoike_normal_expression_GTEx_Analysis_v10_log2tpmplus1 |
| - silicobio/hoike_condition_expression_TCGA_various_log2tpmplus1 |
| --- |
| |
| # Hōʻike: A Joint-Embedding Predictive Architecture for Transcriptome Data Generation with Diffusion Models |
|
|
| <h3 align="right">Silico Biosciences</h3> |
|
|
| Visit the GitHub repository for the full framework code: https://github.com/silicobio/hoike |
|
|
|
|
| ## Usage |
|
|
| ```py |
| ## 1. Look up the condition samples for a tissue that exists in the normal reference set. |
| user_target_tissue = "Skin" |
| condition_subset = dataset.condition_df[dataset.condition_df["tissue_type"] == user_target_tissue].reset_index(drop=True) |
| normal_baseline_array = dataset.normal_profiles[user_target_tissue] |
| |
| ## 2. Generate with sampling-time normalization consistent with diffusion training. |
| generated_df = generate_synthetic_condition_data_consistent( |
| normal_profile=normal_baseline_array, |
| jepa=jepa_model, |
| diffusion=diff_model, |
| scheduler=scheduler, |
| gene_cols=dataset.gene_cols, |
| num_samples=2500, |
| value_cap=condition_value_cap, |
| sampling_noise_scale=1.1, |
| ) |
| ``` |
