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README.md

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+---
+license: mit
+task_categories:
+  - other
+tags:
+  - biology
+  - gene-regulatory-networks
+  - single-cell
+  - glioblastoma
+  - perturbation
+  - grn-inference
+size_categories:
+  - 10K<n<100K
+---
+
+# scRegNet Data & Results
+
+Processed data and results for **scRegNet**: perturbation-calibrated gene representations
+for context-specific gene regulatory network inference in glioblastoma.
+
+**Code**: [github.com/mmTheBest/scRegNet](https://github.com/mmTheBest/scRegNet)
+
+## Contents
+
+### Processed Data (`data/processed/`)
+
+| File | Description | Size |
+|------|-------------|------|
+| `gene_universe.json` | G\* gene universe (18,716 genes) | 252 KB |
+| `hgfb_raw.npz` | H-GFB features (18,716 × 1,152) | 83 MB |
+| `microglia_labels.npz` | Microglia perturbation labels (38 regulators) | 800 KB |
+| `k562_labels.npz` | K562 perturbation labels (9,611 regulators) | 19 MB |
+| `embeddings/*.npz` | 6 embedding sources (18,716 × 256 each) | 116 MB |
+| `atlas/celltype_means.npz` | Cell-type mean expression (11 types) | 784 KB |
+| `atlas/neftel_subtypes.npz` | Neftel subtype means (6 subtypes) | 3.2 MB |
+| `validation/*.json` | ChIP-Atlas, DoRothEA, TRRUST, canonical TFs | 9 MB |
+| `validation/depmap_ceres.npz` | DepMap CERES scores (50 GBM lines) | — |
+| `validation/msigdb_genesets.json` | MSigDB gene sets (7,540 sets) | — |
+
+### Results (`results/`)
+
+| Directory | Description |
+|-----------|-------------|
+| `stage_b/` | Stage B model outputs (6 embeddings + 3 controls) |
+| `stage_c/` | Stage C GRN predictions (6 sources × 17 contexts, parquet) |
+| `competing/` | 8 competing method outputs |
+| `experiments/exp_1-9/` | All 9 experiment results (JSON) |
+| `figures/` | 7 main + 6 extended figures (PDF + PNG) |
+
+### Not Included (Regeneratable)
+
+- Raw count matrices (`data/processed/stage_c/`, 4.3 GB) — regenerate from GEO GSE274546
+- Per-cell expression matrices (`data/processed/atlas/celltype_expr/`, 1.8 GB) — regenerate via `05a_prepare_atlas.py`
+
+## Usage
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download everything
+snapshot_download("mm1109/scRegNet", repo_type="dataset", local_dir="./scRegNet-data")
+
+# Load embeddings
+import numpy as np
+data = np.load("scRegNet-data/data/processed/embeddings/geneformer.npz")
+embeddings = data["embeddings"]  # (18716, 256)
+genes = data["genes"]            # (18716,)
+
+# Load GRN predictions
+import pandas as pd
+grn = pd.read_parquet("scRegNet-data/results/stage_c/geneformer.parquet")
+```
+
+## License
+
+MIT