README.md

metadata

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

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

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