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Datasets:
mm1109
/
scRegNet

Tasks:
Other
Size:
10K<n<100K
Tags:
biology
gene-regulatory-networks
single-cell
glioblastoma
perturbation
grn-inference
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'validation' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/mm1109/scRegNet@1667fe191686bb68fdb87a1f5d13f5809f6aae54/data/processed/validation/chipatlas_targets.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 195, in _generate_tables
                  raise ValueError(
              ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/mm1109/scRegNet@1667fe191686bb68fdb87a1f5d13f5809f6aae54/data/processed/validation/chipatlas_targets.json.

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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
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