Pre-computed data for VitaGRN: a unified framework that decrypts transcriptional regulatory rewiring during cellular differentiation and predicts whole-genome single-cell perturbation responses in a zero-shot manner.

📦 Data Packages

VitaGRN provides 10 on-demand data packages. Download only what you need:

Package	Size	Contents	Required For
`core`	~5 MB	TF embeddings (ProTrek 1024-dim), UMAP coords, train/test splits, K562/H9 gene lists	Everything
`model`	~161 MB	Pre-trained BayesianRidge Structure Anchor checkpoint (`branch_a_model.pkl`)	Inference (`vitagrn-eval hybrid`)
`grn_k562`	~41 MB	K562 5-channel GRN scaffolds + 4-tier gold standards	GRN evaluation & scaffold building
`grn_h9`	~759 MB	H9 ESC/NPC phase2 GRN networks	Figure 5 (H9 development)
`perturb_core`	~75 MB	Perturbation predictions (218 TFs × 5000 genes), eval metrics, benchmark results	Perturbation evaluation
`perturb_atlas`	~125 MB	K562 virtual perturbation atlas (mean + std matrices)	Figure 4, Figure 5
`experiments`	~288 MB	Pre-computed figure data (fig2–fig5, ed_fig1–8)	Paper figure reproduction
`grn_bench`	~57 MB	GRN topology benchmark: gold standards + 20 baseline model predictions	GRN Bench evaluation
`perturb_bench`	~171 MB	Perturbation benchmark: ground-truth expression matrices + baseline predictions	Perturb Bench evaluation
`third_party`	~19 MB	STRING v12 PPI (K562), CollecTRI signed edges, JASPAR2024 CORE PWM	Scaffold building (convenience)

🚀 Quick Start

Install HuggingFace CLI

pip install huggingface_hub

Download Specific Packages

# Minimal for inference (~282 MB)
hf download Chris-young-2004/VitaGRN \
    --include "data/embeddings/*" --include "data/splits/*" --local-dir .
hf download Chris-young-2004/VitaGRN \
    --include "data/models/*" --local-dir .
hf download Chris-young-2004/VitaGRN \
    --include "data/grn/k562/*" --local-dir .
hf download Chris-young-2004/VitaGRN \
    --include "data/perturbation/predictions/*" --include "data/perturbation/eval/*" --include "data/perturbation/benchmark/*" --local-dir .

# Or use our downloader script (from the VitaGRN repo)
bash download_data.sh --package core model grn_k562 perturb_core

# Full figure reproduction (~1.7 GB)
bash download_data.sh --all

Using the VitaGRN Downloader

From the VitaGRN repository:

# Show available packages
bash download_data.sh --list

# Download specific packages
bash download_data.sh --package core model grn_k562 perturb_core

# Download everything
bash download_data.sh --all

Files are extracted to their expected locations under data/ and Benchmark/, matching the VitaGRN directory structure exactly.

📁 Directory Structure

When downloaded, files map directly into the VitaGRN repository:

VitaGRN/
├── data/
│   ├── embeddings/          ← core package
│   │   ├── tf_embeddings.npy
│   │   ├── tf_embedding_names_gene_symbol.json
│   │   ├── umap_coords.npy
│   │   └── umap_motif_mask.npy
│   ├── splits/              ← core package
│   │   └── k562_heldout_seed42_for_vitagrn.json
│   ├── gene_list_k562_top5000.txt   ← core package (5000 highly variable K562 genes)
│   ├── gene_list_h9_top500.txt      ← core package (500 highly variable H9 genes)
│   ├── models/              ← model package
│   │   └── branch_a_model.pkl
│   ├── grn/
│   │   ├── k562/            ← grn_k562 package
│   │   │   ├── scaffolds/
│   │   │   │   ├── base_ism_topology_input.csv
│   │   │   │   ├── context_fusion_topology_input.csv
│   │   │   │   ├── grn_skeleton_enhanced_k562_5000genes.csv
│   │   │   │   ├── h3k27ac_topology_input.csv
│   │   │   │   └── ppi_enhanced_topology_input.csv
│   │   │   └── gold_standards/
│   │   │       ├── tier1_collectri.csv
│   │   │       ├── tier2_broad_grn.csv
│   │   │       ├── tier3_functional_all.csv
│   │   │       └── tier4_with_binding.csv
│   │   └── h9/              ← grn_h9 package
│   │       ├── esc_phase2_results.csv
│   │       └── npc_phase2_results.csv
│   ├── perturbation/
│   │   ├── predictions/     ← perturb_core package
│   │   │   ├── predictions_delta_test.csv
│   │   │   └── predictions_delta_test_std.csv
│   │   ├── eval/            ← perturb_core package
│   │   ├── benchmark/       ← perturb_core package
│   │   └── atlas/           ← perturb_atlas package
│   │       ├── k562_virtual_perturbation_mean.csv
│   │       ├── k562_virtual_perturbation_std.csv
│   │       └── k562_virtual_perturbation_metadata.csv
│   ├── experiments/         ← experiments package
│   │   ├── fig2/
│   │   ├── fig3/
│   │   ├── fig4/
│   │   ├── fig5/
│   │   └── ed_fig/
│   ├── ppi/                 ← third_party package
│   │   ├── k562_string_v12_gene_symbol_ppi_edges.csv
│   │   └── collectri_signed_edges.csv
│   └── JASPAR2024/          ← third_party package
│       └── JASPAR2024_CORE_vertebrates_non-redundant_pfms_jaspar.txt
└── Benchmark/
    ├── GRN Bench/data/      ← grn_bench package
    │   ├── gold_standards/
    │   ├── predictions/
    │   └── spaces/
    └── Perturb Bench/data/  ← perturb_bench package
        └── perturbation/benchmark/
            ├── y_true_delta.csv
            ├── y_true_expression.csv
            └── predictions/

🔴 External Data (Download Separately)

These datasets are not hosted in this repository. Users must obtain them from the original sources:

#	Dataset	Target Path	Size	Source
1	hg38 reference genome	`data/genomes/hg38/hg38.fa`	~3 GB	UCSC hg38
2	AlphaGenome checkpoint	Set `ALPHAGENOME_WEIGHTS_PATH`	~701 MB	google-deepmind/alphagenome
3	GENCODE v46 GTF	`data/genomes/hg38/gencode.v46.annotation.gtf`	~50 MB	GENCODE
4	ProTrek 650M model weights	`{PROTREK_PATH}/weights/ProTrek_650M/`	~2.5 GB	westlake-repl/ProTrek_650M_UniRef50
5	K562 Perturb-seq h5ad	Set via `--control_h5ad` / `--perturb_h5ad`	~GBs	CausalBench
6	RPE1 Perturb-seq	Set via CLI flags	~GBs	scPerturb
7	GDSC2 drug response	Set via CLI flags	~MBs	DepMap Portal

Download Instructions

# hg38 reference genome
mkdir -p data/genomes/hg38
wget -P data/genomes/hg38 https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
gunzip data/genomes/hg38/hg38.fa.gz
samtools faidx data/genomes/hg38/hg38.fa

# GENCODE v46 GTF
wget -P data/genomes/hg38 https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/gencode.v46.annotation.gtf.gz
gunzip data/genomes/hg38/gencode.v46.annotation.gtf.gz

# AlphaGenome checkpoint → follow instructions at:
# https://github.com/google-deepmind/alphagenome

# ProTrek 650M model weights (only needed for Layer 2 re-training / sign correction;
# inference with pre-computed embeddings from the core package skips this step)
git lfs install
git clone https://huggingface.co/westlake-repl/ProTrek_650M_UniRef50
export PROTREK_PATH=/path/to/ProTrek_650M_UniRef50

📊 Data Sources & Attribution

All datasets used in this study are publicly available:

Resource	Source	Used For
K562 Perturb-seq	CausalBench	Perturbation response training & evaluation
RPE1 Perturb-seq	scPerturb	Cross-cell-line generalization
GDSC2 drug response	DepMap Portal	Drug response validation
hg38 reference genome	UCSC	Sequence extraction for ISM
JASPAR 2024 motifs	JASPAR	TF binding motif scanning
AlphaGenome weights	Google DeepMind	In-silico mutagenesis predictions
STRING v12 PPI	STRING DB	Protein-protein interaction network
CollecTRI	saezlab/CollecTRI	Gold standard GRN (Tier 1)
TRRUST	grnpedia.org	Gold standard GRN (Tier 2)
BroadGRN	Harmonizome	Gold standard GRN (Tier 2)
ENCODE K562 ChIP-seq	ENCODE	Gold standard GRN (Tier 4)

📖 Citation

If you use VitaGRN data in your research, please cite:

@misc{vitagrn-data,
  title={VitaGRN},
  author={Zhiwen Yang, Sikai Huang, Ge Bai},
  year={2026},
  url={https://huggingface.co/datasets/Chris-young-2004/VitaGRN},
  publisher={Hugging Face}
}

📜 License

This dataset is released under the Apache 2.0 license. Third-party data components (JASPAR, STRING, CollecTRI) are redistributed under their original licenses — users must also comply with the license terms of each upstream data source.

Downloads last month: 125

Total file size:

1.72 GB