FuhaiLiAiLab/OmniCellTOSG_Dataset

OmniCellTOSG

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🧭 Overview

OmniCellTOSG is a large-scale Text–Omic Signaling Graph (TOSG) resource for single-cell foundation model pretraining and omics analysis. It combines:

• Expression matrices (sharded .npy for scalable IO)
• Graph topology (full, internal, and PPI edges)
• Textual metadata (entity names, descriptions, sequences) with precomputed embeddings

Supported tasks include graph–language foundation model pretraining, cell-type annotation, disease status and gender classification, plus core signaling inference.

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📁 Folder Layout

OmniCellTOSG_Dataset/
├── expression_matrix/
│   ├── braincellatlas_brain_part_0.npy
│   ├── cellxgene_blood_part_0.npy
│   └── ... (additional *.npy shards)
├── cell_metadata_with_mappings.csv
├── edge_index.npy
├── s_bio.csv
├── s_desc.csv
├── s_name.csv
├── x_bio_emb.npy
├── x_desc_emb.npy
└── x_name_emb.csv

Notes

• Files in expression_matrix/*.npy are sharded partitions of single-cell expression matrices; merge shards (concatenate/stack) to reconstruct the full matrix for a given source/organ.
• cell_metadata_with_mappings.csv contains standardized per-cell annotations (e.g., tissue, disease, sex, cell type, ontology mappings).
• edge_index.npy, s_bio.csv, s_name.csv, and s_desc.csv provide the graph topology (COO [2, E]) and entity metadata (biological sequences, names, descriptions).
• x_bio_emb.npy, x_desc_emb.npy, and x_name_emb.csv are precomputed entity embeddings ([#entities × D], encoder-dependent) aligned to the CSVs—use these to skip on-the-fly embedding.

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⚙️ Installation

pip install git+https://github.com/FuhaiLiAiLab/OmniCellTOSG

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🚀 Quick Start

from CellTOSG_Loader.data_loader import CellTOSGDataLoader

conditions = {"tissue_general": "brain", "disease_name": "Alzheimer's Disease"}

dataset = CellTOSGDataLoader(
    root="/path/to/OmniCellTOSG_Dataset",
    conditions=conditions,
    downstream_task="cell_type",   # "disease" | "gender" | "cell_type"
    label_column="cell_type",
    sample_ratio=0.10,
    shuffle=True,
    balanced=False,
    train_text=False,              # False → use precomputed x_name_emb / x_desc_emb
    train_bio=False,               # False → use precomputed x_bio_emb
    random_state=42,
    output_dir="./Output/data_ad_celltype_0.1",
)

x_all   = dataset.data                 # dict: {x_name_emb, x_desc_emb, x_bio_emb, ...}
y_all   = dataset.labels               # labels aligned to rows
all_edge_index = dataset.edge_index                   # full graph (COO [2, E])
internal_edge_index = dataset.internal_edge_index     # optional transcript–protein edges
ppi_edge_index = dataset.ppi_edge_index               # optional PPI edges     
x_name_emb, x_desc_emb, x_bio_emb = pre_embed_text(args, dataset, pretrain_model, device) # Prepare text and seq embeddings

Parameters (CellTOSGDataLoader)

• root (str, required) — Filesystem path to the dataset root (e.g., /path/to/OmniCellTOSG_Dataset).
• conditions (dict, required) — Row filters over cell metadata
  (e.g., {"tissue_general": "brain", "disease_name": "Alzheimer's Disease"}).
• downstream_task (str, required) — "disease" | "gender" | "cell_type".
• label_column (str, required) — Target label column (e.g., "disease", "gender", "cell_type").
• sample_ratio (float, optional) — Fraction of rows to sample (0–1). Mutually exclusive with sample_size.
• sample_size (int, optional) — Absolute number of rows to sample. Mutually exclusive with sample_ratio.
• balanced (bool, default: False) — Enable class-balancing using task-specific priority labels.
• shuffle (bool, default: False) — Shuffle rows during sampling/composition.
• random_state (int, default: 42) — Seed for reproducibility.
• train_text (bool, default: False) — If False, return precomputed name/desc embeddings; if True, return raw text (s_name, s_desc) for custom embedding.
• train_bio (bool, default: False) — If False, return precomputed sequence embeddings; if True, return raw sequences (s_bio) for custom embedding.
• output_dir (str, optional) — Directory for loader-generated artifacts (splits, logs, cached subsets).

Returns (typical):

• x, y: features and labels for the selected split
• edge_index, internal_edge_index, ppi_edge_index: graph topological information
• Either raw text/sequence fields (s_name, s_desc, s_bio) or their precomputed embeddings (x_name_emb, x_desc_emb, x_bio_emb), returned according to the train_text/train_bio flags.

---

🧪 Pretraining

python pretrain.py

---

🏋️ Training Examples (CLI)

Disease status (AD, brain)

# Alzheimer's Disease (Take AD as an example)
python train.py \
  --downstream_task disease \
  --label_column disease \
  --tissue_general brain \
  --disease_name "Alzheimer's Disease" \
  --sample_ratio 0.1 \
  --train_base_layer gat \
  --train_lr 0.0005 \
  --train_batch_size 3 \
  --train_test_random_seed 42 \
  --dataset_output_dir ./Output/data_ad_disease_0.1

Gender (AD, brain)

# Alzheimer's Disease (Take AD as an example)
python train.py \
  --downstream_task gender \
  --label_column gender \
  --tissue_general brain \
  --disease_name "Alzheimer's Disease" \
  --sample_ratio 0.1 \
  --train_base_layer gat \
  --train_lr 0.0005 \
  --train_batch_size 2 \
  --train_test_random_seed 42 \
  --dataset_output_dir ./Output/data_ad_gender_0.1

Cell type annotation (LUAD, lung)

# Lung (LUAD) (Take LUAD as an example)
python train.py \
  --downstream_task cell_type \
  --label_column cell_type \
  --tissue_general "lung" \
  --disease_name "Lung Adenocarcinoma" \
  --sample_ratio 0.2 \
  --train_base_layer gat \
  --train_lr 0.0001 \
  --train_batch_size 3 \
  --train_test_random_seed 42 \
  --dataset_output_dir ./Output/data_luad_celltype_0.2

Signaling inference

python analysis.py

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⚖️ Licensing & Attribution

This dataset aggregates data from CellxGENE, the Brain Cell Atlas, and GEO. Use of these resources is governed by their respective terms and citation policies:

• CellxGENE Terms of Service — Follow the platform’s ToS for data access, reuse, and sharing.
  🔗 https://cellxgene.cziscience.com/tos

• Brain Cell Atlas (citation required)
  Cite:
  Xinyue Chen#, Yin Huang#, Liangfeng Huang#, Ziliang Huang#, Zhao-Zhe Hao#, Lahong Xu, Nana Xu, Zhi Li, Yonggao Mou, Mingli Ye, Renke You, Xuegong Zhang, Sheng Liu*, Zhichao Miao*. A brain cell atlas integrating single-cell transcriptomes across human brain regions. Nat Med (2024). https://doi.org/10.1038/s41591-024-03150-z

• GEO Citation Policy — Follow NCBI GEO guidelines for citing datasets and third-party analyses.
  🔗 https://www.ncbi.nlm.nih.gov/geo/info/citations.html#third-party

Note: You are responsible for ensuring compliance with the licenses/terms and for providing appropriate attribution to each source in any publications or derived works.

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📚 Citation

If you use OmniCellTOSG, please cite:

@misc{omnicelltosg2025,
  title  = {OmniCellTOSG: A Text–Omic Signaling Graph Dataset for Single-Cell Learning},
  author = {Zhang, Heming and Li, Fuhai and collaborators},
  year   = {2025},
  note   = {Dataset on Hugging Face},
  url    = {https://huggingface.co/FuhaiLiAiLab}
}