README.md

---
pretty_name: OmniCellTOSG
dataset_name: omnicelltosg
dataset_summary: |
  OmniCellTOSG is a large-scale Text–Omic Signaling Graph (TOSG) dataset for single-cell learning.
  It integrates sharded expression matrices, graph topology (full/internal/PPI edges), and textual
  entity metadata (names, descriptions, sequences) with optional precomputed embeddings. It supports
  graph-aware pretraining and downstream tasks such as cell-type annotation, disease status, and gender classification.

# 🏷️ Taxonomy (use standard HF enums where possible)
annotations_creators: [no-annotation]
language_creators: [found]
language: [en]
multilinguality: [monolingual]
source_datasets: [original, external]
size_categories: [">1M"]   # change if needed: n<1K | 1K<n<10K | 10K<n<100K | 100K<n<1M | >1M

# 📚 Tasks (use “other” if your task isn’t in HF’s standard list)
task_categories: [other]
task_ids:
  - multi-label-classification
  - explanation-generation

# 🔖 Tags (free-form keywords)
tags:
  - single-cell
  - transcriptomics
  - foundation-models

# 📄 Licensing & attribution
license: other
license_url:
  - https://cellxgene.cziscience.com/tos
  - https://doi.org/10.1038/s41591-024-03150-z
  - https://www.ncbi.nlm.nih.gov/geo/info/citations.html#third-party

# 🔗 Project links (optional but recommended)
homepage: https://github.com/FuhaiLiAiLab/OmniCellTOSG
repository: https://github.com/FuhaiLiAiLab/OmniCellTOSG
paper: "https://arxiv.org/pdf/2504.02148"
point_of_contact: "Heming Zhang"

# 🧩 Dataset structure hints (optional)
dataset_type: multimodal-graph
configs:
  - config_name: default
    data_files: cell_metadata_with_mappings.csv

# ✅ Maintenance
pretty_format: true
---


# OmniCellTOSG

<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
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<div align="center">
  <img src="https://github.com/FuhaiLiAiLab/OmniCellTOSG/blob/main/Figures/OmniCell-logo.png?raw=true" width="55%" alt="OmniCellTOSG Logo" />
</div>

<div align="center">
  <a href="https://github.com/FuhaiLiAiLab/OmniCellTOSG">
    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-OmniCellTOSG-181717?logo=github">
  </a>
  <a href="https://huggingface.co/FuhaiLiAiLab">
    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-FuhaiLiAiLab-ffcc00?color=ffcc00&logoColor=white">
  </a>
  <a href="https://arxiv.org/pdf/2504.02148" target="_blank">
    <img alt="Paper" src="https://img.shields.io/badge/arXiv-2504.02148-b31b1b?logo=arxiv&logoColor=white">
  </a>
</div>

---

## 🧭 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**.

<div align="center">
  <img src="https://github.com/FuhaiLiAiLab/OmniCellTOSG/blob/main/Figures/Figure2.png?raw=true" alt="Dataset Overview" />
</div>

---

## 📁 Dataset Structure

```text
OmniCellTOSG_Dataset/
├── expression_matrix/
│ ├── braincellatlas_brain_part_0.npy
│ ├── braincellatlas_brain_part_1.npy
│ ├── cellxgene_blood_part_0.npy
│ ├── cellxgene_blood_part_1.npy
│ ├── cellxgene_lung_part_0.npy
│ ├── cellxgene_small_intestine_part_0.npy
│ └── ... (additional *.npy shards)
├── cell_metadata_with_mappings.csv
├── cell_metadata_with_mappings.parquet
├── edge_index.npy
├── internal_edge_index.npy
├── ppi_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**.

---

## ⚙️ Installation

If you only need dataset loading/extraction, download the standalone loader package from the [Releases](https://github.com/FuhaiLiAiLab/OmniCellTOSG/releases/tag/v2.1.0) page.

---

## 🚀 Quick Start

```python
from CellTOSG_Loader import CellTOSGDataLoader

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

ddataset = CellTOSGDataLoader(
    root=args.dataset_root,
    conditions=conditions,
    task=args.task,                          # "disease" | "gender" | "cell_type"
    label_column=args.label_column,          # "disease" | "gender" | "cell_type"
    sample_ratio=args.sample_ratio,          # mutually exclusive with sample_size
    sample_size=args.sample_size,
    shuffle=args.shuffle,
    stratified_balancing=args.stratified_balancing,
    extract_mode=args.extract_mode,          # "inference" | "train"
    random_state=args.random_state,
    train_text=args.train_text,
    train_bio=args.train_bio,
    correction_method=args.correction_method, # None | "combat_seq"
    output_dir=args.output_dir,
)

# --- Access outputs ---
if args.extract_mode == "inference":
    X = dataset.data                         # pandas.DataFrame (expression/features)
    y = dataset.labels                       # pandas.DataFrame
    metadata = dataset.metadata              # pandas.DataFrame (row-aligned metadata)
else:
    X = dataset.data                         # dict: {"train": X_train, "test": X_test}
    y = dataset.labels                       # dict: {"train": y_train, "test": y_test}
    metadata = dataset.metadata              # dict: {"train": meta_train, "test": meta_test}

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., `../OmniCellTOSG/CellTOSG_dataset_v2`).
- **conditions** *(dict, required)* — Metadata filters used to subset rows  
  (e.g., `{"tissue_general": "brain", "disease": "Alzheimer's disease"}`).
- **task** *(str, required)* — Downstream task type: `"disease"` | `"gender"` | `"cell_type"`.
- **label_column** *(str, required)* — Target label column (e.g., `"disease"`, `"gender"`, `"cell_type"`).
- **extract_mode** *(str, required)* — Extraction mode:  
  - `"inference"`: extract a single dataset for inference/analysis (no train/test split)  
  - `"train"`: extract a training-ready dataset and generate splits (e.g., train/test)
- **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`.
- **shuffle** *(bool, default: `False`)* — Shuffle rows during sampling/composition.
- **stratified_balancing** *(bool, default: `False`)* — Enable stratified sampling / class balancing based on `label_column`.
- **random_state** *(int, default: `2025`)* — Random seed for reproducibility (sampling, shuffling, splitting).
- **train_text** *(bool, default: `False`)* — Controls text feature output:  
  - `False`: return precomputed text embeddings (if available)  
  - `True`: return raw text fields for custom embedding
- **train_bio** *(bool, default: `False`)* — Controls biological sequence feature output:  
  - `False`: return precomputed sequence embeddings (if available)  
  - `True`: return raw sequences for custom embedding
- **correction_method** *(str or None, default: `None`)* — Correction method:  
  - `None`: no correction  
  - `"combat_seq"`: apply ComBat-Seq
- **output_dir** *(str, optional)* — Directory for loader outputs (extracted expression matrix, label，splits).

> **Returns:**
> - `extract_mode="inference"`:  
>   - `dataset.data`: `pandas.DataFrame`  
>   - `dataset.labels`: `pandas.DataFrame`  
>   - `dataset.metadata`: `pandas.DataFrame`
> - `extract_mode="train"`:  
>   - `dataset.data`: `dict` (`{"train": X_train, "test": X_test}`)  
>   - `dataset.labels`: `dict` (`{"train": y_train, "test": y_test}`)  
>   - `dataset.metadata`: `dict` (`{"train": meta_train, "test": meta_test}`)
> - `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

```bash
python pretrain.py
```

---

## 🏋️ Training Examples (CLI)

**Disease status (AD, brain)**
```bash
# 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 \
  --random_state 42 \
  --dataset_output_dir ./Data/train_ad_disease_0.1_42
```

**Gender (AD, brain)**
```bash
# 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 \
  --random_state 42 \
  --dataset_output_dir ./Data/train_ad_gender_0.1_42
```

**Cell type annotation (LUAD, lung)**
```bash
# 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 \
  --random_state 42 \
  --dataset_output_dir ./Data/train_luad_celltype_0.2_42
```

**Signaling inference**
```bash
python analysis.py
```

---

## ⚖️ Licensing & Attribution

This dataset aggregates data from **CellxGENE**, the **Brain Cell Atlas**, **GEO** and **HCA**. 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

- **HCA Data Use Agreement**  
  🔗 https://data.humancellatlas.org/about/data-use-agreement

> **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.

---

## 📚 Citation

If you use **OmniCellTOSG**, please cite:

```bibtex
@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}
}
```