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README.md

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+---
+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 sex 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 -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<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 **sex** 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" | "sex" | "cell_type"
+    label_column=args.label_column,          # "disease" | "sex" | "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"` | `"sex"` | `"cell_type"`.
+- **label_column** *(str, required)* — Target label column (e.g., `"disease"`, `"sex"`, `"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
+```
+
+**Sex (AD, brain)**
+```bash
+# Alzheimer's Disease (Take AD as an example)
+python train.py \
+  --downstream_task sex \
+  --label_column sex \
+  --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_sex_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}
+}
+```

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