README.md

---
license: apache-2.0
task_categories:
- biology
- genomics
- tabular-classification
- tabular-regression
tags:
- single-cell
- scRNA-seq
- transcriptomics
- perturbation
- batch-integration
- cell-type-annotation
- anndata
size_categories:
- 100K<n<1M
---



# 🧬 ScDiVa Benchmark Datasets

[**📄 arXiv Paper**](https://arxiv.org/abs/2602.03477) | [**💻 Model Repository**](https://huggingface.co/warming666/ScDiVa) | [**🌐 Project Page**](https://github.com/warming666/ScDiVa)


## 📖 Dataset Description

This repository contains the **pre-processed benchmark datasets** used to evaluate **ScDiVa** (Single-cell Deep Variational Analysis). All datasets are stored in `.h5ad` (AnnData) format, compatible with `scanpy` and `anndata`.

These datasets cover four major downstream tasks in single-cell genomics:
1.  **Batch Integration**
2.  **Cell Type Annotation** (Fine-tuning & Zero-shot)
3.  **Gene Perturbation Prediction**
4.  **Gene Regulatory Network (GRN) Reconstruction**

---

## 📂 File Organization

The datasets are categorized by their primary evaluation task in the ScDiVa paper.

### 1. Batch Integration & Atlas Building
*Datasets containing multiple batches with technical effects to be removed.*

| Filename | Description | Cells |
| :--- | :--- | :--- |
| `pbmc12k_processed.h5ad` | PBMC dataset with 2 batches. | ~12k |
| `immune_processed.h5ad` | Human Immune Atlas (large-scale integration). | ~300k |
| `bmmc_processed.h5ad` | Bone Marrow Mononuclear Cells (NeurIPS 2021). | ~90k |
| `perirhinal_processed.h5ad` | Mouse Brain Perirhinal Cortex. | ~23k |
| `covid19_processed.h5ad` | PBMC from COVID-19 patients. | ~45k |

### 2. Cell Type Annotation (Fine-tuning)
*Datasets used for supervised training and evaluation of cell type classifiers.*

| Filename | Description |
| :--- | :--- |
| `hpancreas_processed.h5ad` | Human Pancreas dataset (State-of-the-art benchmark). |
| `ms_processed.h5ad` | Multiple Sclerosis dataset. |
| `myeloid_processed.h5ad` | Myeloid cells (Set A). |
| `myeloid_b_processed.h5ad` | Myeloid cells (Set B). |

### 3. Zero-shot Annotation
*Datasets used to test the model's generalization capability without task-specific training.*

| Filename | Description |
| :--- | :--- |
| `Pancrm.h5ad` | Human Pancreas (distinct from hPancreas). |
| `PBMC.h5ad` | Standard PBMC benchmark. |
| `PBMC_368K.h5ad` | Large-scale PBMC dataset. |
| `HumanPBMC.h5ad` | Additional human PBMC variant. |
| `Cell_Lines.h5ad` | Cell line benchmarking data. |
| `DC.h5ad` | Dendritic Cells. |
| `MCA.h5ad` | Mouse Cell Atlas subset. |
| `immune_processed.h5ad` | **Human Immune Atlas** (Used for large-scale zero-shot evaluation). |

### 4. Gene Perturbation Prediction
*Datasets for causal inference tasks (predicting gene expression after CRISPR perturbation).*

| Filename | Description |
| :--- | :--- |
| `adamson_processed.h5ad` | Single-gene CRISPRi perturbations (Adamson et al., 2016). |
| `norman.h5ad` | Combinatorial (double-gene) CRISPRa perturbations (Norman et al., 2019). |

### 5. Reconstruction & GRN Inference
*Datasets used to evaluate the model's ability to reconstruct gene expression (Section 4.2) and infer regulatory networks (Section 4.6).*

| Filename | Description |
| :--- | :--- |
| `zheng68k_processed.h5ad` | Mouse hematopoietic stem and progenitor cell scRNA-seq dataset (68k cells). |
| `hpancreas_processed.h5ad` | Human Pancreas dataset. |
| `pbmc12k_processed.h5ad` | PBMC dataset with 2 batches. |
| `immune_processed.h5ad` | Human Immune Atlas(Primary dataset for GRN inference). |

---

## 🚀 How to Use

Since these are `.h5ad` files, we recommend using `huggingface_hub` to download the specific file you need, and then loading it with `scanpy`.

### Prerequisites
```bash
pip install scanpy huggingface_hub

```

### Loading a Dataset

```python
import scanpy as sc
from huggingface_hub import hf_hub_download

# Example: Load the PBMC12k dataset for Batch Integration
file_path = hf_hub_download(
    repo_id="warming666/ScDiVa",
    filename="pbmc12k_processed.h5ad",
    repo_type="dataset"
)

adata = sc.read_h5ad(file_path)

print(adata)
# AnnData object with n_obs × n_vars = 11990 × 2000 ...

```

---

## ⚠️ Data Processing Notes

* **Normalization**: Most datasets labeled `_processed` have typically undergone library size normalization (e.g., target sum 10,000) and log1p transformation. Please check the `adata.X` values or `adata.uns` for specific processing details.
* **Splits**: Train/Test splits, if applicable, are usually stored in `adata.obs['split']` or indicated by boolean masks (e.g., `train_mask`).
* **Gene Vocabulary**: To work with the pre-trained ScDiVa model, ensure the gene symbols in `adata.var_names` match the model's vocabulary (41,818 genes).

---

## 📄 Citation

If you use these processed datasets in your work, please cite the ScDiVa paper:

```bibtex
@article{wang2026scdiva,
  title={ScDiva: Masked Discrete Diffusion for Joint Modeling of Single-Cell Identity and Expression},
  author={Wang, Mingxuan and Chen, Cheng and Jiang, Gaoyang and Ren, Zijia and Zhao, Chuangxin and Shi, Lu and Ma, Yanbiao},
  journal={arXiv preprint arXiv:2602.03477},
  year={2026}
}

```