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---
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}
}
```
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