---
license: cc-by-4.0
language:
- en
task_categories:
- other
tags:
- biology
- single-cell
- scRNA-seq
- h5ad
- cancer
- tumor
- benchmark
- bioinformatics
size_categories:
- 1M<n<10M
---
# SingleCell-Unseen-Benchmark

## Overview

**SingleCell-Unseen-Benchmark** is a large-scale unseen single-cell transcriptomic benchmark designed to systematically evaluate foundation models on cell identification and cell type tracing tasks.  
The benchmark covers **tumor, stem, neural, and normal cell populations**, with a particular emphasis on **unseen data distributions**, including rare cell types, cross-dataset generalization, and heterogeneous tumor states.

In addition to curated datasets, this repository provides **standardized benchmark results** for multiple single-cell foundation models, enabling transparent and reproducible comparison.

---

## Dataset Collection

### Tumor Cells
- **Source**: GEO  
- **Cancer types**: 21  
- **Samples**: 2,225  
- **Cells**: 1,645,662  
- **Cell states**: Primary tumors, metastases, circulating tumor cells (CTCs)

### Stem Cells
- **Source**: CELLxGENE  
- **Datasets**: 5  
- **Cells**: 325,092  
- **Stem cell types**: 4

### Neural Cells
- **Source**: CELLxGENE  
- **Datasets**: 1  
- **Cells**: 423,707  
- **Neural cell types**: 6

### Normal Cells
- **Source**: CELLxGENE  
- **Datasets**: 7  
- **Cells**: 1,838,991  
- **Normal cell types**: 10

### Preprocessing
- All genes were mapped to **HGNC symbols**
- Cells with fewer than **200 detected genes** were removed
- Expression matrices are stored in **AnnData (`.h5ad`) format**

---

## Cell Type and Malignancy Annotation Strategy

Tumor cells derived from GEO were re-identified using a **consensus workflow**:

1. **Lineage-level screening** based on **CancerSCEM 2.0** marker genes  
2. **Malignancy confirmation** using **inferCNV**

CELLxGENE-derived datasets retain their **original annotations**.

This strategy ensures consistent tumor labeling while minimizing dataset-specific bias.

---

## Downstream Benchmark Tasks

The benchmark evaluates foundation models across multiple biologically meaningful tasks:

| Category | Task | Prediction Type |
|--------|------|-----------------|
| Tumor | Tumor cell identification | Binary |
| Tumor | Primary site tracing | Multi-class |
| Stem | Stem cell identification | Binary |
| Stem | Stem cell subtype classification | Multi-class |
| Neural | Neural cell identification | Binary |
| Neural | Neural cell subtype classification | Multi-class |

Models take **high-dimensional cell embeddings** as input and perform prediction using **lightweight downstream classifiers**, isolating representation quality from classifier complexity.

---

## Benchmark Models

The following single-cell foundation models are evaluated:

- **Geneformer**
- **scFoundation**
- **scGPT**
- **UCE**
- **scLONG**

---

## Evaluation Metrics

- **Binary classification tasks**
  - Accuracy
  - Precision
  - Recall
  - F1-score

- **Multi-class classification tasks**
  - Accuracy
  - Macro-Precision
  - Macro-Recall
  - Macro-F1

---

## Data Format and Access

### Data Files

All datasets are provided in **AnnData (`.h5ad`) format**.

> **Note**  
> `.h5ad` files are not natively supported by the Hugging Face Dataset Viewer.  
> Users are expected to download the files and load them locally using standard single-cell analysis tools such as **Scanpy** or **Seurat**.


## Benchmark Results

In addition to raw datasets, we provide **complete benchmark evaluation results** under the `results/` directory.


### Design Rationale

- **`by_model/`**  
  Provides a **model-centric view**, facilitating analysis of how a single model performs across different tasks.

- **`by_task/`**  
  Provides a **task-centric view**, enabling direct comparison of multiple models on the same task.

Both views contain **identical information** and are provided to improve usability, clarity, and reproducibility.

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## Intended Use

This benchmark is intended for:

- Evaluating **generalization and robustness** of single-cell foundation models  
- Studying **tumor cell identification and origin tracing** under unseen conditions  
- Benchmarking representation quality across diverse biological contexts  

The dataset is **not intended for clinical decision-making**.

---

## Citation

If you use this dataset or benchmark in your work, please cite: 


## Contact

For questions, issues, or suggestions, please open an issue on the Hugging Face repository.