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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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task_categories: |
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- other |
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tags: |
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- biology |
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- single-cell |
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- scRNA-seq |
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- h5ad |
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- cancer |
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- tumor |
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- benchmark |
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- bioinformatics |
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size_categories: |
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- 1M<n<10M |
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--- |
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# SingleCell-Unseen-Benchmark |
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## Overview |
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**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. |
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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. |
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In addition to curated datasets, this repository provides **standardized benchmark results** for multiple single-cell foundation models, enabling transparent and reproducible comparison. |
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## Dataset Collection |
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### Tumor Cells |
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- **Source**: GEO |
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- **Cancer types**: 21 |
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- **Samples**: 2,225 |
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- **Cells**: 1,645,662 |
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- **Cell states**: Primary tumors, metastases, circulating tumor cells (CTCs) |
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### Stem Cells |
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- **Source**: CELLxGENE |
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- **Datasets**: 5 |
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- **Cells**: 325,092 |
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- **Stem cell types**: 4 |
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### Neural Cells |
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- **Source**: CELLxGENE |
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- **Datasets**: 1 |
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- **Cells**: 423,707 |
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- **Neural cell types**: 6 |
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### Normal Cells |
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- **Source**: CELLxGENE |
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- **Datasets**: 7 |
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- **Cells**: 1,838,991 |
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- **Normal cell types**: 10 |
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### Preprocessing |
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- All genes were mapped to **HGNC symbols** |
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- Cells with fewer than **200 detected genes** were removed |
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- Expression matrices are stored in **AnnData (`.h5ad`) format** |
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--- |
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## Cell Type and Malignancy Annotation Strategy |
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Tumor cells derived from GEO were re-identified using a **consensus workflow**: |
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1. **Lineage-level screening** based on **CancerSCEM 2.0** marker genes |
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2. **Malignancy confirmation** using **inferCNV** |
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CELLxGENE-derived datasets retain their **original annotations**. |
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This strategy ensures consistent tumor labeling while minimizing dataset-specific bias. |
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--- |
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## Downstream Benchmark Tasks |
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The benchmark evaluates foundation models across multiple biologically meaningful tasks: |
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| Category | Task | Prediction Type | |
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|--------|------|-----------------| |
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| Tumor | Tumor cell identification | Binary | |
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| Tumor | Primary site tracing | Multi-class | |
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| Stem | Stem cell identification | Binary | |
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| Stem | Stem cell subtype classification | Multi-class | |
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| Neural | Neural cell identification | Binary | |
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| Neural | Neural cell subtype classification | Multi-class | |
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Models take **high-dimensional cell embeddings** as input and perform prediction using **lightweight downstream classifiers**, isolating representation quality from classifier complexity. |
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--- |
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## Benchmark Models |
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The following single-cell foundation models are evaluated: |
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- **Geneformer** |
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- **scFoundation** |
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- **scGPT** |
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- **UCE** |
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- **scLONG** |
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--- |
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## Evaluation Metrics |
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- **Binary classification tasks** |
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- Accuracy |
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- Precision |
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- Recall |
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- F1-score |
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- **Multi-class classification tasks** |
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- Accuracy |
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- Macro-Precision |
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- Macro-Recall |
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- Macro-F1 |
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--- |
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## Data Format and Access |
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### Data Files |
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All datasets are provided in **AnnData (`.h5ad`) format**. |
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> **Note** |
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> `.h5ad` files are not natively supported by the Hugging Face Dataset Viewer. |
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> Users are expected to download the files and load them locally using standard single-cell analysis tools such as **Scanpy** or **Seurat**. |
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## Benchmark Results |
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In addition to raw datasets, we provide **complete benchmark evaluation results** under the `results/` directory. |
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### Design Rationale |
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- **`by_model/`** |
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Provides a **model-centric view**, facilitating analysis of how a single model performs across different tasks. |
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- **`by_task/`** |
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Provides a **task-centric view**, enabling direct comparison of multiple models on the same task. |
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Both views contain **identical information** and are provided to improve usability, clarity, and reproducibility. |
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--- |
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## Intended Use |
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This benchmark is intended for: |
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- Evaluating **generalization and robustness** of single-cell foundation models |
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- Studying **tumor cell identification and origin tracing** under unseen conditions |
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- Benchmarking representation quality across diverse biological contexts |
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The dataset is **not intended for clinical decision-making**. |
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--- |
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## Citation |
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If you use this dataset or benchmark in your work, please cite: |
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## Contact |
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For questions, issues, or suggestions, please open an issue on the Hugging Face repository. |
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