| --- |
| 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**. |
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| This strategy ensures consistent tumor labeling while minimizing dataset-specific bias. |
|
|
| --- |
|
|
| ## Downstream Benchmark Tasks |
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|
| 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 |
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|
| 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. |
|
|
| --- |
|
|
| ## Intended Use |
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| This benchmark is intended for: |
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| - 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 |
|
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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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|
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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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