Datasets:

SiatBioInf
/

SingleCell-Unseen-Benchmark

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:

Lineage-level screening based on CancerSCEM 2.0 marker genes
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.

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.