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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+# SingleCell-Unseen-Benchmark
+
+## Overview
+A large-scale unseen single-cell transcriptomic benchmark covering tumor, stem, neural, and normal cell populations for evaluating single-cell foundation models.
+
+## Dataset Collection
+- **Tumor cells**: 21 cancer types from GEO (2,225 samples, 1,645,662 cells), including primary tumors, metastases, and circulating tumor cells (CTCs).  
+- **Stem cells**: 5 datasets from CELLxGENE (325,092 cells, 4 stem cell types).  
+- **Neural cells**: 1 dataset from CELLxGENE (423,707 cells, 6 neural cell types).  
+- **Normal cells**: 7 datasets from CELLxGENE (1,838,991 cells, 10 normal cell types).
+
+All datasets were mapped to HGNC symbols, and cells with <200 detected genes were removed.
+
+## Tumor Cell Identification
+- GEO-derived tumor cells were re-identified using a consensus workflow: lineage-level screening based on **CancerSCEM 2.0 markers**, followed by malignancy confirmation using **inferCNV**.  
+- CELLxGENE-derived datasets used original annotations.
+
+## Downstream Tasks
+Designed to evaluate foundation models across multiple cell types:
+
+| Cell type | 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 embeddings** as input and predict cell types using lightweight classifiers.
+
+## Benchmark Models
+Evaluated models: **Geneformer, scFoundation, scGPT, UCE, scLONG**  
+
+## Evaluation Metrics
+- Binary tasks: Accuracy, Precision, Recall, F1  
+- Multi-class tasks: Accuracy, Macro-Precision, Macro-Recall, Macro-F1
+
+## Usage
+Datasets can be loaded via Scanpy or other single-cell analysis frameworks.