Update README.md

README.md

@@ -19,25 +19,67 @@ size_categories:
 # 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.
+
+**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**: 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 Cells
+- **Source**: GEO  
+- **Cancer types**: 21  
+- **Samples**: 2,225  
+- **Cells**: 1,645,662  
+- **Cell states**: Primary tumors, metastases, circulating tumor cells (CTCs)
 
-## 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.
+### Stem Cells
+- **Source**: CELLxGENE  
+- **Datasets**: 5  
+- **Cells**: 325,092  
+- **Stem cell types**: 4
 
-## Downstream Tasks
-Designed to evaluate foundation models across multiple categories:
+### 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**
+
+---
+
+## Tumor Cell Identification 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.
+
+---
 
-| Category | Task | Prediction type |
-|-----------|------|----------------|
+## 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 |
@@ -45,14 +87,113 @@ Designed to evaluate foundation models across multiple categories:
 | 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.
+Models take **high-dimensional cell embeddings** as input and perform prediction using **lightweight downstream classifiers**, isolating representation quality from classifier complexity.
+
+---
 
 ## Benchmark Models
-Evaluated models: **Geneformer, scFoundation, scGPT, UCE, scLONG**  
+
+The following single-cell foundation models are evaluated:
+
+- **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.
+- **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**.
+
+### Directory Structure
+
+SingleCell-Unseen-Benchmark/
+├── tumor/ # Tumor cell datasets (.h5ad)
+├── stem/ # Stem cell datasets (.h5ad)
+├── neural/ # Neural cell datasets (.h5ad)
+├── normal/ # Normal cell datasets (.h5ad)
+├── results/ # Benchmark results
+└── README.md
+
+## Benchmark Results
+
+In addition to raw datasets, we provide **complete benchmark evaluation results** under the `results/` directory.
+
+### Results Organization
+
+results/
+├── by_model/
+│ ├── geneformer/
+│ │ ├── tumor_identification.csv
+│ │ ├── primary_site_tracing.csv
+│ │ └── ...
+│ ├── scfoundation/
+│ ├── scgpt/
+│ ├── uce/
+│ └── sclong/
+└── by_task/
+├── tumor_identification.csv
+├── primary_site_tracing.csv
+├── stem_identification.csv
+└── ...
+
+
+### 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.
+
+
+