| --- |
| license: other |
| tags: |
| - single-cell |
| - rna-seq |
| - scRNA-seq |
| - h5ad |
| - anndata |
| - genomics |
| - benchmark |
| - shape-analysis |
| size_categories: |
| - 1M<n<10M |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| pretty_name: scShapeBench |
| --- |
| |
| # scShapeBench |
|
|
| A benchmark dataset for single-cell shape analysis, containing **2,547,517 cells** across **105 real-world single-cell RNA-seq datasets** in AnnData (`.h5ad`) format. |
|
|
| ## Dataset Summary |
|
|
| scShapeBench is a curated collection of real-world single-cell gene expression datasets assembled for benchmarking computational methods in single-cell shape analysis. Each dataset is stored as an individual AnnData file with precomputed PCA embeddings and Leiden clustering. |
|
|
| - **Total cells:** 2,547,517 |
| - **Total datasets:** 105 |
| - **Total size:** ~116 GB |
| - **Format:** AnnData (.h5ad) |
| - **Data type:** Real-world (synthetic data to be added in a future release) |
|
|
| ## Dataset Structure |
|
|
| ``` |
| scShapeBench/ |
| ├── data/ |
| │ ├── SCD-0001.h5ad |
| │ ├── SCD-0002.h5ad |
| │ ├── ... |
| │ └── SCD-0112.h5ad |
| ├── cell_metadata.csv # Combined cell-level metadata (2.5M rows) |
| ├── gene_metadata.csv # Combined gene-level metadata |
| ├── dataset_index.csv # Per-file summary: dimensions, size |
| ├── croissant.json # Croissant 1.1 metadata |
| └── README.md |
| ``` |
|
|
| ### File Naming |
|
|
| Files are named `SCD-XXXX.h5ad` where XXXX is a zero-padded index. The numbering is not contiguous (e.g., SCD-0031, SCD-0032, SCD-0034–0036 are absent). |
|
|
| ### Per-File Contents |
|
|
| Each `.h5ad` file contains: |
|
|
| | Component | Description | |
| |-----------|-------------| |
| | `X` | Gene expression matrix (cells × genes), log-normalized | |
| | `obs` | Cell metadata: `n_genes`, `leiden` (cluster assignment) | |
| | `var` | Gene metadata: `gene_ids`, `feature_types`, `genome`, `n_cells`, `highly_variable`, etc. | |
| | `obsm['X_pca']` | Precomputed PCA embeddings | |
| | `uns` | Clustering and HVG parameters | |
|
|
| ### Dataset Index |
|
|
| The `dataset_index.csv` file provides per-file summary statistics: |
|
|
| | Column | Description | |
| |--------|-------------| |
| | `file_id` | Dataset identifier (e.g., SCD-0001) | |
| | `filename` | Filename | |
| | `n_cells` | Number of cells | |
| | `n_genes` | Number of genes | |
| | `file_size_bytes` | File size in bytes | |
|
|
| Dataset sizes range from 1,163 cells (SCD-0006) to 434,340 cells (SCD-0037). |
|
|
| ## Usage |
|
|
| ```python |
| import scanpy as sc |
| import pandas as pd |
| |
| # Load a single dataset |
| adata = sc.read_h5ad("data/SCD-0001.h5ad") |
| print(adata) |
| |
| # Browse available datasets |
| index = pd.read_csv("dataset_index.csv") |
| print(index.sort_values("n_cells", ascending=False).head()) |
| |
| # Load cell metadata across all datasets |
| cell_meta = pd.read_csv("cell_metadata.csv") |
| print(cell_meta.groupby("file_id").size()) |
| ``` |
|
|
| ## Croissant Metadata |
|
|
| This dataset includes a `croissant.json` file conforming to the [Croissant 1.1](https://docs.mlcommons.org/croissant/) metadata standard. This enables programmatic discovery and loading of dataset metadata through compatible tools. |
|
|
| ## Citation |
|
|
| <!-- Add citation information here when available --> |
|
|
| ## License |
|
|
| License to be determined. |
|
|
