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
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 metadata standard. This enables programmatic discovery and loading of dataset metadata through compatible tools.
Citation
License
License to be determined.