Datasets:

scShape-Benchmark
/

scShapeBench

license: cc-by-4.0
tags:
  - single-cell
  - rna-seq
  - scRNA-seq
  - h5ad
  - anndata
  - genomics
  - benchmark
  - shape-analysis
  - synthetic
size_categories:
  - 1M<n<10M
task_categories:
  - tabular-classification
  - tabular-regression
pretty_name: scShapeBench
configs:
  - config_name: scRNAseq
    data_files:
      - split: train
        path: data/scRNAseq/*.h5ad
  - config_name: sample
    data_files:
      - split: train
        path: data/sample/*.h5ad
  - config_name: synthetic
    data_files:
      - split: train
        path: data/synthetic/*.npz
  - config_name: annotations
    data_files:
      - split: train
        path: labels/annotations.parquet
  - config_name: labels
    data_files:
      - split: train
        path: labels/labels.parquet

scShapeBench

A benchmark dataset for single-cell shape analysis with four configurations: real-world scRNA-seq data, synthetic data, annotator labels, and aggregated labels.

Dataset Summary

scShapeBench is a curated collection of datasets assembled for benchmarking computational methods in single-cell shape analysis. It is organized into four configurations:

scRNAseq

Real-world single-cell gene expression datasets. Each dataset is stored as an individual AnnData file with precomputed PCA embeddings and Leiden clustering.

Total cells: 2,547,517
Total datasets: 102
Total size: ~116 GB
Format: AnnData (.h5ad)

synthetic

Synthetically generated single-cell data for controlled benchmarking.

Format: NumPy compressed array (.npz) + per-sample metadata (.json)

annotations

Per-dataset shape labels from 9 independent annotators. Each annotator assigned one or more shape categories to each dataset they reviewed.

Total datasets labeled: 97
Annotators: 9
Shape categories: archetypal, multi_branch, simple_traj, clusters
Format: Parquet, long format (one row per dataset–annotator pair)

labels

Aggregated shape labels derived from the 9 annotator labels using three strategies described in the paper.

Total datasets: 97
Aggregations: majority, soft, confidence_weighted, union
Format: Parquet, long format (one row per dataset–aggregation pair); values are floats in [0, 1] per shape category

Dataset Structure

scShapeBench/
├── data/
│   ├── scRNAseq/
│   │   ├── SCD-0001.h5ad
│   │   ├── SCD-0002.h5ad
│   │   ├── ...
│   │   └── SCD-0112.h5ad
│   └── synthetic/
│       ├── sample_00000.npz
│       ├── sample_00000.json
│       ├── ...
├── labels/
│   ├── annotations.parquet  # Per-annotator shape labels (9 annotators)
│   └── labels.parquet       # Aggregated labels (majority, soft, confidence_weighted, union)
├── 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

scRNAseq: 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).

synthetic: Files are named sample_XXXXX.npz / sample_XXXXX.json with a zero-padded 5-digit index.

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

scRNAseq config

import scanpy as sc
import pandas as pd

# Load a single dataset
adata = sc.read_h5ad("data/scRNAseq/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())

synthetic config

import numpy as np
import json

# Load a single synthetic sample
data = np.load("data/synthetic/sample_00000.npz")
meta = json.load(open("data/synthetic/sample_00000.json"))

annotations config

import pandas as pd

annotations = pd.read_parquet("labels/annotations.parquet")
# columns: dataset_id, annotator_id, archetypal, multi_branch, simple_traj, clusters

# Fraction of annotators who labeled a dataset as multi_branch
agreement = annotations.groupby("dataset_id")["multi_branch"].mean()

labels config

import pandas as pd

labels = pd.read_parquet("labels/labels.parquet")
# columns: dataset_id, aggregation, archetypal, multi_branch, simple_traj, clusters

# Get majority-vote labels (binary)
majority = labels[labels["aggregation"] == "majority"].set_index("dataset_id")

# Get union labels (any annotator assigned the class)
union = labels[labels["aggregation"] == "union"].set_index("dataset_id")

Sample Data

The full scRNAseq configuration is ~~116 GB. A representative sample (~~2.6 GB) is available at data/sample/ and accessible as the sample config:

from datasets import load_dataset
ds = load_dataset("scShape-Benchmark/scShapeBench", "sample")

It contains 9 .h5ad files selected to span the range of dataset sizes present in the full benchmark:

File	Cells	Size
`SCD-0006.h5ad`	1,163	61 MB
`SCD-0014.h5ad`	1,886	57 MB
`SCD-0015.h5ad`	1,973	57 MB
`SCD-0010.h5ad`	8,686	56 MB
`SCD-0002.h5ad`	8,368	227 MB
`SCD-0052.h5ad`	9,669	211 MB
`SCD-0062.h5ad`	7,976	223 MB
`SCD-0071.h5ad`	18,890	858 MB
`SCD-0074.h5ad`	21,225	823 MB

Selection criteria: Files were chosen to cover three size tiers — small (<80 MB, 4 files), medium (200–230 MB, 3 files), and large (>800 MB, 2 files) — so reviewers can inspect data at each scale without downloading the full corpus. Within each tier, files were selected by ascending file size from the full dataset_index.csv. All lightweight metadata files (labels/, dataset_index.csv, cell_metadata.csv, gene_metadata.csv) are available in full regardless of which scRNAseq files are downloaded.

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

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to share and adapt the material for any purpose, provided appropriate credit is given.