README.md

---
license: mit
pretty_name: Single-Cell Brain Zarr Collection
tags:
  - biology
  - bioinformatics
  - single-cell
  - scrna-seq
  - zarr
  - scanpy
  - anndata
  - cellxgene
---

# Single-Cell Brain Zarr Collection

Production-ready brain single-cell RNA-seq data exported from the CellxGene Census into native Zarr stores for chunked, on-demand access on the Hugging Face Hub.

## Why Zarr

Single-cell expression matrices get impractical fast if you treat them like ordinary dense files. Zarr is the point of this repo: it makes large atlas-scale data usable without forcing users to download or materialize the whole matrix before they can do anything useful.

| View | Matrix shape | Dense float32 equivalent | Zarr size on Hub | Why it matters |
|---|---:|---:|---:|---|
| Quickstart sample `brain.zarr` | `150,000 x 61,497` | `36.90 GB` | `52.52 MB` | Small tutorial-sized store you can open immediately. |
| Full sharded collection `brain_00000.zarr` ... `brain_00028.zarr` | `28,967,109 x 61,497` | `7.13 TB` | `38.14 GB` | Full dataset stays practical because access is chunked and row-sharded. |

- Compression vs dense float32:
  - Quickstart sample: about `703x` smaller.
  - Full collection: about `187x` smaller.
- Sample benchmark recorded during build:
  - Open Zarr group: `0.0014 s`
  - Read one `1000 x 1000` chunk: `0.0175 s`
- Practical speed difference:
  - Before Zarr: users are pushed toward full-file or full-matrix workflows.
  - After Zarr: users can open the store, inspect metadata, and read only the chunks they need.

## What Is In This Repo

- `brain.zarr`
  - Quickstart sample with `150,000` cells.
  - Good for tutorials, schema inspection, and lightweight tests.
- `brain_00000.zarr` ... `brain_00028.zarr`
  - Full production collection.
  - `29` row-sharded stores.
  - Most shards contain `1,000,000` cells; the final shard contains `967,109`.
- `dataset_summary.json`
  - Summary statistics for the sample export.

## Source And Provenance

- Upstream source: CellxGene Census API
- Census version: `2025-11-08`
- Organism: `Homo sapiens`
- Filter used for export: `tissue_general == 'brain' and is_primary_data == True`
- Source label in store metadata: `cellxgene-census`
- Random seed recorded in store metadata: `42`

This repo is a Zarr packaging of the upstream Census data to make browser-friendly, programmatic, chunked access practical on the Hub.

## Data Layout

### Expression matrix

- Key: `X`
- Dtype: `float32`
- Compression: Blosc `zstd` with bitshuffle
- Sample chunks: `(1000, 1000)`
- Full-store chunks: `(256, 61497)`

### Observation metadata in full stores

- `obs/_index`
- `obs/assay`
- `obs/cell_type`
- `obs/dataset_id`
- `obs/disease`
- `obs/donor_id`
- `obs/n_counts`
- `obs/n_genes`
- `obs/pct_mito`
- `obs/sex`
- `obs/tissue`

### Variable metadata in full stores

- `var/_index`
- `var/feature_id`
- `var/feature_name`
- `var/feature_type`

## Recommended Usage

- Use `brain.zarr` if you want a fast, self-contained sample for development or demos.
- Use the `brain_000xx.zarr` stores for full-scale work.
- Process the full collection shard by shard unless you explicitly have the memory budget to combine everything.
- Treat `X` as lazily loaded. Avoid converting the full dataset to one in-memory dense array.

## Quick Start

### Open the sample store directly from Hugging Face

```python
import fsspec
import zarr

mapper = fsspec.get_mapper(
    "hf://datasets/KokosDev/single-cell-brain-zarr@main/brain.zarr"
)
root = zarr.open_group(mapper, mode="r")

print(root["X"].shape)
print(root["obs/_index"][:5])
```

### Open one full shard

```python
import fsspec
import zarr

shard = "brain_00000.zarr"
mapper = fsspec.get_mapper(
    f"hf://datasets/KokosDev/single-cell-brain-zarr@main/{shard}"
)
root = zarr.open_group(mapper, mode="r")

print(shard, root["X"].shape)
print(root["obs/cell_type"][:5])
print(root["obs/n_counts"][:5])
```

### Iterate over all full shards

```python
import fsspec
import zarr

for i in range(29):
    shard = f"brain_{i:05d}.zarr"
    mapper = fsspec.get_mapper(
        f"hf://datasets/KokosDev/single-cell-brain-zarr@main/{shard}"
    )
    root = zarr.open_group(mapper, mode="r")
    print(shard, root["X"].shape)
```

## Scanpy / AnnData Notes

- `brain.zarr` is the safer starting point if you want to materialize an `AnnData` object locally.
- The full `29`-shard collection is intended for shard-wise workflows, streaming, preprocessing, and atlas-scale analysis.
- QC-style columns are already included in full stores:
  - `n_counts`
  - `n_genes`
  - `pct_mito`

## Intended Use

- Single-cell analysis and preprocessing
- Training and evaluation pipelines for biology ML workloads
- Large-scale feature extraction or embedding jobs
- Benchmarking chunked I/O and Hub-based data access
- Scanpy / AnnData workflows that need a small sample plus a scalable full dataset path

## Important Notes

- This repo contains native Zarr stores, not Parquet or CSV exports.
- The quickstart sample and the full sharded collection serve different purposes and are both intentionally included.
- If you are benchmarking or building loaders, prefer the sharded stores for realistic large-scale access patterns.

## Acknowledgements

Built from the CellxGene Census. Please cite and follow the upstream Census terms, licensing, and attribution requirements when using this data in research or products.