shape list | cell_type_counts dict | disease_labels list | benchmark dict | meta dict |
|---|---|---|---|---|
[
150000,
62710
] | {
"_metadata_unavailable": 0
} | [
"_metadata_unavailable"
] | {
"open_seconds": 0.10663213580846786,
"read_chunk_seconds": 0.01689237169921398,
"chunk_shape": [
1000,
1000
],
"chunk_origin": [
13298,
47761
],
"shape": [
150000,
62710
]
} | {
"n_obs": 8044908,
"n_var": 62710,
"sample_rows": 150000,
"sample_source_shard": "out/full_shards/tahoe_00000.zarr",
"source_format": "h5ad",
"x_chunks": [
1000,
1000
],
"x_compression": {
"clevel": 3,
"cname": "zstd",
"codec": "blosc",
"shuffle": "bitshuffle"
}
} |
Tahoe-100M Zarr Collection
Production-ready tahoe single-cell RNA-seq data exported from Arc Virtual Cell Atlas (Tahoe-100M) 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 tahoe.zarr |
150,000 x 62,710 |
37.63 GB |
333.37 MB |
Small tutorial-sized store you can open immediately. |
Full sharded collection tahoe_00000.zarr ... tahoe_00013.zarr |
100,648,790 x 62,710 |
25.25 TB |
240.58 GB |
Full dataset stays practical because access is chunked and row-sharded. |
- Compression vs dense float32:
- Quickstart sample: about
112.9xsmaller. - Full collection: about
104.9xsmaller.
- Quickstart sample: about
- Sample benchmark recorded during build:
- Open Zarr group:
0.1066 s - Read one
1000 x 1000chunk:0.0169 s
- Open Zarr group:
- 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
tahoe.zarr- Quickstart sample with
150,000cells. - Good for tutorials, schema inspection, and lightweight tests.
- Quickstart sample with
tahoe_00000.zarr...tahoe_00013.zarr- Full production collection.
14row-sharded stores.- Nominal shard target:
1,000,000cells.
dataset_summary.json- Summary statistics for the sample export.
Source And Provenance
- Upstream source: Arc Virtual Cell Atlas (Tahoe-100M)
- Source version:
2025-02-25 - Organism:
Homo sapiens - Filter used for export:
pass_filter == "full" - Source label in store metadata:
arc-virtual-cell-atlas - Random seed recorded in store metadata:
42
This repo is a Zarr packaging of the upstream source data to make browser-friendly, programmatic, chunked access practical on the Hub.
Data Layout
Expression matrix
- Key:
X - Dtype:
float32 - Compression: Blosc
zstdwith bitshuffle - Sample chunks:
(1000, 1000) - Full-store chunks:
(256, 62710)
Observation metadata in full stores
obs/_index
Variable metadata in full stores
var/_index
Recommended Usage
- Use
tahoe.zarrif you want a fast, self-contained sample for development or demos. - Use the
tahoe_000xx.zarrstores for full-scale work. - Process the full collection shard by shard unless you explicitly have the memory budget to combine everything.
- Treat
Xas lazily loaded. Avoid converting the full dataset to one in-memory dense array.
Quick Start
Open the sample store directly from Hugging Face
import fsspec
import zarr
mapper = fsspec.get_mapper(
"hf://datasets/KokosDev/tahoe-100m-zarr@main/tahoe.zarr"
)
root = zarr.open_group(mapper, mode="r")
print(root["X"].shape)
print(root["obs/_index"][:5])
Open one full shard
import fsspec
import zarr
shard = "tahoe_00000.zarr"
mapper = fsspec.get_mapper(
f"hf://datasets/KokosDev/tahoe-100m-zarr@main/{shard}"
)
root = zarr.open_group(mapper, mode="r")
print(shard, root["X"].shape)
print(root["obs/_index"][:5])
Iterate over all full shards
import fsspec
import zarr
for i in range(14):
shard = f"tahoe_{i:05d}.zarr"
mapper = fsspec.get_mapper(
f"hf://datasets/KokosDev/tahoe-100m-zarr@main/{shard}"
)
root = zarr.open_group(mapper, mode="r")
print(shard, root["X"].shape)
Scanpy / AnnData Notes
tahoe.zarris the safer starting point if you want to materialize anAnnDataobject locally.- The full sharded collection is intended for shard-wise workflows, streaming, preprocessing, and atlas-scale analysis.
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
Important Notes
- This repo contains native Zarr stores, not Parquet or CSV exports.
- The quickstart sample and full sharded collection serve different purposes and are both intentionally included.
- This upstream export only exposes
obs/_indexandvar/_indexin the Zarr stores. dataset_summary.jsonuses_metadata_unavailablesentinels for annotation-derived fields so the Hub viewer can infer a stable schema.
Acknowledgements
Built from Arc Virtual Cell Atlas (Tahoe-100M). Please cite and follow upstream terms, licensing, and attribution requirements when using this data in research or products.
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