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[ 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 112.9x smaller.
    • Full collection: about 104.9x smaller.
  • Sample benchmark recorded during build:
    • Open Zarr group: 0.1066 s
    • Read one 1000 x 1000 chunk: 0.0169 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

  • tahoe.zarr
    • Quickstart sample with 150,000 cells.
    • Good for tutorials, schema inspection, and lightweight tests.
  • tahoe_00000.zarr ... tahoe_00013.zarr
    • Full production collection.
    • 14 row-sharded stores.
    • Nominal shard target: 1,000,000 cells.
  • 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 zstd with 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.zarr if you want a fast, self-contained sample for development or demos.
  • Use the tahoe_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

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.zarr is the safer starting point if you want to materialize an AnnData object 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/_index and var/_index in the Zarr stores.
  • dataset_summary.json uses _metadata_unavailable sentinels 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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