CyteArc

CyteArc is a fast Python framework for single-cell RNA, ATAC, protein, and multi-omic analysis that scales to 100M+ cells. In benchmarks, it processed one million cells within 15 minutes, from cell filtering through UMAP and clustering to marker gene identification. On Tahoe-100M, the same workflow ran in under 20 hours without any subsampling.

Cytebase is a ready-to-connect repository of CyteArc datastores, containing approximately 200 million cells and 1.1 TB of data on Hugging Face. Mount remote datasets for reanalysis and build or reuse references for mapping new cells, without downloading the complete count matrix. Counts stay remote while you save your own results in a store you control.

CyteArc's built-in provenance connects saved results to the exact cells, features, parameters, and upstream computations that produced them, so you can reuse completed work and trace each analysis.

Install

Python 3.12+.

uv venv --python 3.12
uv pip install --python .venv "cytearc[extra]"

Detailed installation instructions here

Quick start

import cytearc

ds = cytearc.DataStore(
    "s3://bucket/cells.zarr",  # also gs://, hf://, or a local path
)
run = ds.pipeline.run()  # durable QC → graph → UMAP → clustering → marker run

ds.plots.embedding(
    run=run,
    layout="umap",
    color_by="clusters",
)

Read the scRNA-seq tutorial for a granular workflow, or remote stores for cloud setups.

Cytebase

Cytebase provides a searchable catalog of public datasets stored as cloud-hosted CyteArc DataStores. Search by study, tissue, disease, or organism, then explore published cell annotations and available embeddings. Public access needs no credentials.

Install the Cytebase extra alongside the analysis extras:

uv pip install --python .venv --prerelease allow "cytearc[cytebase,extra]"

from cytearc import cytebase

catalog = cytebase.Catalog()
matches = catalog.search("kidney")
print(matches)

# Open one of the matching datasets for read-only exploration.
dataset_id = matches[0]["cytebase_id"]
ds = catalog.open_datastore(dataset_id)

# Create a writable local store for your own analysis, keeping counts remote.
analysis = catalog.mount_datastore(dataset_id, at="kidney_analysis.zarr")

Use analysis with the pipeline shown above to save new results locally. Computation runs in your Python environment, reading count blocks over the network as needed.

Follow the Cytebase tutorial for catalog filters, plotting, and example notebooks, or see the Cytebase API reference.

Documentation

Read workflow vignettes and API references in the documentation.

CyteArc's capabilities

Support

GitHub issues

CyteArc is open source software released under the BSD 3-Clause License and maintained by Nygen.