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Scarf

Single Cell Analysis on Remote Filesystems

Tests Coverage Docs PyPI Python 3.12, 3.13, and 3.14 Downloads

[!IMPORTANT] Scarf 1.0 is coming soon. Install the Latest release release candidate with uv pip install --prerelease allow "scarf[extra]". The current stable release on PyPI is 0.32.3.

Scarf is a Python framework for analysing single-cell RNA, ATAC, protein, and multi-omic data, from a few thousand cells to tens of millions.

Problem How Scarf solves it What you get
Your dataset is larger than RAM Out-of-core algorithms, and neighbour search streams from cell-major and gene-major layouts, inside a memory budget you set No subsampling, so rare populations survive, benchmarked to 10M cells
The data is stored remotely and requires downloading Fetches only the chunks an operation touches, and writes results to a store you own Start analysing immediately, with one authoritative copy
A single parameter change costs hours of computation Each step is fingerprinted by its settings and inputs, so reuse is by content, not by layer name Only what changed recomputes, and the old version stays for comparison
Sub-population analysis leaves scattered copies that nobody can trace back Subsets are masks in one file, and every result carries the cells and parameters behind it A year later, a result still explains itself

Install

Python 3.12+.

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

Detailed installation instructions here

Quick start

import scarf

ds = scarf.DataStore(
    "s3://bucket/10M_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",
)
image

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

Documentation

Read workflow vignettes and API references on Read The Docs 📖

AI-assisted and autonomous workflows should start with Analysis with AI agents.

Scarf's capabilities

Area Methods
Modalities scRNA-seq, scATAC-seq, CITE-seq, matched multi-omics
Core workflow Quality control, feature selection, normalization, PCA and LSI, KNN graph, UMAP, densMAP, t-SNE, Leiden, Paris, marker search
Integration Harmony, partial PCA, shared and weighted nearest neighbours, integration metrics
Mapping Symphony-style reference mapping, label transfer, projection diagnostics
Trajectory Population Balance Analysis pseudotime, expression dynamics and modules, multi-sink fate probabilities
Also included Cell-cycle scoring, gene-set activity, graph-diffusion imputation, doublet scores, HTO demultiplexing, TopACeDo downsampling, pseudobulk export

Citation

Dhapola et al. Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data. Nature Communications 13, 4616 (2022).

Support

GitHub issues

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

Project links

Data verified by PyPI on Aug 30, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Stable release available (0.32.3)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for parashardhapola from gravatar.com parashardhapola

Credits

Author: Parashar Dhapola

GitHub Statistics

Data verified by PyPI on Aug 30, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.12

Provides Extra

agent extra test docs

Tags

single-cell scRNA-seq scATAC-seq CITE-seq Harmony WNN batch-correction Zarr memory-efficient

Classifiers

Report project as malware

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