PIASO

Precise Integrative Analysis of Single-cell Omics

PIASO is a Python and Rust toolkit for single-cell omics — scRNA-seq, scATAC-seq and spatial transcriptomics — covering the analysis from raw counts through to the figures in a paper. Performance-critical routines are implemented in Rust; pre-compiled wheels ship for Linux, macOS (Intel and Apple Silicon) and Windows, so there is nothing to build.

import piaso, cosg

piaso.tl.infog(adata, n_top_genes=3000)                       # normalize + select
piaso.tl.runSVD(adata, layer="infog", n_components=50, key_added="X_svd")
piaso.tl.neighbors(adata, use_rep="X_svd"); piaso.tl.leiden(adata)
piaso.tl.umap(adata, use_rep="X_svd")
cosg.cosg(adata, groupby="leiden")                            # marker genes
piaso.pl.embedding(adata, color="leiden")

That whole workflow runs on a plain pip install piaso-tools — no scanpy required.

Who this is for

You have a single-cell dataset and want an analysis, not a toolchain. Reading, QC, normalization, dimensionality reduction, clustering, marker genes, annotation and plotting are one package with one set of conventions.
Your data outgrew memory. The same function calls run on an AnnData in RAM or stream from a cytome file on disk, where peak memory is set by the batch size instead of the cell count. Validated to several million cells.
You care what the figure looks like. The plotting suite and piaso.settings are built for publication figures rather than for quick looks.
You work with a coding agent. The API is published in an agent-readable form (see below), so the agent works from the current signatures.

If you only need one method, the pieces are usable on their own — cosg for markers, cytome for storage.

The ecosystem

PIASO is the analysis layer of a small set of packages that fit together, and each is useful alone:


PIASO	analysis: normalization, dimensionality reduction, clustering, annotation, plotting
cytome	a single-file format for single-cell multi-omics; what PIASO streams from
COSG	fast, accurate marker gene and marker region identification
PIASO-data	genome references and tutorial datasets, fetched and cached on demand
PIASO-for-agents	the ecosystem in a form coding agents can read
LARIS · Emergene	spatial ligand–receptor analysis · per-cell differential analysis across conditions

Documentation

piaso.org — tutorials, API reference and release notes.

Installation

Install from PyPI (stable release):

pip install piaso-tools

This also installs cytome, the on-disk dataset format PIASO reads and writes. Nothing extra to install to work with .cytome files.

Install from bioconda (stable release):

conda install -c conda-forge -c bioconda piaso

Install from GitHub (latest development version):

pip install git+https://github.com/genecell/PIASO.git

Using PIASO with a coding agent

PIASO-for-agents makes the PIASO ecosystem available to coding agents from one canonical knowledge base, generating Claude skills, Cursor rules, AGENTS.md, llms.txt, and an MCP server. Useful if you work in Claude Code, Cursor, Copilot, Codex, Windsurf, Cline, or Aider and want the agent to know the current API rather than guess it.

Any model with web access can be pointed straight at:

https://piaso.org/llms.txt
https://piaso.org/llms-full.txt

Contributing

Issues and pull requests are welcome at github.com/genecell/PIASO. Bug reports are most useful with the output of piaso.__version__ and a minimal example.

Citation

If PIASO is useful for your research, please consider citing Wu, S.J., Dai, M. et al. Pyramidal neurons proportionately alter cortical interneuron subtypes. Nature (2026). https://doi.org/10.1038/s41586-025-09996-8

Contact

Min Dai dai@broadinstitute.org

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