September 2024
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scverse Foundational tools for single-cell omics data analysis
Core packages

Standard for annotated matrices


Multimodal data format


Single-cell analysis framework


Multi-omics analysis framework


Single-cell machine learning framework


Single-cell immune sequencing analysis framework


Spatial single-cell analysis


Spatial data format

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A broader ecosystem of packages builds on the scverse core packages. These tools implement models and analytical approaches to tackle challenges in spatial omics, regulatory genomics, trajectory inference, visualization, and more.

Continue to scverse ecosystem

scverse is a consortium of foundational tools (mostly in Python) for omics data in life sciences. It has been founded to ensure the long-term maintenance of these core tools.

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scverse is a community project currently governed by the developers of the core packages. Please reach out if you’d like to be involved!

Learn more about scverse community

scverse tools are used in numerous research and industry projects across the globe and are referenced in thousands of academic publications. Consider consulting the following references for more information about core scverse libraries and citing the relevant articles when using them in your work:

Virshup I, Bredikhin D, Heumos L, Palla G, Sturm G, Gayoso A, Kats I, Koutrouli M, scverse community, Berger B, Pe'er D, Regev A, Teichmann S, Finotello F, Wolf F, Yosef N, Stegle O, Theis F: The scverse project provides a computational ecosystem for single-cell omics data analysis. Nature Biotechnology. 2023 April 10
Wolf F, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19, 15 (2018)
Bredikhin D, Kats I, Stegle O. MUON: multimodal omics analysis framework. Genome Biology 23, 42 (2022)
Virshup I, Rybakov S, Theis FJ, Angerer P, Wolf FA. anndata: Annotated data. bioRxiv. 2021 Dec 19
Gayoso A, Lopez R, Xing G, Boyeau P, Valiollah Pour Amiri V, Hong J, Wu K, Jayasuriya M, Mehlman E, Langevin M, Liu Y. A Python library for probabilistic analysis of single-cell omics data. Nature Biotechnology. 2022 Feb 7:1-4
Sturm G, Szabo T, Fotakis G, Haider M, Rieder D, Trajanoski Z, Finotello F. Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data. Bioinformatics. 2020 Sep 15;36(18):4817-8
Palla G, Spitzer H, Klein M, Fischer D, Schaar AC, Kuemmerle LB, Rybakov S, Ibarra IL, Holmberg O, Virshup I, Lotfollahi M, Richter S, Theis FJ. Squidpy: a scalable framework for spatial omics analysis. Nature Methods 19, 171–178 (2022)
Marconato L, Palla G, Yamauchi KA, Virshup I, Heidari E, Treis T, Vierdag WM, Toth M, Stockhaus S, Shrestha RB, Rombaut B. SpatialData: an open and universal data framework for spatial omics. Nature Methods. 2024 Mar 20:1-5