@scverse

scverse 

scverse is an open-source Python ecosystem for single-cell and spatial omics data analysis. It provides shared data structures, interoperable analysis frameworks, and strong community support for researchers working with high-throughput molecular data. The ecosystem enables scalable, modular, and reproducible workflows for transcriptomics, epigenomics, multimodal, and spatial omics analyses.

Key benefits

  • Interoperable data structures: AnnData, MuData, and SpatialData enable seamless integration across tools in the Python single-cell ecosystem.
  • Scalable analysis: Supports datasets with >1 million cells; GPU acceleration available via rapids-singlecell.
  • Community-driven development: Public forum, chat, regular community meetings, and training courses.
  • Modular and extensible: Independent packages for gene expression, spatial analysis, multimodal integration, deep learning, perturbation analysis, and immune repertoire analysis.

Applications

  • Single-cell analysis: cell type identification, trajectory inference, clustering, differential expression, multimodal integration (RNA-seq, ATAC-seq, etc.).
  • Spatial omics: tissue organization, cellular niches, and cell–cell communication analysis.
  • Multi-condition and multi-batch integration at large scale.
  • Deep probabilistic modeling and graph-based workflows.
  • Perturbation and pathway analysis including enrichment and transcription factor activity inference.

Intended use

scverse is intended for computational biologists, bioinformaticians, and experimental life scientists working with single-cell or spatial omics data. Basic Python programming skills and familiarity with single-cell or spatial omics concepts are recommended.

Consulting and community support are provided through the public Discourse forum (discourse.scverse.org), Zulip chat, community meetings, and training courses.

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Multi-institutional open-source project fiscally sponsored by NumFOCUS.