DOCSEARCHbyAlgolia
Submit Docs

Seurat Documentation

repository·main·Indexed 25 days ago

https://github.com/satijalab/seurat

An R toolkit for single-cell genomics analysis supporting spatial, multimodal, and scalable workflows. Seurat v5 introduces bridge integration for cross-modality mapping, sketch-based analysis for datasets with millions of cells, and BPCells integration for high-performance data handling. It supports both sequencing-based (e.g., Visium, SLIDE-seq) and imaging-based (e.g., MERFISH, Xenium, CosMX) spatial datasets.

Tokens
712
Snippets
1
Records
7
Agent score
34%

What's inside Seurat

  1. Overview of Seurat

    main
    Seurat is an R package designed for the quality control (QC), analysis, and exploration of single-cell RNA-seq data. It is used to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements and to integrate diverse types of single-cell data. It is released under the MIT license.

  2. Analyze large-scale datasets using Sketch-based analysis

    main

    To handle datasets spanning millions of cells that cannot be fully loaded into memory, Seurat v5 supports 'sketch'-based analysis. This method stores representative subsamples of a large dataset in-memory for rapid, iterative analysis while keeping the full dataset accessible via on-disk storage.

    Key resources for sketch-based workflows:

    • Sketch-based clustering (1.3M cells): articles/seurat5_sketch_analysis.html
    • Sketch-based integration (1M cells): articles/ParseBio_sketch_integration.html
    • Mapping 1.5M cells to Azimuth reference: articles/COVID_SCTMapping.html
    • Interacting with BPCell matrices: articles/seurat5_bpcells_interaction_vignette.html

  3. Perform integrative multimodal analysis with Bridge Integration

    main

    Seurat v5 introduces 'bridge integration', a statistical method to integrate experiments measuring different modalities (e.g., separate scRNA-seq and scATAC-seq datasets) by using a separate multiomic dataset as a molecular 'bridge'. This allows users to map datasets from one modality onto another to assist in interpretation and annotation.

    For detailed workflows, refer to the following guides:

    • Streamlined integration of scRNA-seq data: articles/seurat5_integration.html
    • Cross-modality bridge integration: articles/seurat5_integration_bridge.html

  5. Install Seurat

    main
    Seurat is an R toolkit for single-cell genomics that can be installed on Mac OS X, Linux, and Windows. For the most stable version, use the CRAN release. For the latest development features, use the main branch.

  6. Analyze sequencing and imaging-based spatial datasets

    main

    Seurat v5 provides support for various spatially resolved data types, including:

    • Sequencing-based technologies: e.g., Visium, SLIDE-seq.
    • Imaging-based technologies: e.g., MERFISH/Vizgen, Xenium, CosMX.

    Supported analytical techniques include scRNA-seq integration, deconvolution, and niche identification. Refer to these vignettes for implementation:

    • Sequencing-based spatial analysis: articles/spatial_vignette.html
    • Imaging-based spatial analysis: articles/seurat5_spatial_vignette_2.html