10x Genomics Support/Loupe Browser/Analysis/

Loupe Browser Spatial Tutorials

Loupe Browser tutorials review the major analysis capabilities available for Visium Spatial datasets.

Follow the instruction on the Installation page to download and install OS-specific software (Windows or macOS).

Click on the SpatialTutorial.cloupe file from the list of Recent Files. The file opens in a new window with the mouse brain image and the spots overlayed on top of the image. The spots are color-coded by the cluster that the spot was assigned to.

The SpatialTutorial was generated from fresh frozen mouse brain tissue section. A brightfield image of the hematoxylin & eosin (H&E) stained tissue section was acquired. Refer to the dataset page for more about the imaging and sequencing details. From Loupe Browser v6.1.0 and later, the embedded .cloupe file was generated by Space Ranger v1.3 pipeline which was run with automatic fiducial detection and image alignment arguments. The embedded tutorial in older versions of Loupe Browser was generated using Space Ranger v1.0. Several other Spatial Gene Expression datasets are publicly hosted and available for download. These datasets include the .cloupe file that you can use to visualize the results. Visit the Spatial Gene Expression datasets page.

  • Manual Fiducial Alignment: Use Loupe Browser guided fiducial alignment and tissue selection tools (version 4.0 and later) to generate the manual alignment file in JSON format necessary for spaceranger count pipeline.
  • Manual CytAssist Image Alignment: Use the Loupe Browser-guided landmark selection and auto refine tools (version 6.2 onwards) to align the CytAssist captured image on Visium slide with a microscope image of the same tissue section on a standard glass slide to generate an alignment JSON file necessary for spaceranger count pipeline.
  • Navigation: Understand the key features of the interface, the different views, modes, and data selector panels as well as export and import options.
  • Evaluate Gene Markers: Use gene markers of known cell type to view their spatial distribution and explore the use of gene expression independent spatial enrichment metric Moran's I (version 5.1 and later) to evaluate spatiality.
  • Explore Spatial Clusters: Understand gene expression data in a spatial context and use gene lists to create custom categories and evaluate differential gene expression between them.
  • Characterize Substructure: Identify subpopulations within clusters in the dataset and create complex Boolean filters to locate regional subfields within a cluster.
  • Spatial Reclustering: Reanalyze spatial datasets for clusters or regions of interest by customizing clustering and projection parameters to identify new insights.
  • Sharing Results: Save features of interest, export data tables, and plots from the Visium Spatial data.