bio-spatial-transcriptomics-spatial-multiomics

Analyze high-resolution spatial transcriptomics data from platforms like Visium HD, Slide-seq, and Stereo-seq by running end-to-end workflows that include binning, cell segmentation, and spatial neighbor graph construction to reveal subcellular patterns.

Yes, you can compute Moran's I statistics and identify spatially variable genes using Squidpy and SpatialData, which support spatial statistics and visualization to map molecular signals to subcellular domains.

Integrate spatial multi-omics data with histology morphology through multi-modal integration workflows that relate gene expression patterns directly to tissue architecture and subcellular domains.

The best way to perform cell segmentation and Leiden clustering on spatial data is to load a high-resolution dataset and run an end-to-end analysis using SpatialData and Squidpy to identify neighborhood relationships.

Yes, you need a properly configured Python runtime environment with compatible versions of tooling such as SpatialData and Squidpy to execute subcellular resolution analyses and spatial multi-omics integration.

Apply binning to subcellular spatial transcriptomics datasets when processing high-density data from platforms like Visium HD or Stereo-seq, enabling accurate spatial statistics and multi-modal integration with histology.