tooluniverse-spatial-transcriptomics

What it does

Analyzes spatial transcriptomics data to map gene expression within tissue architecture, identifying spatial domains, spatially variable genes, and cell-cell proximity. It supports integrating spatial data with single-cell RNA-seq, and includes workflows for clustering, neighborhood analysis, ligand-receptor spatial interactions, and tissue-focused reports. The skill emphasizes analyzing spatial organization, tissue morphology, tumor microenvironment structure, and integration with scRNA-seq data for comprehensive tissue analysis.

How it works

• Data Import: Loads spatial transcriptomics data (e.g., 10x Visium, MERFISH, seqFISH, Slide-seq, Xenium) and tissue images. Provides Python examples for loading Visium data with an AnnData object and accessing spatial coordinates and tissue images.
• Quality Control: Performs spot-level QC, alignment verification, and filtering of low-quality spots based on gene counts, UMI counts, and mitochondrial content.
• Preprocessing & Normalization: Applies gene filtering, total-count normalization, log transformation, and optional spatial smoothing. Stores raw counts for reference.
• Spatial Clustering: Conducts PCA, builds spatial neighbor graphs, and runs Leiden clustering with spatial constraints to define spatial domains. Visualizes domains on the tissue.
• Spatial Variable Genes: Computes Moran's I-based spatial autocorrelation to identify spatially patterned genes, and ranks them by spatial significance.
• Neighborhood Analysis: Defines spatial neighborhoods and assesses enrichment of cell types or domains in proximity; identifies interaction zones and niche regions.
• Integration with scRNA-seq: Performs deconvolution to map cell type abundances per spot, and visualizes spatial distributions of cell types.
• Spatial Cell Communication: Identifies proximal cell-type pairs and scores ligand-receptor interactions using a ligand-receptor database (OmniPath) and spatial proximity data; highlights interaction hotspots.
• Spatial Reporting & Visualization: Generates tissue-domain overviews, maps of spatially variable genes, cell-type spatial maps, interaction networks, and 3D visualizations if applicable.

When to use it

Use when you have spatial transcriptomics data and questions about tissue architecture, spatial organization, or cell–cell interactions in tissue context. Applicable for identifying spatial domains, spatial gene expression patterns, tumor microenvironment structure, and integrating spatial data with scRNA-seq annotations. Example triggers include requests to identify spatial domains in a dataset, find spatially variable genes, map tumor microenvironment structure, or integrate spatial data with scRNA-seq data.

What it can touch

• Tools referenced: 0