spatial-transcriptomics

To analyze 10X Visium data, integrate gene expression matrices with spatial coordinates using Squidpy and Scanpy to perform quality control, normalization, and spatial clustering for tissue domain identification.

Detecting spatially variable genes in tissue maps is achieved by applying SpatialDE and NaiveDE to spatially-resolved transcriptomics data, allowing you to identify genes exhibiting significant spatial expression patterns across the tissue architecture.

You can map single-cell reference atlases to spatial transcriptomics data by performing deconvolution using Cell2Location or GraphST, integrating high-plex spatial coordinates with single-cell gene expression profiles to resolve complex tissue architectures.

This workflow supports cell-cell communication analysis in a spatial context by utilizing Liana to study cellular interactions within spatially-resolved transcriptomics data, mapping how cells communicate across distinct tissue domains.

Processing Xenium and MERFISH high-plex spatial data involves loading the spatial transcriptomics datasets into anndata structures, integrating precise spatial coordinates with gene expression matrices to visualize complex tissue architectures and identify domains.

You need Scanpy and Anndata to perform neighborhood enrichment on tissue maps, as Squidpy relies on these foundational libraries to process spatial coordinates and gene expression matrices for spatial neighborhood analysis.