To analyze data from the 10x Genomics Xenium platform using Seurat, the process begins with loading the necessary libraries and preparing the dataset. The data is then read into a Seurat object using the `LoadXenium` function, which incorporates the RNA data and spatial information. After filtering out cells with zero counts, normalization is performed using SCTransform, followed by dimensionality reduction techniques such as PCA and UMAP, and clustering of cells based on their expression profiles. Visualization of gene expression and cell segmentation is facilitated through functions like `ImageDimPlot` and `ImageFeaturePlot`.

Subsequent steps involve using the Robust Cell Type Decomposition (RCTD) method to annotate cell types based on spatial context and constructing a niche assay to analyze the spatial relationships between different cell types. This includes grouping cells by their predicted cell type or niche identity, allowing for insights into the composition and organization of the tissue. Various visualization techniques illustrate the results throughout the analysis, providing a comprehensive understanding of the spatially resolved gene expression data.