spatial-statistics

To compute Moran's I for spatial autocorrelation, run spatial statistics on a spatial transcriptomics dataset to identify spatially structured gene expression. The Skill calculates Moran's I across a spatial graph and stores the results in adata.uns for downstream interpretation.

Yes, you can measure cell-type clustering and co-occurrence in tissue data using Ripley's K/L. The spatial statistics analysis assesses distance-dependent proximities and dispersion of cell-type distributions, outputting the results directly into adata.uns.

Yes, spatial statistics analysis requires an AnnData object with spatial coordinates in adata.obs and a specific cell-type annotation stored in adata.obs['celltype']. You also need squidpy and scanpy installed to execute the computations.

The best way to rank cell types by their influence within a spatial graph is to compute centrality scores. This spatial statistics analysis calculates centrality scores to quantify cell-type influence and stores the results in adata.uns and adata.obs.

Yes, you can visualize spatial patterns and gene expression after computing spatial statistics. The analysis outputs results into adata.uns and adata.obs, enabling you to generate visualizations to interpret spatial autocorrelation and cell-type distributions.

Yes, this spatial statistics analysis works with single-slice spatial transcriptomics datasets. It is applicable to datasets with gene or protein modalities and cell-type annotations, computing spatial statistics to quantify spatial patterns in tissue data.