spatial-statistics

To calculate spatial autocorrelation for genes in omics data, you can compute global and local metrics like Moran's I, Geary's C, and Getis-Ord Gi* using a preprocessed AnnData file containing spatial coordinates.

You can analyze cell co-localization using neighborhood enrichment to quantify how often annotated cell types appear in spatial proximity within spatial transcriptomics datasets.

Point pattern analysis evaluates cluster distribution by calculating Ripley's L function, which identifies whether spatial point patterns deviate from complete spatial randomness across varying distance scales.

Yes, spatial statistics analysis requires preprocessed AnnData files that include spatial coordinates to accurately compute neighborhood enrichment, spatial autocorrelation, and point pattern distributions.

The best way to identify gene expression hot spots within tissue sections is by calculating local spatial autocorrelation metrics such as Getis-Ord Gi* and Local Moran's I.

Yes, this spatial statistics approach works directly with squidpy and scanpy, utilizing anndata structures to perform comprehensive omics analysis and generate matplotlib visualizations.