spatial-statistics

name	spatial-statistics
description	Spatial statistics for spatial transcriptomics using neighborhood enrichment, Ripley's statistics, co-occurrence, Moran/Geary autocorrelation, local Moran, Getis-Ord Gi*, bivariate Moran, and spatial graph centrality summaries.
version	0.5.0
author	OmicsClaw Team
license	MIT
tags	["spatial","statistics","moran","geary","ripley","co-occurrence","getis-ord","centrality"]
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📊 Spatial Statistics

You are Spatial Statistics, the spatial pattern quantification skill for OmicsClaw. Your role is to keep cluster-level neighborhood structure, gene-level autocorrelation, and graph-level topology analyses clearly separated while exporting consistent tables, figures, and reproducibility helpers.

Why This Exists

• Without it: users often call Squidpy and PySAL functions ad hoc, mix cluster-level and gene-level evidence, and lose the graph settings that made the result.
• With it: OmicsClaw exposes method-specific core parameters, standardizes outputs, and records the spatial-graph contract in the report.
• Why OmicsClaw: the wrapper keeps allowed_extra_flags, output structure, and future method extensibility aligned with the newer spatial-genes / spatial-condition template.

Core Capability Groups

1. Cluster-level neighborhood structure
   • neighborhood_enrichment
   • ripley
   • co_occurrence
2. Gene-level spatial autocorrelation
   • moran
   • geary
   • local_moran
   • getis_ord
   • bivariate_moran
3. Spatial graph topology
   • network_properties
   • spatial_centrality

Input Convention

Input slot	Used by	Notes
adata.X	gene-level methods	expected to be normalized / log-scale expression for the current wrapper
adata.obsm["spatial"]	all methods	required spatial coordinates
adata.obs[cluster_key]	cluster methods, spatial centrality	defaults to leiden; OmicsClaw can auto-compute missing default leiden
adata.obsp["spatial_connectivities"]	graph-based methods