Detects spatially variable genes, spatial autocorrelation, and cell-type colocalization for spatial transcriptomics using Squidpy with PySAL/esda for local statistics. Use when choosing an SVG method by its null and scaling (SpatialDE/SPARK GP variance-component vs SPARK-X/nnSVG linear vs Moran/Geary graph autocorrelation); separating genes that are spatially variable because of cell-type composition from genes regulated within a cell type; choosing the right autocorrelation statistic (global Moran/Geary vs Getis-Ord hot/cold spots vs local LISA and its FDR trap); and choosing a colocalization null strong enough to defeat the abundance/compartment confound (conditional or toroidal vs the weak Squidpy default permutation).
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryContent & writingStars0
About this skill
Detects spatially variable genes, spatial autocorrelation, and cell-type colocalization for spatial transcriptomics using Squidpy with PySAL/esda for local statistics. Use when choosing an SVG method by its null and scaling (SpatialDE/SPARK GP variance-component vs SPARK-X/nnSVG linear vs Moran/Geary graph autocorrelation); separating genes that are spatially variable because of cell-type composition from genes regulated within a cell type; choosing the right autocorrelation statistic (global Moran/Geary vs Getis-Ord hot/cold spots vs local LISA and its FDR trap); and choosing a colocalization null strong enough to defeat the abundance/compartment confound (conditional or toroidal vs the weak Squidpy default permutation).
Quick start
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Detects spatially variable genes, spatial autocorrelation, and cell-type colocalization for spatial transcriptomics using Squidpy with PySAL/esda for local statistics. Use when choosing an SVG method by its null and scaling (SpatialDE/SPARK GP variance-component vs SPARK-X/nnSVG linear vs Moran/Geary graph autocorrelation); separating genes that are spatially variable because of cell-type composition from genes regulated within a cell type; choosing the right autocorrelation statistic (global Moran/Geary vs Getis-Ord hot/cold spots vs local LISA and its FDR trap); and choosing a colocalization null strong enough to defeat the abundance/compartment confound (conditional or toroidal vs the weak Squidpy default permutation).
About this listing
Category
Content & writing
Reported license
Not specified
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.