Spatial Single Cell Analysis in Python
Learning cell communication from spatial graphs of cells
MCP server for spatial transcriptomics analysis through natural language interfaces.
Production framework for 10x Visium, Xenium, and MERFISH spatial transcriptomics. Includes Squidpy spatial neighborhood analysis, ligand-receptor cell communication, and WebGL overlays.
An open-source eduAn open-source educational framework for Spatial Transcriptomics and Single-Cell AI analysis in Python.
This is a pipeline for ingestion, cleaning, and spatial analysis of Akoya PCF images
10X Visium spatial transcriptomics analysis with Squidpy
Spatial transcriptomics analysis using 10x Genomics Visium and Xenium platforms
Scanpy + Squidpy pipeline for 10x Visium spatial transcriptomics: QC, Leiden clustering, marker genes, cell-type annotation, and spatial mapping.
End-to-end spatial transcriptomics pipeline for 10x Genomics Visium human brain glioblastoma — cell type annotation, GBM subtype characterization, and spatial neighborhood analysis
Spatial transcriptomics benchmark on Visium mouse brain — squidpy analysis plus a topological data analysis test showing persistent homology detects spatial structure that Moran's I underestimates
10x Visium spatial transcriptomics pipeline — Squidpy, Moran's I, neighborhood enrichment, TLS-like niche detection | Workflow demonstration | Python
Spatial transcriptomics analysis of human DLPFC Visium data with SVG detection, clustering benchmark, Tangram mapping, and neighborhood analysis.
A spatial transcriptomics agent skill. Not a tool executor — a thinking partner. Three modes named after Greek philosophers: Aristotle (empiricist), Plato (dialectician), Socrates (gadfly). BioMCP-powered.
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