Verified:GitHub

banksy

Orchestrates BANKSY-based spatial transcriptomics analysis from graphs to clustering and visualization in Python and optionally R.

1|Updated Dec 3, 2025

Search Tags:#visualization #clustering #spatial-transcriptomics #AnnData #Leiden #banksy #MCLUST

Ready to run

npx skills add https://github.com/Ketomihine/my_skills --skill banksy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: banksy
Source: https://github.com/Ketomihine/my_skills/tree/main/banksy
Command: npx skills add https://github.com/Ketomihine/my_skills --skill banksy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

Banksy provides a cohesive workflow to analyze spatial transcriptomics data by constructing spatial neighbourhood graphs, generating BANKSY matrices that integrate self and neighbour features, and performing clustering and visualization to reveal tissue structure.

Core Features & Use Cases

Graph construction and weighting: build spatial graphs with fixed neighbours and azimuthal components, then assign distance-based weights.
Matrix fusion and clustering: concatenate self- with neighbour- matrices into BANKSY representations and cluster with Leiden or MCLUST; compare spatial vs non-spatial results.
Visualization and downstream analysis: generate spatial maps, UMAPs, PCA embeddings, and plot connectivity; convert results back to AnnData for downstream analyses.
Use Case: apply BANKSY to a sci data object to identify tissue regions in a Slide-seq or 10x spatial dataset and compare against manual labels.

Quick Start

Load your spatial AnnData object with coordinates.
Run initialize_banksy to create banksy_dict and compute neighbour graphs.
Run generate_banksy_matrix to build the BANKSY matrix and perform clustering with run_Leiden_partition or run_mclust_partition; visualize results with plot_banksy.

Frequently Asked Questions about banksy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is BANKSY spatial transcriptomics analysis used for?▼

BANKSY spatial transcriptomics analysis integrates spatial neighbourhood graphs with gene expression data to construct BANKSY matrices, enabling clustering and visualization to reveal underlying tissue structure and cellular composition.

How do I cluster spatial transcriptomics data using Leiden or MCLUST?▼

You can cluster spatial transcriptomics data by generating a BANKSY matrix that fuses self and neighbour features, then applying Leiden or MCLUST partitioning algorithms to identify spatial domains within the tissue.

Can I use BANKSY clustering with my existing spatial AnnData object?▼

Yes, BANKSY workflows accept spatial AnnData objects containing spatial coordinates, allowing you to compute neighbour graphs, perform clustering, and convert the results back to AnnData for downstream analyses.

What is the best way to visualize spatial domains from Slide-seq datasets?▼

To visualize spatial domains from Slide-seq datasets, apply BANKSY clustering to generate spatial maps and UMAP or PCA embeddings, plotting connectivity to compare spatial results against manual labels.

How do spatial clustering results compare against non-spatial gene expression clustering?▼

BANKSY compares spatial versus non-spatial clustering by concatenating self and neighbour matrices, showing how integrating spatial neighbourhood weighting alters tissue region identification compared to standard gene expression alone.