banksy-merged-v3

To run multisample BANKSY spatial transcriptomics analysis, you apply coordinate staggering to avoid sample overlap, perform HVG filtering, harmonize samples, and execute Leiden clustering. The workflow uses Python libraries like scanpy and anndata for reproducible spatial pattern visualization.

BANKSY spatial clustering integrates spatial neighborhood information with gene expression matrices in AnnData to identify spatial domains. You need it when standard non-spatial clustering fails to capture tissue architecture and spatial patterns across multiple samples.

Yes, you can use Leiden clustering and Harmony integration for multisample spatial transcriptomics datasets. The workflow applies Harmony for batch harmonization across samples and Leiden clustering on the BANKSY matrix to identify spatially coherent cell clusters.

Yes, the BANKSY multisample workflow requires coordinate staggering for Visium and Slide-seq data. Coordinate staggering separates spatial coordinates across multiple samples to prevent sample overlap during joint spatial domain analysis and visualization.

You need numpy, scipy, pandas, anndata, scanpy, igraph, leidenalg, matplotlib, and seaborn to set up BANKSY analysis. These standard data science libraries enable the end-to-end pipeline including PCA, harmonization, Leiden clustering, and spatial visualization.

Multisample spatial transcriptomics clustering shows overlapping spatial coordinates when coordinate staggering is not applied. The BANKSY workflow requires staggering spatial coordinates across samples to prevent overlap before harmonization and joint Leiden clustering.