SPATIAL TRANSCRIPTOMICS · TUTORIAL

BANKSY: Using Neighborhood Context to Find Spatial Domains

Why adding just 20% neighbor signal outperforms pure expression clustering for tissue layer detection

July 2026  ·  9 min read  ·  SpatiaBio

Standard Leiden clustering on spatial data ignores one obvious fact: cells that sit next to each other tend to be in the same tissue layer. BANKSY (Building Aggregates with a Neighborhood Kernel for Yeoman Segmentation) fixes this by mixing each cell's own expression with a smoothed average of its neighbors — then clustering on the combined signal. The result is cleaner, more spatially coherent domains without requiring any graph-based post-processing.

Figure 1. BANKSY spatial domain map (left) and performance comparison vs. other methods by ARI and NMI (right).

The Core Idea: Lambda Controls the Mix

BANKSY introduces one key parameter: lambda (0 to 1), which controls how much neighbor context to blend in:

lambda = 0

Pure cell-intrinsic expression. Identical to standard Leiden.

lambda = 0.2

Sweet spot. Adds spatial coherence while preserving cell identity.

lambda = 0.8

Strong spatial smoothing. Best for laminar structures (brain cortex).

The embedding is constructed as: z = (1 - lambda) * x_self + lambda * x_neighbors, where x_neighbors is a weighted mean of expression in the spatial neighborhood (typically k=15-30 nearest neighbors in physical space).

Running BANKSY with Squidpy

import squidpy as sq
import scanpy as sc
import numpy as np
from banksy import Banksy

# Load and preprocess
adata = sc.read_visium('path/to/visium/')
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
adata = adata[:, adata.var.highly_variable]

# Build spatial graph (required for BANKSY)
sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=15)

# Run BANKSY
banksy = Banksy(
    adata,
    banksy_lambda=0.2,   # neighbor mix ratio
    resolution=0.8,       # Leiden resolution
    pca_dims=20,
    n_neighbors=15,
)
banksy.fit()
banksy.plot_domains(adata, color='banksy_labels')

Installation: pip install banksy-py — works with AnnData/Squidpy natively.

BANKSY vs. Other Spatial Domain Methods

	BANKSY	BayesSpace	STAGATE	Leiden only
Approach	Neighbor smoothing + clustering	Bayesian HMRF	Graph autoencoder	Expression only
Speed	Fast	Slow	Slow (GPU)	Fastest
Scalability	Visium HD / Xenium OK	Visium only	Large datasets OK	Any
Interpretability	High (lambda is intuitive)	Low	Low (latent space)	High
Best for	General use, layered tissues	Small datasets, fine structure	Complex, non-laminar	Quick baseline

Practical Tips

• Start with lambda=0.2 for most tissues. Go higher (0.5-0.8) only for clearly laminar structures like brain cortex or retina.
• Run both BANKSY and Leiden and compare — BANKSY domains should look spatially cleaner. If they don't, your spatial graph might be poorly constructed.
• Use sq.gr.spatial_neighbors with coord_type='generic' for Visium. The default 'grid' mode can misrepresent actual tissue distances.
• BANKSY domains are not cell types — they are tissue microenvironments. Combine with cell type deconvolution (cell2location or Tangram) to get the full picture.

Key Takeaways

• BANKSY adds spatial coherence with a single parameter (lambda) on top of standard clustering
• lambda=0.2 is a reliable default; increase for laminar tissues
• Faster and more interpretable than deep learning alternatives for most use cases
• Best used alongside cell type deconvolution, not as a replacement

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