Introduction

Cellular neighborhood analysis aims to identify and characterize microenvironments with specific cell compositions and spatial distribution patterns within tissues. By analyzing cellular neighborhoods, we can gain deeper insights into cell-cell interactions, tissue structure, and microenvironmental changes under disease conditions. It also provides a classification basis for differential gene analysis and cell communication analysis.

Module Overview

Mainly divided into two modes: Single Cell Type Mode and Cell Type Score Mode

Single Cell Type Mode:

• For each spatial spot, based on its cell type (e.g., CAF, T cell, etc.), a neighborhood window is defined (can be a fixed radius or a fixed number of neighbors)

• The composition ratio of each cell type within each window is calculated

• Based on these cell composition data, all spots or specified target cell types are clustered to identify different cellular neighborhoods (CN)

Cell Type Score Mode:

• Directly use the cell type scores of each spatial spot (e.g., abundance/probability scores for each cell type obtained by cell2location, etc.)

• Based on these score matrices, all spots are clustered to identify different cellular neighborhoods (CN)

Reference

• Kuppe, C., Ramirez Flores, R. O., Li, Z., Hayat, S., Levinson, R. T., Liao, X., ... & Kramann, R. (2022). Spatial multi-omic map of human myocardial infarction. Nature, 608(7924), 766-777.

• Liu, Y., Sinjab, A., Min, J., Han, G., Paradiso, F., Zhang, Y., ... & Wang, L. (2025). Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics. Cancer cell, 43(5), 905-924.

• Schürch, C. M., Bhate, S. S., Barlow, G. L., Phillips, D. J., Noti, L., Zlobec, I., ... & Nolan, G. P. (2020). Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell, 182(5), 1341-1359.